From bfeb839cf775e463d62b7c6b12570ad97cc41a5c Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Tue, 28 Jul 2020 17:07:00 +0100 Subject: [PATCH 01/10] #1115 make param classes --- .../parameters/electrical_parameters.rst | 2 +- .../parameters/geometric_parameters.rst | 3 +- docs/source/parameters/index.rst | 6 +- .../parameters/lead_acid_parameters.rst | 4 + .../parameters/lithium_ion_parameters.rst | 4 + docs/source/parameters/parameter_values.rst | 4 +- .../standard_parameters_lead_acid.rst | 4 - .../standard_parameters_lithium_ion.rst | 4 - docs/source/parameters/thermal_parameters.rst | 4 +- docs/tutorials/add-model.rst | 5 +- examples/notebooks/compare-ecker-data.ipynb | 10 +- .../notebooks/models/pouch-cell-model.ipynb | 6 +- .../compare_comsol/compare_comsol_DFN.py | 2 +- pybamm/__init__.py | 12 +- pybamm/expression_tree/unary_operators.py | 26 +- pybamm/geometry/battery_geometry.py | 25 +- ...ead_exchange_current_density_Sulzer2019.py | 5 +- ...ide_exchange_current_density_Sulzer2019.py | 4 +- ...gen_exchange_current_density_Sulzer2019.py | 5 +- ...olyte_exchange_current_density_Chen2020.py | 4 +- ...lyte_exchange_current_density_Ecker2015.py | 4 +- .../anodes/graphite_Ecker2015/parameters.csv | 2 +- ...rolyte_exchange_current_density_Kim2011.py | 6 +- ...e_exchange_current_density_Ramadass2004.py | 4 +- ...lyte_exchange_current_density_PeymanMPM.py | 4 +- ...e_exchange_current_density_Dualfoil1998.py | 4 +- .../parameters.csv | 4 +- ...lyte_exchange_current_density_Ecker2015.py | 4 +- .../LiNiCoO2_Ecker2015/parameters.csv | 2 +- ...lyte_exchange_current_density_PeymanMPM.py | 4 +- ...e_exchange_current_density_Dualfoil1998.py | 4 +- .../cathodes/lico2_Marquis2019/parameters.csv | 4 +- ...e_exchange_current_density_Ramadass2004.py | 4 +- ...rolyte_exchange_current_density_Kim2011.py | 6 +- ...olyte_exchange_current_density_Chen2020.py | 4 +- pybamm/models/base_model.py | 17 +- .../full_battery_models/base_battery_model.py | 6 +- .../lead_acid/base_lead_acid_model.py | 2 +- .../lead_acid/basic_full.py | 30 +- .../lithium_ion/base_lithium_ion_model.py | 7 +- .../through_cell/explicit_convection.py | 2 +- .../through_cell/full_convection.py | 2 +- .../effective_resistance_current_collector.py | 54 +- .../external_circuit/experiment_events.py | 2 +- .../submodels/interface/base_interface.py | 4 +- .../submodels/particle/base_particle.py | 7 +- pybamm/parameters/electrical_parameters.py | 90 ++- pybamm/parameters/geometric_parameters.py | 150 ++-- pybamm/parameters/lead_acid_parameters.py | 728 +++++++++++++++++ pybamm/parameters/lithium_ion_parameters.py | 734 ++++++++++++++++++ .../standard_parameters_lead_acid.py | 579 -------------- .../standard_parameters_lithium_ion.py | 610 --------------- pybamm/parameters/thermal_parameters.py | 299 ++++--- pybamm/simulation.py | 2 +- test.py | 26 + .../test_models/standard_output_tests.py | 43 +- .../test_interface/test_butler_volmer.py | 10 +- .../test_interface/test_lead_acid.py | 8 +- .../test_interface/test_lithium_ion.py | 6 +- .../unit/test_expression_tree/test_symbol.py | 2 +- .../test_unary_operators.py | 8 +- .../test_geometry/test_battery_geometry.py | 27 +- .../test_convection/test_base_convection.py | 2 +- .../test_explicit_convection.py | 2 +- .../test_convection/test_full_convection.py | 2 +- .../test_convection/test_no_convection.py | 2 +- .../test_composite_potential_pair.py | 2 +- .../test_homogeneous_current_collector.py | 2 +- .../test_potential_pair.py | 2 +- .../test_ohm/test_composite_ohm.py | 2 +- .../test_electrode/test_ohm/test_full_ohm.py | 2 +- .../test_ohm/test_leading_ohm.py | 2 +- .../test_ohm/test_surface_form_ohm.py | 2 +- .../test_composite_conductivity.py | 4 +- .../test_full_conductivity.py | 2 +- .../test_leading_order_conductivity.py | 2 +- .../test_full_surface_form_conductivity.py | 2 +- .../test_leading_surface_form_conductivity.py | 2 +- .../test_constant_concentration.py | 2 +- .../test_full_diffusion.py | 2 +- .../test_leading_order_diffusion.py | 2 +- .../test_current_control.py | 2 +- .../test_function_control.py | 6 +- .../test_power_control.py | 2 +- .../test_interface/test_diffusion_limited.py | 2 +- .../test_inverse_butler_volmer.py | 2 +- .../test_kinetics/test_butler_volmer.py | 2 +- .../test_kinetics/test_tafel.py | 2 +- .../test_interface/test_lead_acid.py | 2 +- .../test_interface/test_lithium_ion.py | 2 +- .../test_full_oxygen_diffusion.py | 2 +- .../test_leading_oxygen_diffusion.py | 2 +- .../test_oxygen_diffusion/test_no_oxygen.py | 2 +- .../test_particle/test_fast_many_particles.py | 2 +- .../test_fast_single_particle.py | 2 +- .../test_fickian_many_particles.py | 2 +- .../test_fickian_single_particle.py | 2 +- .../test_porosity/test_constant_porosity.py | 2 +- .../test_full_reaction_driven_porosity.py | 2 +- .../test_leading_reaction_driven_porosity.py | 2 +- .../test_thermal/test_base_thermal.py | 2 +- .../test_thermal/test_isothermal.py | 2 +- .../test_thermal/test_lumped.py | 2 +- .../test_pouch_cell/test_pouch_1plus1D.py | 2 +- .../test_pouch_cell/test_pouch_2plus1D.py | 2 +- .../test_thermal/test_x_full.py | 2 +- .../test_bruggeman_tortuosity.py | 2 +- .../test_parameters/test_current_functions.py | 11 +- ...ensionless_parameter_values_lithium_ion.py | 8 +- .../test_electrical_parameters.py | 11 +- .../test_geometric_parameters.py | 15 +- .../test_standard_parameters_lead_acid.py | 59 +- tests/unit/test_quick_plot.py | 2 +- 113 files changed, 2118 insertions(+), 1738 deletions(-) create mode 100644 docs/source/parameters/lead_acid_parameters.rst create mode 100644 docs/source/parameters/lithium_ion_parameters.rst delete mode 100644 docs/source/parameters/standard_parameters_lead_acid.rst delete mode 100644 docs/source/parameters/standard_parameters_lithium_ion.rst create mode 100644 pybamm/parameters/lead_acid_parameters.py create mode 100644 pybamm/parameters/lithium_ion_parameters.py delete mode 100644 pybamm/parameters/standard_parameters_lead_acid.py delete mode 100644 pybamm/parameters/standard_parameters_lithium_ion.py create mode 100644 test.py diff --git a/docs/source/parameters/electrical_parameters.rst b/docs/source/parameters/electrical_parameters.rst index b04620e195..6cc609ea32 100644 --- a/docs/source/parameters/electrical_parameters.rst +++ b/docs/source/parameters/electrical_parameters.rst @@ -1,4 +1,4 @@ Electrical Parameters ===================== -.. automodule:: pybamm.parameters.electrical_parameters +.. autoclass:: pybamm.ElectricalParameters diff --git a/docs/source/parameters/geometric_parameters.rst b/docs/source/parameters/geometric_parameters.rst index cb0d174c1a..a7d0cfbd61 100644 --- a/docs/source/parameters/geometric_parameters.rst +++ b/docs/source/parameters/geometric_parameters.rst @@ -1,5 +1,4 @@ Geometric Parameters ==================== -.. automodule:: pybamm.parameters.geometric_parameters - +.. autoclass:: pybamm.GeometricParameters diff --git a/docs/source/parameters/index.rst b/docs/source/parameters/index.rst index 02f6e82025..9d7585717d 100644 --- a/docs/source/parameters/index.rst +++ b/docs/source/parameters/index.rst @@ -7,6 +7,6 @@ Parameters geometric_parameters electrical_parameters thermal_parameters - standard_parameters_lithium_ion - standard_parameters_lead_acid - parameter_sets \ No newline at end of file + lithium_ion_parameters + lead_acid_parameters + parameter_sets diff --git a/docs/source/parameters/lead_acid_parameters.rst b/docs/source/parameters/lead_acid_parameters.rst new file mode 100644 index 0000000000..c4d4bc0723 --- /dev/null +++ b/docs/source/parameters/lead_acid_parameters.rst @@ -0,0 +1,4 @@ +Lead-Acid Parameters +==================== + +.. autoclass:: pybamm.LeadAcidParameters diff --git a/docs/source/parameters/lithium_ion_parameters.rst b/docs/source/parameters/lithium_ion_parameters.rst new file mode 100644 index 0000000000..48928506c4 --- /dev/null +++ b/docs/source/parameters/lithium_ion_parameters.rst @@ -0,0 +1,4 @@ +Lithium-ion Parameters +====================== + +.. autoclass:: pybamm.LithiumIonParameters diff --git a/docs/source/parameters/parameter_values.rst b/docs/source/parameters/parameter_values.rst index 2ea2d6750d..adcbac4c31 100644 --- a/docs/source/parameters/parameter_values.rst +++ b/docs/source/parameters/parameter_values.rst @@ -1,5 +1,5 @@ -Base Parameter Values -===================== +Parameter Values +================ .. autoclass:: pybamm.ParameterValues :members: diff --git a/docs/source/parameters/standard_parameters_lead_acid.rst b/docs/source/parameters/standard_parameters_lead_acid.rst deleted file mode 100644 index cbfcd0b3af..0000000000 --- a/docs/source/parameters/standard_parameters_lead_acid.rst +++ /dev/null @@ -1,4 +0,0 @@ -Standard Lead-Acid Parameters -============================= - -.. automodule:: pybamm.parameters.standard_parameters_lead_acid diff --git a/docs/source/parameters/standard_parameters_lithium_ion.rst b/docs/source/parameters/standard_parameters_lithium_ion.rst deleted file mode 100644 index 19b2b37c32..0000000000 --- a/docs/source/parameters/standard_parameters_lithium_ion.rst +++ /dev/null @@ -1,4 +0,0 @@ -Standard Lithium-ion Parameters -=============================== - -.. automodule:: pybamm.parameters.standard_parameters_lithium_ion diff --git a/docs/source/parameters/thermal_parameters.rst b/docs/source/parameters/thermal_parameters.rst index d06ef55ed2..3d2ded012c 100644 --- a/docs/source/parameters/thermal_parameters.rst +++ b/docs/source/parameters/thermal_parameters.rst @@ -1,4 +1,4 @@ Thermal Parameters -===================== +================== -.. automodule:: pybamm.parameters.thermal_parameters +.. autoclass:: pybamm.ThermalParameters diff --git a/docs/tutorials/add-model.rst b/docs/tutorials/add-model.rst index ee941ed1a3..e55c3d0e9d 100644 --- a/docs/tutorials/add-model.rst +++ b/docs/tutorials/add-model.rst @@ -142,10 +142,11 @@ The inbuilt models in PyBaMM do not add all the model attributes within their ow In addition to calling submodels, common sets of variables and parameters found in lithium-ion and lead acid batteries are provided in `standard_variables.py`, -`standard_parameters_lithium_ion.py`, -`standard_parameters_lead_acid.py`, +`lithium_ion_parameters.py`, +`lead_acid_parameters.py`, `electrical_parameters.py`, `geometric_parameters.py`, +`thermal_parameters.py`, and `standard_spatial_vars.py` which we encourage use of to save redefining the same parameters and variables in every model and submodel. diff --git a/examples/notebooks/compare-ecker-data.ipynb b/examples/notebooks/compare-ecker-data.ipynb index 8754c0d9a4..f51c513a03 100644 --- a/examples/notebooks/compare-ecker-data.ipynb +++ b/examples/notebooks/compare-ecker-data.ipynb @@ -92,11 +92,11 @@ "source": [ "var = pybamm.standard_spatial_vars\n", "var_pts = {\n", - " var.x_n: int(parameter_values.evaluate(pybamm.geometric_parameters.L_n / 1e-6)),\n", - " var.x_s: int(parameter_values.evaluate(pybamm.geometric_parameters.L_s / 1e-6)),\n", - " var.x_p: int(parameter_values.evaluate(pybamm.geometric_parameters.L_p / 1e-6)),\n", - " var.r_n: int(parameter_values.evaluate(pybamm.geometric_parameters.R_n / 1e-7)),\n", - " var.r_p: int(parameter_values.evaluate(pybamm.geometric_parameters.R_p / 1e-7)),\n", + " var.x_n: int(parameter_values.evaluate(model.param.L_n / 1e-6)),\n", + " var.x_s: int(parameter_values.evaluate(model.param.L_s / 1e-6)),\n", + " var.x_p: int(parameter_values.evaluate(model.param.L_p / 1e-6)),\n", + " var.r_n: int(parameter_values.evaluate(model.param.R_n / 1e-7)),\n", + " var.r_p: int(parameter_values.evaluate(model.param.R_p / 1e-7)),\n", "}" ] }, diff --git a/examples/notebooks/models/pouch-cell-model.ipynb b/examples/notebooks/models/pouch-cell-model.ipynb index 2df81d2ae3..245d084794 100644 --- a/examples/notebooks/models/pouch-cell-model.ipynb +++ b/examples/notebooks/models/pouch-cell-model.ipynb @@ -251,12 +251,12 @@ "outputs": [], "source": [ "# set up times\n", - "tau = param.evaluate(pybamm.standard_parameters_lithium_ion.tau_discharge)\n", + "tau = param.evaluate(dfn.param.tau_discharge)\n", "comsol_t = comsol_variables[\"time\"]\n", "pybamm_t = comsol_t / tau\n", "# set up space\n", "mesh = simulations[\"1+1D DFN\"].mesh\n", - "L_z = param.evaluate(pybamm.standard_parameters_lithium_ion.L_z)\n", + "L_z = param.evaluate(dfn.param.L_z)\n", "pybamm_z = mesh[\"current collector\"].nodes\n", "z_interp = pybamm_z * L_z\n", "\n", @@ -742,7 +742,7 @@ } ], "source": [ - "T_ref = param.evaluate(pybamm.standard_parameters_lithium_ion.T_ref)\n", + "T_ref = param.evaluate(dfn.param.T_ref)\n", "var = \"X-averaged cell temperature [K]\"\n", "comsol_var = comsol_solution[var]\n", "\n", diff --git a/examples/scripts/compare_comsol/compare_comsol_DFN.py b/examples/scripts/compare_comsol/compare_comsol_DFN.py index a9cafb0f1b..5efe21d260 100644 --- a/examples/scripts/compare_comsol/compare_comsol_DFN.py +++ b/examples/scripts/compare_comsol/compare_comsol_DFN.py @@ -55,7 +55,7 @@ # Make Comsol 'model' for comparison whole_cell = ["negative electrode", "separator", "positive electrode"] comsol_t = comsol_variables["time"] -L_x = param.evaluate(pybamm.standard_parameters_lithium_ion.L_x) +L_x = param.evaluate(pybamm_model.param.L_x) def get_interp_fun(variable_name, domain): diff --git a/pybamm/__init__.py b/pybamm/__init__.py index f95fc07e90..80f46de6db 100644 --- a/pybamm/__init__.py +++ b/pybamm/__init__.py @@ -108,6 +108,7 @@ def version(formatted=False): to_python, EvaluatorPython, ) + if system() != "Windows": from .expression_tree.operations.evaluate import EvaluatorJax @@ -159,14 +160,15 @@ def version(formatted=False): from .geometry import standard_spatial_vars # -# Parameters class and methods +# Parameter classes and methods # from .parameters.parameter_values import ParameterValues from .parameters import constants -from .parameters import geometric_parameters -from .parameters import electrical_parameters -from .parameters import thermal_parameters -from .parameters import standard_parameters_lithium_ion, standard_parameters_lead_acid +from .parameters.geometric_parameters import GeometricParameters +from .parameters.electrical_parameters import ElectricalParameters +from .parameters.thermal_parameters import ThermalParameters +from .parameters.lithium_ion_parameters import LithiumIonParameters +from .parameters.lead_acid_parameters import LeadAcidParameters from .parameters import parameter_sets diff --git a/pybamm/expression_tree/unary_operators.py b/pybamm/expression_tree/unary_operators.py index 7c7e0e6a9a..adbb13766f 100644 --- a/pybamm/expression_tree/unary_operators.py +++ b/pybamm/expression_tree/unary_operators.py @@ -1043,30 +1043,32 @@ def x_average(symbol): if a.id == b.id == c.id: return a else: - l_n = pybamm.geometric_parameters.l_n - l_s = pybamm.geometric_parameters.l_s - l_p = pybamm.geometric_parameters.l_p + geo = pybamm.GeometricParameters() + l_n = geo.l_n + l_s = geo.l_s + l_p = geo.l_p return (l_n * a + l_s * b + l_p * c) / (l_n + l_s + l_p) # Otherwise, use Integral to calculate average value else: + geo = pybamm.GeometricParameters() if symbol.domain == ["negative electrode"]: x = pybamm.standard_spatial_vars.x_n - l = pybamm.geometric_parameters.l_n + l = geo.l_n elif symbol.domain == ["separator"]: x = pybamm.standard_spatial_vars.x_s - l = pybamm.geometric_parameters.l_s + l = geo.l_s elif symbol.domain == ["positive electrode"]: x = pybamm.standard_spatial_vars.x_p - l = pybamm.geometric_parameters.l_p + l = geo.l_p elif symbol.domain == ["negative electrode", "separator", "positive electrode"]: x = pybamm.standard_spatial_vars.x l = pybamm.Scalar(1) elif symbol.domain == ["negative particle"]: x = pybamm.standard_spatial_vars.x_n - l = pybamm.geometric_parameters.l_n + l = geo.l_n elif symbol.domain == ["positive particle"]: x = pybamm.standard_spatial_vars.x_p - l = pybamm.geometric_parameters.l_p + l = geo.l_p else: x = pybamm.SpatialVariable("x", domain=symbol.domain) v = pybamm.ones_like(symbol) @@ -1108,8 +1110,9 @@ def z_average(symbol): return symbol.orphans[0] # Otherwise, use Integral to calculate average value else: + geo = pybamm.GeometricParameters() z = pybamm.standard_spatial_vars.z - l_z = pybamm.geometric_parameters.l_z + l_z = geo.l_z return Integral(symbol, z) / l_z @@ -1144,10 +1147,11 @@ def yz_average(symbol): return symbol.orphans[0] # Otherwise, use Integral to calculate average value else: + geo = pybamm.GeometricParameters() y = pybamm.standard_spatial_vars.y z = pybamm.standard_spatial_vars.z - l_y = pybamm.geometric_parameters.l_y - l_z = pybamm.geometric_parameters.l_z + l_y = geo.l_y + l_z = geo.l_z return Integral(symbol, [y, z]) / (l_y * l_z) diff --git a/pybamm/geometry/battery_geometry.py b/pybamm/geometry/battery_geometry.py index c2b0f99f78..d3e0b68901 100644 --- a/pybamm/geometry/battery_geometry.py +++ b/pybamm/geometry/battery_geometry.py @@ -22,8 +22,9 @@ def battery_geometry(include_particles=True, current_collector_dimension=0): """ var = pybamm.standard_spatial_vars - l_n = pybamm.geometric_parameters.l_n - l_s = pybamm.geometric_parameters.l_s + geo = pybamm.GeometricParameters() + l_n = geo.l_n + l_s = geo.l_s geometry = { "negative electrode": {var.x_n: {"min": 0, "max": l_n}}, @@ -45,24 +46,24 @@ def battery_geometry(include_particles=True, current_collector_dimension=0): geometry["current collector"] = { var.z: {"min": 0, "max": 1}, "tabs": { - "negative": {"z_centre": pybamm.geometric_parameters.centre_z_tab_n}, - "positive": {"z_centre": pybamm.geometric_parameters.centre_z_tab_p}, + "negative": {"z_centre": geo.centre_z_tab_n}, + "positive": {"z_centre": geo.centre_z_tab_p}, }, } elif current_collector_dimension == 2: geometry["current collector"] = { - var.y: {"min": 0, "max": pybamm.geometric_parameters.l_y}, - var.z: {"min": 0, "max": pybamm.geometric_parameters.l_z}, + var.y: {"min": 0, "max": geo.l_y}, + var.z: {"min": 0, "max": geo.l_z}, "tabs": { "negative": { - "y_centre": pybamm.geometric_parameters.centre_y_tab_n, - "z_centre": pybamm.geometric_parameters.centre_z_tab_n, - "width": pybamm.geometric_parameters.l_tab_n, + "y_centre": geo.centre_y_tab_n, + "z_centre": geo.centre_z_tab_n, + "width": geo.l_tab_n, }, "positive": { - "y_centre": pybamm.geometric_parameters.centre_y_tab_p, - "z_centre": pybamm.geometric_parameters.centre_z_tab_p, - "width": pybamm.geometric_parameters.l_tab_p, + "y_centre": geo.centre_y_tab_p, + "z_centre": geo.centre_z_tab_p, + "width": geo.l_tab_p, }, }, } diff --git a/pybamm/input/parameters/lead-acid/anodes/lead_Sulzer2019/lead_exchange_current_density_Sulzer2019.py b/pybamm/input/parameters/lead-acid/anodes/lead_Sulzer2019/lead_exchange_current_density_Sulzer2019.py index b1573249fa..601e362031 100644 --- a/pybamm/input/parameters/lead-acid/anodes/lead_Sulzer2019/lead_exchange_current_density_Sulzer2019.py +++ b/pybamm/input/parameters/lead-acid/anodes/lead_Sulzer2019/lead_exchange_current_density_Sulzer2019.py @@ -1,4 +1,4 @@ -from pybamm import standard_parameters_lead_acid +from pybamm import Parameter def lead_exchange_current_density_Sulzer2019(c_e, T): @@ -27,6 +27,7 @@ def lead_exchange_current_density_Sulzer2019(c_e, T): """ j0_ref = 0.06 # srinivasan2003mathematical - j0 = j0_ref * (c_e / standard_parameters_lead_acid.c_e_typ) + c_e_typ = Parameter("Typical electrolyte concentration [mol.m-3]") + j0 = j0_ref * (c_e / c_e_typ) return j0 diff --git a/pybamm/input/parameters/lead-acid/cathodes/lead_dioxide_Sulzer2019/lead_dioxide_exchange_current_density_Sulzer2019.py b/pybamm/input/parameters/lead-acid/cathodes/lead_dioxide_Sulzer2019/lead_dioxide_exchange_current_density_Sulzer2019.py index 0122e99530..a87f16eaff 100644 --- a/pybamm/input/parameters/lead-acid/cathodes/lead_dioxide_Sulzer2019/lead_dioxide_exchange_current_density_Sulzer2019.py +++ b/pybamm/input/parameters/lead-acid/cathodes/lead_dioxide_Sulzer2019/lead_dioxide_exchange_current_density_Sulzer2019.py @@ -1,4 +1,4 @@ -from pybamm import standard_parameters_lead_acid +from pybamm import LeadAcidParameters def lead_dioxide_exchange_current_density_Sulzer2019(c_e, T): @@ -28,7 +28,7 @@ def lead_dioxide_exchange_current_density_Sulzer2019(c_e, T): """ c_ox = 0 c_hy = 0 - param = standard_parameters_lead_acid + param = LeadAcidParameters() c_w_dim = (1 - c_e * param.V_e - c_ox * param.V_ox - c_hy * param.V_hy) / param.V_w c_w_ref = (1 - param.c_e_typ * param.V_e) / param.V_w c_w = c_w_dim / c_w_ref diff --git a/pybamm/input/parameters/lead-acid/cathodes/lead_dioxide_Sulzer2019/oxygen_exchange_current_density_Sulzer2019.py b/pybamm/input/parameters/lead-acid/cathodes/lead_dioxide_Sulzer2019/oxygen_exchange_current_density_Sulzer2019.py index 12aee8b923..5152126110 100644 --- a/pybamm/input/parameters/lead-acid/cathodes/lead_dioxide_Sulzer2019/oxygen_exchange_current_density_Sulzer2019.py +++ b/pybamm/input/parameters/lead-acid/cathodes/lead_dioxide_Sulzer2019/oxygen_exchange_current_density_Sulzer2019.py @@ -1,4 +1,4 @@ -from pybamm import standard_parameters_lead_acid +from pybamm import Parameter def oxygen_exchange_current_density_Sulzer2019(c_e, T): @@ -27,6 +27,7 @@ def oxygen_exchange_current_density_Sulzer2019(c_e, T): """ j0_ref = 2.5e-23 # srinivasan2003mathematical - j0 = j0_ref * (c_e / standard_parameters_lead_acid.c_e_typ) + c_e_typ = Parameter("Typical electrolyte concentration [mol.m-3]") + j0 = j0_ref * (c_e / c_e_typ) return j0 diff --git a/pybamm/input/parameters/lithium-ion/anodes/graphite_Chen2020/graphite_LGM50_electrolyte_exchange_current_density_Chen2020.py b/pybamm/input/parameters/lithium-ion/anodes/graphite_Chen2020/graphite_LGM50_electrolyte_exchange_current_density_Chen2020.py index a8ca90060f..9abea7e1bd 100644 --- a/pybamm/input/parameters/lithium-ion/anodes/graphite_Chen2020/graphite_LGM50_electrolyte_exchange_current_density_Chen2020.py +++ b/pybamm/input/parameters/lithium-ion/anodes/graphite_Chen2020/graphite_LGM50_electrolyte_exchange_current_density_Chen2020.py @@ -1,4 +1,4 @@ -from pybamm import exp, constants, standard_parameters_lithium_ion +from pybamm import exp, constants, Parameter def graphite_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, T): @@ -32,7 +32,7 @@ def graphite_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, E_r = 35000 arrhenius = exp(E_r / constants.R * (1 / 298.15 - 1 / T)) - c_n_max = standard_parameters_lithium_ion.c_n_max + c_n_max = Parameter("Maximum concentration in negative electrode [mol.m-3]") return ( m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_n_max - c_s_surf) ** 0.5 diff --git a/pybamm/input/parameters/lithium-ion/anodes/graphite_Ecker2015/graphite_electrolyte_exchange_current_density_Ecker2015.py b/pybamm/input/parameters/lithium-ion/anodes/graphite_Ecker2015/graphite_electrolyte_exchange_current_density_Ecker2015.py index 4ad18f8dd2..140f1cbe70 100644 --- a/pybamm/input/parameters/lithium-ion/anodes/graphite_Ecker2015/graphite_electrolyte_exchange_current_density_Ecker2015.py +++ b/pybamm/input/parameters/lithium-ion/anodes/graphite_Ecker2015/graphite_electrolyte_exchange_current_density_Ecker2015.py @@ -1,4 +1,4 @@ -from pybamm import exp, constants, standard_parameters_lithium_ion +from pybamm import exp, constants, Parameter def graphite_electrolyte_exchange_current_density_Ecker2015(c_e, c_s_surf, T): @@ -41,7 +41,7 @@ def graphite_electrolyte_exchange_current_density_Ecker2015(c_e, c_s_surf, T): arrhenius = exp(-E_r / (constants.R * T)) * exp(E_r / (constants.R * 296.15)) - c_n_max = standard_parameters_lithium_ion.c_n_max + c_n_max = Parameter("Maximum concentration in negative electrode [mol.m-3]") return ( m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_n_max - c_s_surf) ** 0.5 diff --git a/pybamm/input/parameters/lithium-ion/anodes/graphite_Ecker2015/parameters.csv b/pybamm/input/parameters/lithium-ion/anodes/graphite_Ecker2015/parameters.csv index bf72401486..c2f2d453c9 100644 --- a/pybamm/input/parameters/lithium-ion/anodes/graphite_Ecker2015/parameters.csv +++ b/pybamm/input/parameters/lithium-ion/anodes/graphite_Ecker2015/parameters.csv @@ -11,7 +11,7 @@ Negative electrode OCP [V],[function]graphite_ocp_Ecker2015_function,, ,,, # Microstructure,,, Negative electrode porosity,0.329,, -Negative electrode active material volume fraction, 0.555,, +Negative electrode active material volume fraction, 0.372,, Negative particle radius [m],1.37E-05,, Negative particle distribution in x,1,, Negative electrode surface area to volume ratio [m-1], 81548,, diff --git a/pybamm/input/parameters/lithium-ion/anodes/graphite_Kim2011/graphite_electrolyte_exchange_current_density_Kim2011.py b/pybamm/input/parameters/lithium-ion/anodes/graphite_Kim2011/graphite_electrolyte_exchange_current_density_Kim2011.py index 192b479be4..c98aac2d18 100644 --- a/pybamm/input/parameters/lithium-ion/anodes/graphite_Kim2011/graphite_electrolyte_exchange_current_density_Kim2011.py +++ b/pybamm/input/parameters/lithium-ion/anodes/graphite_Kim2011/graphite_electrolyte_exchange_current_density_Kim2011.py @@ -1,4 +1,4 @@ -from pybamm import exp, constants, standard_parameters_lithium_ion +from pybamm import exp, constants, Parameter def graphite_electrolyte_exchange_current_density_Kim2011(c_e, c_s_surf, T): @@ -31,9 +31,9 @@ def graphite_electrolyte_exchange_current_density_Kim2011(c_e, c_s_surf, T): i0_ref = 36 # reference exchange current density at 100% SOC sto = 0.36 # stochiometry at 100% SOC - c_s_n_max = standard_parameters_lithium_ion.c_n_max # max electrode concentration + c_s_n_max = Parameter("Maximum concentration in negative electrode [mol.m-3]") c_s_n_ref = sto * c_s_n_max # reference electrode concentration - c_e_ref = standard_parameters_lithium_ion.c_e_typ # ref electrolyte concentration + c_e_ref = Parameter("Typical electrolyte concentration [mol.m-3]") alpha = 0.5 # charge transfer coefficient m_ref = i0_ref / ( diff --git a/pybamm/input/parameters/lithium-ion/anodes/graphite_Ramadass2004/graphite_electrolyte_exchange_current_density_Ramadass2004.py b/pybamm/input/parameters/lithium-ion/anodes/graphite_Ramadass2004/graphite_electrolyte_exchange_current_density_Ramadass2004.py index 8afd798fdc..063bff2587 100644 --- a/pybamm/input/parameters/lithium-ion/anodes/graphite_Ramadass2004/graphite_electrolyte_exchange_current_density_Ramadass2004.py +++ b/pybamm/input/parameters/lithium-ion/anodes/graphite_Ramadass2004/graphite_electrolyte_exchange_current_density_Ramadass2004.py @@ -1,4 +1,4 @@ -from pybamm import exp, constants, standard_parameters_lithium_ion +from pybamm import exp, constants, Parameter def graphite_electrolyte_exchange_current_density_Ramadass2004(c_e, c_s_surf, T): @@ -30,7 +30,7 @@ def graphite_electrolyte_exchange_current_density_Ramadass2004(c_e, c_s_surf, T) E_r = 37480 arrhenius = exp(E_r / constants.R * (1 / 298.15 - 1 / T)) - c_n_max = standard_parameters_lithium_ion.c_n_max + c_n_max = Parameter("Maximum concentration in negative electrode [mol.m-3]") return ( m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_n_max - c_s_surf) ** 0.5 diff --git a/pybamm/input/parameters/lithium-ion/anodes/graphite_UMBL_Mohtat2020/graphite_electrolyte_exchange_current_density_PeymanMPM.py b/pybamm/input/parameters/lithium-ion/anodes/graphite_UMBL_Mohtat2020/graphite_electrolyte_exchange_current_density_PeymanMPM.py index 12ee3ae4be..ef13eb3862 100644 --- a/pybamm/input/parameters/lithium-ion/anodes/graphite_UMBL_Mohtat2020/graphite_electrolyte_exchange_current_density_PeymanMPM.py +++ b/pybamm/input/parameters/lithium-ion/anodes/graphite_UMBL_Mohtat2020/graphite_electrolyte_exchange_current_density_PeymanMPM.py @@ -1,4 +1,4 @@ -from pybamm import exp, constants, standard_parameters_lithium_ion +from pybamm import exp, constants, Parameter def graphite_electrolyte_exchange_current_density_PeymanMPM(c_e, c_s_surf, T): @@ -30,7 +30,7 @@ def graphite_electrolyte_exchange_current_density_PeymanMPM(c_e, c_s_surf, T): E_r = 37480 arrhenius = exp(E_r / constants.R * (1 / 298.15 - 1 / T)) - c_n_max = standard_parameters_lithium_ion.c_n_max + c_n_max = Parameter("Maximum concentration in negative electrode [mol.m-3]") return ( m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_n_max - c_s_surf) ** 0.5 diff --git a/pybamm/input/parameters/lithium-ion/anodes/graphite_mcmb2528_Marquis2019/graphite_electrolyte_exchange_current_density_Dualfoil1998.py b/pybamm/input/parameters/lithium-ion/anodes/graphite_mcmb2528_Marquis2019/graphite_electrolyte_exchange_current_density_Dualfoil1998.py index ab4e56a7d8..d618bd464b 100644 --- a/pybamm/input/parameters/lithium-ion/anodes/graphite_mcmb2528_Marquis2019/graphite_electrolyte_exchange_current_density_Dualfoil1998.py +++ b/pybamm/input/parameters/lithium-ion/anodes/graphite_mcmb2528_Marquis2019/graphite_electrolyte_exchange_current_density_Dualfoil1998.py @@ -1,4 +1,4 @@ -from pybamm import exp, constants, standard_parameters_lithium_ion +from pybamm import exp, constants, Parameter def graphite_electrolyte_exchange_current_density_Dualfoil1998(c_e, c_s_surf, T): @@ -28,7 +28,7 @@ def graphite_electrolyte_exchange_current_density_Dualfoil1998(c_e, c_s_surf, T) E_r = 37480 arrhenius = exp(E_r / constants.R * (1 / 298.15 - 1 / T)) - c_n_max = standard_parameters_lithium_ion.c_n_max + c_n_max = Parameter("Maximum concentration in negative electrode [mol.m-3]") return ( m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_n_max - c_s_surf) ** 0.5 diff --git a/pybamm/input/parameters/lithium-ion/anodes/graphite_mcmb2528_Marquis2019/parameters.csv b/pybamm/input/parameters/lithium-ion/anodes/graphite_mcmb2528_Marquis2019/parameters.csv index 920c51c12a..49cb9e5c62 100644 --- a/pybamm/input/parameters/lithium-ion/anodes/graphite_mcmb2528_Marquis2019/parameters.csv +++ b/pybamm/input/parameters/lithium-ion/anodes/graphite_mcmb2528_Marquis2019/parameters.csv @@ -1,6 +1,6 @@ Name [units],Value,Reference,Notes # Empty rows and rows starting with ‘#’ will be ignored,,, -,,, + # Electrode properties,,, Negative electrode conductivity [S.m-1],100,Scott Moura FastDFN,graphite Maximum concentration in negative electrode [mol.m-3],24983.2619938437,Scott Moura FastDFN, @@ -9,7 +9,7 @@ Negative electrode OCP [V],[function]graphite_mcmb2528_ocp_Dualfoil1998, ,,, # Microstructure,,, Negative electrode porosity,0.3,Scott Moura FastDFN,electrolyte volume fraction -Negative electrode active material volume fraction,0.7,,assuming zero binder volume fraction +Negative electrode active material volume fraction,0.6,, Negative particle radius [m],1E-05,Scott Moura FastDFN, Negative particle distribution in x,1,, Negative electrode surface area to volume ratio [m-1],180000,Scott Moura FastDFN, diff --git a/pybamm/input/parameters/lithium-ion/cathodes/LiNiCoO2_Ecker2015/nco_electrolyte_exchange_current_density_Ecker2015.py b/pybamm/input/parameters/lithium-ion/cathodes/LiNiCoO2_Ecker2015/nco_electrolyte_exchange_current_density_Ecker2015.py index f8ee94dcc8..e804f00ceb 100644 --- a/pybamm/input/parameters/lithium-ion/cathodes/LiNiCoO2_Ecker2015/nco_electrolyte_exchange_current_density_Ecker2015.py +++ b/pybamm/input/parameters/lithium-ion/cathodes/LiNiCoO2_Ecker2015/nco_electrolyte_exchange_current_density_Ecker2015.py @@ -1,4 +1,4 @@ -from pybamm import exp, constants, standard_parameters_lithium_ion +from pybamm import exp, constants, Parameter def nco_electrolyte_exchange_current_density_Ecker2015(c_e, c_s_surf, T): @@ -41,7 +41,7 @@ def nco_electrolyte_exchange_current_density_Ecker2015(c_e, c_s_surf, T): E_r = 4.36e4 arrhenius = exp(-E_r / (constants.R * T)) * exp(E_r / (constants.R * 296.15)) - c_p_max = standard_parameters_lithium_ion.c_p_max + c_p_max = Parameter("Maximum concentration in positive electrode [mol.m-3]") return ( m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_p_max - c_s_surf) ** 0.5 diff --git a/pybamm/input/parameters/lithium-ion/cathodes/LiNiCoO2_Ecker2015/parameters.csv b/pybamm/input/parameters/lithium-ion/cathodes/LiNiCoO2_Ecker2015/parameters.csv index b84e1dd462..db5333e6b2 100644 --- a/pybamm/input/parameters/lithium-ion/cathodes/LiNiCoO2_Ecker2015/parameters.csv +++ b/pybamm/input/parameters/lithium-ion/cathodes/LiNiCoO2_Ecker2015/parameters.csv @@ -11,7 +11,7 @@ Positive electrode OCP [V],[function]nco_ocp_Ecker2015_function,, ,,, # Microstructure,,, Positive electrode porosity,0.296,, -Positive electrode active material volume fraction, 0.58,, +Positive electrode active material volume fraction, 0.408,, Positive particle radius [m],6.5E-06,, Positive particle distribution in x,1,, Positive electrode surface area to volume ratio [m-1],188455,, diff --git a/pybamm/input/parameters/lithium-ion/cathodes/NMC_UMBL_Mohtat2020/NMC_electrolyte_exchange_current_density_PeymanMPM.py b/pybamm/input/parameters/lithium-ion/cathodes/NMC_UMBL_Mohtat2020/NMC_electrolyte_exchange_current_density_PeymanMPM.py index 9d85f6caf3..4359ccd0e6 100644 --- a/pybamm/input/parameters/lithium-ion/cathodes/NMC_UMBL_Mohtat2020/NMC_electrolyte_exchange_current_density_PeymanMPM.py +++ b/pybamm/input/parameters/lithium-ion/cathodes/NMC_UMBL_Mohtat2020/NMC_electrolyte_exchange_current_density_PeymanMPM.py @@ -1,4 +1,4 @@ -from pybamm import exp, constants, standard_parameters_lithium_ion +from pybamm import exp, constants, Parameter def NMC_electrolyte_exchange_current_density_PeymanMPM(c_e, c_s_surf, T): @@ -28,7 +28,7 @@ def NMC_electrolyte_exchange_current_density_PeymanMPM(c_e, c_s_surf, T): E_r = 39570 arrhenius = exp(E_r / constants.R * (1 / 298.15 - 1 / T)) - c_p_max = standard_parameters_lithium_ion.c_p_max + c_p_max = Parameter("Maximum concentration in positive electrode [mol.m-3]") return ( m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_p_max - c_s_surf) ** 0.5 diff --git a/pybamm/input/parameters/lithium-ion/cathodes/lico2_Marquis2019/lico2_electrolyte_exchange_current_density_Dualfoil1998.py b/pybamm/input/parameters/lithium-ion/cathodes/lico2_Marquis2019/lico2_electrolyte_exchange_current_density_Dualfoil1998.py index ee7470eacf..b7955322f5 100644 --- a/pybamm/input/parameters/lithium-ion/cathodes/lico2_Marquis2019/lico2_electrolyte_exchange_current_density_Dualfoil1998.py +++ b/pybamm/input/parameters/lithium-ion/cathodes/lico2_Marquis2019/lico2_electrolyte_exchange_current_density_Dualfoil1998.py @@ -1,4 +1,4 @@ -from pybamm import exp, constants, standard_parameters_lithium_ion +from pybamm import exp, constants, Parameter def lico2_electrolyte_exchange_current_density_Dualfoil1998(c_e, c_s_surf, T): @@ -28,7 +28,7 @@ def lico2_electrolyte_exchange_current_density_Dualfoil1998(c_e, c_s_surf, T): E_r = 39570 arrhenius = exp(E_r / constants.R * (1 / 298.15 - 1 / T)) - c_p_max = standard_parameters_lithium_ion.c_p_max + c_p_max = Parameter("Maximum concentration in positive electrode [mol.m-3]") return ( m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_p_max - c_s_surf) ** 0.5 diff --git a/pybamm/input/parameters/lithium-ion/cathodes/lico2_Marquis2019/parameters.csv b/pybamm/input/parameters/lithium-ion/cathodes/lico2_Marquis2019/parameters.csv index bfac949bd7..c20d490dea 100644 --- a/pybamm/input/parameters/lithium-ion/cathodes/lico2_Marquis2019/parameters.csv +++ b/pybamm/input/parameters/lithium-ion/cathodes/lico2_Marquis2019/parameters.csv @@ -9,7 +9,7 @@ Positive electrode OCP [V],[function]lico2_ocp_Dualfoil1998,, ,,, # Microstructure,,, Positive electrode porosity,0.3,Scott Moura FastDFN,electrolyte volume fraction -Positive electrode active material volume fraction,0.7,,assuming zero binder volume fraction +Positive electrode active material volume fraction,0.5,, Positive particle radius [m],1E-05,Scott Moura FastDFN, Positive particle distribution in x,1,, Positive electrode surface area to volume ratio [m-1],150000,Scott Moura FastDFN, @@ -30,4 +30,4 @@ Positive electrode density [kg.m-3],3262,, # Thermal parameters,,, Positive electrode specific heat capacity [J.kg-1.K-1],700,, Positive electrode thermal conductivity [W.m-1.K-1],2.1,, -Positive electrode OCP entropic change [V.K-1],[function]lico2_entropic_change_Moura2016,, \ No newline at end of file +Positive electrode OCP entropic change [V.K-1],[function]lico2_entropic_change_Moura2016,, diff --git a/pybamm/input/parameters/lithium-ion/cathodes/lico2_Ramadass2004/lico2_electrolyte_exchange_current_density_Ramadass2004.py b/pybamm/input/parameters/lithium-ion/cathodes/lico2_Ramadass2004/lico2_electrolyte_exchange_current_density_Ramadass2004.py index d6a4fb50fa..bdd5256284 100644 --- a/pybamm/input/parameters/lithium-ion/cathodes/lico2_Ramadass2004/lico2_electrolyte_exchange_current_density_Ramadass2004.py +++ b/pybamm/input/parameters/lithium-ion/cathodes/lico2_Ramadass2004/lico2_electrolyte_exchange_current_density_Ramadass2004.py @@ -1,4 +1,4 @@ -from pybamm import exp, constants, standard_parameters_lithium_ion +from pybamm import exp, constants, Parameter def lico2_electrolyte_exchange_current_density_Ramadass2004(c_e, c_s_surf, T): @@ -30,7 +30,7 @@ def lico2_electrolyte_exchange_current_density_Ramadass2004(c_e, c_s_surf, T): E_r = 39570 arrhenius = exp(E_r / constants.R * (1 / 298.15 - 1 / T)) - c_p_max = standard_parameters_lithium_ion.c_p_max + c_p_max = Parameter("Maximum concentration in positive electrode [mol.m-3]") return ( m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_p_max - c_s_surf) ** 0.5 diff --git a/pybamm/input/parameters/lithium-ion/cathodes/nca_Kim2011/nca_electrolyte_exchange_current_density_Kim2011.py b/pybamm/input/parameters/lithium-ion/cathodes/nca_Kim2011/nca_electrolyte_exchange_current_density_Kim2011.py index 0bec50fa04..52938cf215 100644 --- a/pybamm/input/parameters/lithium-ion/cathodes/nca_Kim2011/nca_electrolyte_exchange_current_density_Kim2011.py +++ b/pybamm/input/parameters/lithium-ion/cathodes/nca_Kim2011/nca_electrolyte_exchange_current_density_Kim2011.py @@ -1,4 +1,4 @@ -from pybamm import exp, constants, standard_parameters_lithium_ion +from pybamm import exp, constants, Parameter def nca_electrolyte_exchange_current_density_Kim2011(c_e, c_s_surf, T): @@ -29,9 +29,9 @@ def nca_electrolyte_exchange_current_density_Kim2011(c_e, c_s_surf, T): """ i0_ref = 4 # reference exchange current density at 100% SOC sto = 0.41 # stochiometry at 100% SOC - c_s_max = standard_parameters_lithium_ion.c_p_max # max electrode concentration + c_s_max = Parameter("Maximum concentration in positive electrode [mol.m-3]") c_s_ref = sto * c_s_max # reference electrode concentration - c_e_ref = standard_parameters_lithium_ion.c_e_typ # ref electrolyte concentration + c_e_ref = Parameter("Typical electrolyte concentration [mol.m-3]") alpha = 0.5 # charge transfer coefficient m_ref = i0_ref / ( diff --git a/pybamm/input/parameters/lithium-ion/cathodes/nmc_Chen2020/nmc_LGM50_electrolyte_exchange_current_density_Chen2020.py b/pybamm/input/parameters/lithium-ion/cathodes/nmc_Chen2020/nmc_LGM50_electrolyte_exchange_current_density_Chen2020.py index c2ca46c249..0925f69260 100644 --- a/pybamm/input/parameters/lithium-ion/cathodes/nmc_Chen2020/nmc_LGM50_electrolyte_exchange_current_density_Chen2020.py +++ b/pybamm/input/parameters/lithium-ion/cathodes/nmc_Chen2020/nmc_LGM50_electrolyte_exchange_current_density_Chen2020.py @@ -1,4 +1,4 @@ -from pybamm import exp, constants, standard_parameters_lithium_ion +from pybamm import exp, constants, Parameter def nmc_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, T): @@ -31,7 +31,7 @@ def nmc_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, T): E_r = 17800 arrhenius = exp(E_r / constants.R * (1 / 298.15 - 1 / T)) - c_p_max = standard_parameters_lithium_ion.c_p_max + c_p_max = Parameter("Maximum concentration in positive electrode [mol.m-3]") return ( m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_p_max - c_s_surf) ** 0.5 diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py index d9d636073c..8e9184b77c 100644 --- a/pybamm/models/base_model.py +++ b/pybamm/models/base_model.py @@ -7,16 +7,6 @@ import warnings -class ParamClass: - """Class for converting a module of parameters into a class. For pickling.""" - - def __init__(self, methods): - for k, v in methods.__dict__.items(): - # don't save module attributes (e.g. pybamm, numpy) - if not (k.startswith("__") or inspect.ismodule(v)): - self.__dict__[k] = v - - class BaseModel(object): """Base model class for other models to extend. @@ -261,12 +251,7 @@ def param(self): @param.setter def param(self, values): - if values is None: - self._param = None - else: - # convert module into a class - # (StackOverflow: https://tinyurl.com/yk3euon3) - self._param = ParamClass(values) + self._param = values @property def options(self): diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py index 5460417252..03d7de92c9 100644 --- a/pybamm/models/full_battery_models/base_battery_model.py +++ b/pybamm/models/full_battery_models/base_battery_model.py @@ -360,9 +360,9 @@ def set_standard_output_variables(self): # Spatial var = pybamm.standard_spatial_vars - L_x = pybamm.geometric_parameters.L_x - L_y = pybamm.geometric_parameters.L_y - L_z = pybamm.geometric_parameters.L_z + L_x = self.param.L_x + L_y = self.param.L_y + L_z = self.param.L_z self.variables.update( { "x": var.x, diff --git a/pybamm/models/full_battery_models/lead_acid/base_lead_acid_model.py b/pybamm/models/full_battery_models/lead_acid/base_lead_acid_model.py index 463a2c8b67..e5eb3f1a15 100644 --- a/pybamm/models/full_battery_models/lead_acid/base_lead_acid_model.py +++ b/pybamm/models/full_battery_models/lead_acid/base_lead_acid_model.py @@ -17,7 +17,7 @@ class BaseModel(pybamm.BaseBatteryModel): def __init__(self, options=None, name="Unnamed lead-acid model"): super().__init__(options, name) - self.param = pybamm.standard_parameters_lead_acid + self.param = pybamm.LeadAcidParameters() # Default timescale is discharge timescale self.timescale = self.param.tau_discharge diff --git a/pybamm/models/full_battery_models/lead_acid/basic_full.py b/pybamm/models/full_battery_models/lead_acid/basic_full.py index a81f5717a4..74b7829ad2 100644 --- a/pybamm/models/full_battery_models/lead_acid/basic_full.py +++ b/pybamm/models/full_battery_models/lead_acid/basic_full.py @@ -42,13 +42,13 @@ def __init__(self, name="Basic full model"): Q = pybamm.Variable("Discharge capacity [A.h]") # Variables that vary spatially are created with a domain c_e_n = pybamm.Variable( - "Negative electrolyte concentration", domain="negative electrode", + "Negative electrolyte concentration", domain="negative electrode" ) c_e_s = pybamm.Variable( - "Separator electrolyte concentration", domain="separator", + "Separator electrolyte concentration", domain="separator" ) c_e_p = pybamm.Variable( - "Positive electrolyte concentration", domain="positive electrode", + "Positive electrolyte concentration", domain="positive electrode" ) # Concatenations combine several variables into a single variable, to simplify # implementing equations that hold over several domains @@ -56,40 +56,38 @@ def __init__(self, name="Basic full model"): # Electrolyte potential phi_e_n = pybamm.Variable( - "Negative electrolyte potential", domain="negative electrode", - ) - phi_e_s = pybamm.Variable( - "Separator electrolyte potential", domain="separator", + "Negative electrolyte potential", domain="negative electrode" ) + phi_e_s = pybamm.Variable("Separator electrolyte potential", domain="separator") phi_e_p = pybamm.Variable( - "Positive electrolyte potential", domain="positive electrode", + "Positive electrolyte potential", domain="positive electrode" ) phi_e = pybamm.Concatenation(phi_e_n, phi_e_s, phi_e_p) # Electrode potential phi_s_n = pybamm.Variable( - "Negative electrode potential", domain="negative electrode", + "Negative electrode potential", domain="negative electrode" ) phi_s_p = pybamm.Variable( - "Positive electrode potential", domain="positive electrode", + "Positive electrode potential", domain="positive electrode" ) # Porosity eps_n = pybamm.Variable( - "Negative electrode porosity", domain="negative electrode", + "Negative electrode porosity", domain="negative electrode" ) eps_s = pybamm.Variable("Separator porosity", domain="separator") eps_p = pybamm.Variable( - "Positive electrode porosity", domain="positive electrode", + "Positive electrode porosity", domain="positive electrode" ) eps = pybamm.Concatenation(eps_n, eps_s, eps_p) # Pressure (for convection) pressure_n = pybamm.Variable( - "Negative electrolyte pressure", domain="negative electrode", + "Negative electrolyte pressure", domain="negative electrode" ) pressure_p = pybamm.Variable( - "Positive electrolyte pressure", domain="positive electrode", + "Positive electrolyte pressure", domain="positive electrode" ) # Constant temperature @@ -138,8 +136,8 @@ def __init__(self, name="Basic full model"): ###################### v_n = -pybamm.grad(pressure_n) v_p = -pybamm.grad(pressure_p) - l_s = pybamm.geometric_parameters.l_s - l_n = pybamm.geometric_parameters.l_n + l_s = param.l_s + l_n = param.l_n x_s = pybamm.SpatialVariable("x_s", domain="separator") # Difference in negative and positive electrode velocities determines the diff --git a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py index e32fb95329..f5037bc450 100644 --- a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py +++ b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py @@ -15,7 +15,7 @@ class BaseModel(pybamm.BaseBatteryModel): def __init__(self, options=None, name="Unnamed lithium-ion model"): super().__init__(options, name) - self.param = pybamm.standard_parameters_lithium_ion + self.param = pybamm.LithiumIonParameters(options) # Default timescale is discharge timescale self.timescale = self.param.tau_discharge @@ -37,13 +37,12 @@ def set_standard_output_variables(self): # Particle concentration position var = pybamm.standard_spatial_vars - param = pybamm.geometric_parameters self.variables.update( { "r_n": var.r_n, - "r_n [m]": var.r_n * param.R_n, + "r_n [m]": var.r_n * self.param.R_n, "r_p": var.r_p, - "r_p [m]": var.r_p * param.R_p, + "r_p [m]": var.r_p * self.param.R_p, } ) diff --git a/pybamm/models/submodels/convection/through_cell/explicit_convection.py b/pybamm/models/submodels/convection/through_cell/explicit_convection.py index 040c0ed7da..62156f275d 100644 --- a/pybamm/models/submodels/convection/through_cell/explicit_convection.py +++ b/pybamm/models/submodels/convection/through_cell/explicit_convection.py @@ -24,7 +24,7 @@ def get_coupled_variables(self, variables): # Set up param = self.param - l_n = pybamm.geometric_parameters.l_n + l_n = param.l_n x_n = pybamm.standard_spatial_vars.x_n x_s = pybamm.standard_spatial_vars.x_s x_p = pybamm.standard_spatial_vars.x_p diff --git a/pybamm/models/submodels/convection/through_cell/full_convection.py b/pybamm/models/submodels/convection/through_cell/full_convection.py index 752abc9f7a..c4e968ad9b 100644 --- a/pybamm/models/submodels/convection/through_cell/full_convection.py +++ b/pybamm/models/submodels/convection/through_cell/full_convection.py @@ -56,7 +56,7 @@ def get_coupled_variables(self, variables): # Set up param = self.param - l_n = pybamm.geometric_parameters.l_n + l_n = param.l_n x_s = pybamm.standard_spatial_vars.x_s # Transverse velocity in the separator determines through-cell velocity diff --git a/pybamm/models/submodels/current_collector/effective_resistance_current_collector.py b/pybamm/models/submodels/current_collector/effective_resistance_current_collector.py index 45ac5e00d3..ac568f8fb0 100644 --- a/pybamm/models/submodels/current_collector/effective_resistance_current_collector.py +++ b/pybamm/models/submodels/current_collector/effective_resistance_current_collector.py @@ -38,7 +38,7 @@ def __init__( ): super().__init__(name) self.options = options - self.param = pybamm.standard_parameters_lithium_ion + self.param = pybamm.LithiumIonParameters() # Set default length scales self.length_scales = { @@ -100,8 +100,8 @@ def get_fundamental_variables(self): # Add spatial variables var = pybamm.standard_spatial_vars - L_y = pybamm.geometric_parameters.L_y - L_z = pybamm.geometric_parameters.L_z + L_y = param.L_y + L_z = param.L_z if self.options["dimensionality"] == 1: variables.update({"z": var.z, "z [m]": var.z * L_z}) elif self.options["dimensionality"] == 2: @@ -217,33 +217,30 @@ def default_parameter_values(self): @property def default_geometry(self): geometry = {} + param = self.param var = pybamm.standard_spatial_vars if self.options["dimensionality"] == 1: geometry["current collector"] = { var.z: {"min": 0, "max": 1}, "tabs": { - "negative": { - "z_centre": pybamm.geometric_parameters.centre_z_tab_n - }, - "positive": { - "z_centre": pybamm.geometric_parameters.centre_z_tab_p - }, + "negative": {"z_centre": param.centre_z_tab_n}, + "positive": {"z_centre": param.centre_z_tab_p}, }, } elif self.options["dimensionality"] == 2: geometry["current collector"] = { - var.y: {"min": 0, "max": pybamm.geometric_parameters.l_y}, - var.z: {"min": 0, "max": pybamm.geometric_parameters.l_z}, + var.y: {"min": 0, "max": param.l_y}, + var.z: {"min": 0, "max": param.l_z}, "tabs": { "negative": { - "y_centre": pybamm.geometric_parameters.centre_y_tab_n, - "z_centre": pybamm.geometric_parameters.centre_z_tab_n, - "width": pybamm.geometric_parameters.l_tab_n, + "y_centre": param.centre_y_tab_n, + "z_centre": param.centre_z_tab_n, + "width": param.l_tab_n, }, "positive": { - "y_centre": pybamm.geometric_parameters.centre_y_tab_p, - "z_centre": pybamm.geometric_parameters.centre_z_tab_p, - "width": pybamm.geometric_parameters.l_tab_p, + "y_centre": param.centre_y_tab_p, + "z_centre": param.centre_z_tab_p, + "width": param.l_tab_p, }, }, } @@ -317,7 +314,7 @@ class AlternativeEffectiveResistance2D(pybamm.BaseModel): def __init__(self): super().__init__() self.name = "Effective resistance in current collector model (2D)" - self.param = pybamm.standard_parameters_lithium_ion + self.param = pybamm.LithiumIonParameters() # Set default length scales self.length_scales = { @@ -396,8 +393,8 @@ def __init__(self): # Add spatial variables var = pybamm.standard_spatial_vars - L_y = pybamm.geometric_parameters.L_y - L_z = pybamm.geometric_parameters.L_z + L_y = param.L_y + L_z = param.L_z self.variables.update( {"y": var.y, "y [m]": var.y * L_y, "z": var.z, "z [m]": var.z * L_z} ) @@ -475,21 +472,22 @@ def default_parameter_values(self): @property def default_geometry(self): + param = self.param var = pybamm.standard_spatial_vars geometry = { "current collector": { - var.y: {"min": 0, "max": pybamm.geometric_parameters.l_y}, - var.z: {"min": 0, "max": pybamm.geometric_parameters.l_z}, + var.y: {"min": 0, "max": param.l_y}, + var.z: {"min": 0, "max": param.l_z}, "tabs": { "negative": { - "y_centre": pybamm.geometric_parameters.centre_y_tab_n, - "z_centre": pybamm.geometric_parameters.centre_z_tab_n, - "width": pybamm.geometric_parameters.l_tab_n, + "y_centre": param.centre_y_tab_n, + "z_centre": param.centre_z_tab_n, + "width": param.l_tab_n, }, "positive": { - "y_centre": pybamm.geometric_parameters.centre_y_tab_p, - "z_centre": pybamm.geometric_parameters.centre_z_tab_p, - "width": pybamm.geometric_parameters.l_tab_p, + "y_centre": param.centre_y_tab_p, + "z_centre": param.centre_z_tab_p, + "width": param.l_tab_p, }, }, } diff --git a/pybamm/models/submodels/external_circuit/experiment_events.py b/pybamm/models/submodels/external_circuit/experiment_events.py index 1eaef5a47f..60fc0a31c0 100644 --- a/pybamm/models/submodels/external_circuit/experiment_events.py +++ b/pybamm/models/submodels/external_circuit/experiment_events.py @@ -13,7 +13,7 @@ def __init__(self, param): def set_events(self, variables): # add current and voltage events to the model # current events both negative and positive to catch specification - n_cells = pybamm.electrical_parameters.n_cells + n_cells = self.param.n_cells self.events.extend( [ pybamm.Event( diff --git a/pybamm/models/submodels/interface/base_interface.py b/pybamm/models/submodels/interface/base_interface.py index 10b58b9d1b..e248e25218 100644 --- a/pybamm/models/submodels/interface/base_interface.py +++ b/pybamm/models/submodels/interface/base_interface.py @@ -204,10 +204,10 @@ def _get_average_total_interfacial_current_density(self, variables): i_boundary_cc = variables["Current collector current density"] if self.domain == "Negative": - j_total_average = i_boundary_cc / pybamm.geometric_parameters.l_n + j_total_average = i_boundary_cc / self.param.l_n elif self.domain == "Positive": - j_total_average = -i_boundary_cc / pybamm.geometric_parameters.l_p + j_total_average = -i_boundary_cc / self.param.l_p return j_total_average diff --git a/pybamm/models/submodels/particle/base_particle.py b/pybamm/models/submodels/particle/base_particle.py index 28a696f3c6..1c3d224f21 100644 --- a/pybamm/models/submodels/particle/base_particle.py +++ b/pybamm/models/submodels/particle/base_particle.py @@ -23,17 +23,16 @@ def __init__(self, param, domain): def _get_standard_concentration_variables(self, c_s, c_s_xav): + param = self.param c_s_surf = pybamm.surf(c_s) - c_s_surf_av = pybamm.x_average(c_s_surf) - geo_param = pybamm.geometric_parameters if self.domain == "Negative": c_scale = self.param.c_n_max - active_volume = geo_param.a_n_dim * geo_param.R_n / 3 + active_volume = param.a_n_dim * param.R_n / 3 elif self.domain == "Positive": c_scale = self.param.c_p_max - active_volume = geo_param.a_p_dim * geo_param.R_p / 3 + active_volume = param.a_p_dim * param.R_p / 3 c_s_av = pybamm.r_average(c_s_xav) c_s_av_vol = active_volume * c_s_av variables = { diff --git a/pybamm/parameters/electrical_parameters.py b/pybamm/parameters/electrical_parameters.py index b98c0d2e7f..c233a4c622 100644 --- a/pybamm/parameters/electrical_parameters.py +++ b/pybamm/parameters/electrical_parameters.py @@ -5,34 +5,62 @@ import numpy as np -# -------------------------------------------------------------------------------------- -# Dimensional Parameters -I_typ = pybamm.Parameter("Typical current [A]") -Q = pybamm.Parameter("Cell capacity [A.h]") -C_rate = abs(I_typ / Q) -n_electrodes_parallel = pybamm.Parameter( - "Number of electrodes connected in parallel to make a cell" -) -n_cells = pybamm.Parameter("Number of cells connected in series to make a battery") -i_typ = pybamm.Function( - np.abs, I_typ / (n_electrodes_parallel * pybamm.geometric_parameters.A_cc) -) -voltage_low_cut_dimensional = pybamm.Parameter("Lower voltage cut-off [V]") -voltage_high_cut_dimensional = pybamm.Parameter("Upper voltage cut-off [V]") - -# Current as a function of *dimensional* time. The below is overwritten in -# standard_parameters_lithium_ion.py and standard_parameters_lead_acid.py -# to use the correct timescale used for non-dimensionalisation. For a base model, -# the user may provide the typical timescale as a parameter. -timescale = pybamm.Parameter("Typical timescale [s]") -dimensional_current_with_time = pybamm.FunctionParameter( - "Current function [A]", {"Time[s]": pybamm.t * timescale} -) -dimensional_current_density_with_time = dimensional_current_with_time / ( - n_electrodes_parallel * pybamm.geometric_parameters.A_cc -) - -# Dimensionless current -current_with_time = ( - dimensional_current_with_time / I_typ * pybamm.Function(np.sign, I_typ) -) +class ElectricalParameters: + """ + Standard electrical parameters + + Layout: + 1. Dimensional Parameters + 2. Dimensionless Parameters + """ + + def __init__(self): + + # Get geometric parameters + self.geo = pybamm.GeometricParameters() + + # Set parameters + self._set_dimensional_parameters() + self._set_dimensionless_parameters() + + def _set_dimensional_parameters(self): + "Defines the dimensional parameters" + + self.I_typ = pybamm.Parameter("Typical current [A]") + self.Q = pybamm.Parameter("Cell capacity [A.h]") + self.C_rate = abs(self.I_typ / self.Q) + self.n_electrodes_parallel = pybamm.Parameter( + "Number of electrodes connected in parallel to make a cell" + ) + self.n_cells = pybamm.Parameter( + "Number of cells connected in series to make a battery" + ) + self.i_typ = pybamm.Function( + np.abs, self.I_typ / (self.n_electrodes_parallel * self.geo.A_cc) + ) + self.voltage_low_cut_dimensional = pybamm.Parameter("Lower voltage cut-off [V]") + self.voltage_high_cut_dimensional = pybamm.Parameter( + "Upper voltage cut-off [V]" + ) + + # Current as a function of *dimensional* time. The below is overwritten in + # lithium_ion_parameters.py and lead_acid_parameters.py to use the correct + # timescale used for non-dimensionalisation. For a base model, the user may + # provide the typical timescale as a parameter. + self.timescale = pybamm.Parameter("Typical timescale [s]") + self.dimensional_current_with_time = pybamm.FunctionParameter( + "Current function [A]", {"Time[s]": pybamm.t * self.timescale} + ) + self.dimensional_current_density_with_time = ( + self.dimensional_current_with_time + / (self.n_electrodes_parallel * self.geo.A_cc) + ) + + def _set_dimensionless_parameters(self): + "Defines the dimensionless parameters" + + self.current_with_time = ( + self.dimensional_current_with_time + / self.I_typ + * pybamm.Function(np.sign, self.I_typ) + ) diff --git a/pybamm/parameters/geometric_parameters.py b/pybamm/parameters/geometric_parameters.py index ee4554b258..3650218113 100644 --- a/pybamm/parameters/geometric_parameters.py +++ b/pybamm/parameters/geometric_parameters.py @@ -1,72 +1,100 @@ # # Geometric Parameters # -""" -Standard geometric parameters -""" import pybamm -# -------------------------------------------------------------------------------------- -"Dimensional Parameters" -# Macroscale geometry -L_cn = pybamm.Parameter("Negative current collector thickness [m]") -L_n = pybamm.Parameter("Negative electrode thickness [m]") -L_s = pybamm.Parameter("Separator thickness [m]") -L_p = pybamm.Parameter("Positive electrode thickness [m]") -L_cp = pybamm.Parameter("Positive current collector thickness [m]") -L_x = L_n + L_s + L_p # Total distance between current collectors -L = L_cn + L_x + L_cp # Total cell thickness -L_y = pybamm.Parameter("Electrode width [m]") -L_z = pybamm.Parameter("Electrode height [m]") -A_cc = L_y * L_z # Area of current collector -A_cooling = pybamm.Parameter("Cell cooling surface area [m2]") -V_cell = pybamm.Parameter("Cell volume [m3]") -# Tab geometry -L_tab_n = pybamm.Parameter("Negative tab width [m]") -Centre_y_tab_n = pybamm.Parameter("Negative tab centre y-coordinate [m]") -Centre_z_tab_n = pybamm.Parameter("Negative tab centre z-coordinate [m]") -L_tab_p = pybamm.Parameter("Positive tab width [m]") -Centre_y_tab_p = pybamm.Parameter("Positive tab centre y-coordinate [m]") -Centre_z_tab_p = pybamm.Parameter("Positive tab centre z-coordinate [m]") -A_tab_n = L_tab_n * L_cn # Area of negative tab -A_tab_p = L_tab_p * L_cp # Area of negative tab +class GeometricParameters: + """ + Standard geometric parameters + Layout: + 1. Dimensional Parameters + 2. Dimensionless Parameters + """ -# Microscale geometry -a_n_dim = pybamm.Parameter("Negative electrode surface area to volume ratio [m-1]") -a_p_dim = pybamm.Parameter("Positive electrode surface area to volume ratio [m-1]") -R_n = pybamm.Parameter("Negative particle radius [m]") -R_p = pybamm.Parameter("Positive particle radius [m]") -b_e_n = pybamm.Parameter("Negative electrode Bruggeman coefficient (electrolyte)") -b_e_s = pybamm.Parameter("Separator Bruggeman coefficient (electrolyte)") -b_e_p = pybamm.Parameter("Positive electrode Bruggeman coefficient (electrolyte)") -b_s_n = pybamm.Parameter("Negative electrode Bruggeman coefficient (electrode)") -b_s_s = pybamm.Parameter("Separator Bruggeman coefficient (electrode)") -b_s_p = pybamm.Parameter("Positive electrode Bruggeman coefficient (electrode)") + def __init__(self): -# -------------------------------------------------------------------------------------- -"Dimensionless Parameters" -# Macroscale Geometry -l_cn = L_cn / L_x -l_n = L_n / L_x -l_s = L_s / L_x -l_p = L_p / L_x -l_cp = L_cp / L_x -l_x = L_x / L_x -l_y = L_y / L_z -l_z = L_z / L_z -a_cc = l_y * l_z -a_cooling = A_cooling / (L_z ** 2) -v_cell = V_cell / (L_x * L_z ** 2) + # Set parameters + self._set_dimensional_parameters() + self._set_dimensionless_parameters() -l = L / L_x -delta = L_x / L_z # Aspect ratio + def _set_dimensional_parameters(self): + "Defines the dimensional parameters" -# Tab geometry -l_tab_n = L_tab_n / L_z -centre_y_tab_n = Centre_y_tab_n / L_z -centre_z_tab_n = Centre_z_tab_n / L_z -l_tab_p = L_tab_p / L_z -centre_y_tab_p = Centre_y_tab_p / L_z -centre_z_tab_p = Centre_z_tab_p / L_z + # Macroscale geometry + self.L_cn = pybamm.Parameter("Negative current collector thickness [m]") + self.L_n = pybamm.Parameter("Negative electrode thickness [m]") + self.L_s = pybamm.Parameter("Separator thickness [m]") + self.L_p = pybamm.Parameter("Positive electrode thickness [m]") + self.L_cp = pybamm.Parameter("Positive current collector thickness [m]") + self.L_x = ( + self.L_n + self.L_s + self.L_p + ) # Total distance between current collectors + self.L = self.L_cn + self.L_x + self.L_cp # Total cell thickness + self.L_y = pybamm.Parameter("Electrode width [m]") + self.L_z = pybamm.Parameter("Electrode height [m]") + self.A_cc = self.L_y * self.L_z # Area of current collector + self.A_cooling = pybamm.Parameter("Cell cooling surface area [m2]") + self.V_cell = pybamm.Parameter("Cell volume [m3]") + + # Tab geometry + self.L_tab_n = pybamm.Parameter("Negative tab width [m]") + self.Centre_y_tab_n = pybamm.Parameter("Negative tab centre y-coordinate [m]") + self.Centre_z_tab_n = pybamm.Parameter("Negative tab centre z-coordinate [m]") + self.L_tab_p = pybamm.Parameter("Positive tab width [m]") + self.Centre_y_tab_p = pybamm.Parameter("Positive tab centre y-coordinate [m]") + self.Centre_z_tab_p = pybamm.Parameter("Positive tab centre z-coordinate [m]") + self.A_tab_n = self.L_tab_n * self.L_cn # Area of negative tab + self.A_tab_p = self.L_tab_p * self.L_cp # Area of negative tab + + # Microscale geometry + self.a_n_dim = pybamm.Parameter( + "Negative electrode surface area to volume ratio [m-1]" + ) + self.a_p_dim = pybamm.Parameter( + "Positive electrode surface area to volume ratio [m-1]" + ) + self.R_n = pybamm.Parameter("Negative particle radius [m]") + self.R_p = pybamm.Parameter("Positive particle radius [m]") + self.b_e_n = pybamm.Parameter( + "Negative electrode Bruggeman coefficient (electrolyte)" + ) + self.b_e_s = pybamm.Parameter("Separator Bruggeman coefficient (electrolyte)") + self.b_e_p = pybamm.Parameter( + "Positive electrode Bruggeman coefficient (electrolyte)" + ) + self.b_s_n = pybamm.Parameter( + "Negative electrode Bruggeman coefficient (electrode)" + ) + self.b_s_s = pybamm.Parameter("Separator Bruggeman coefficient (electrode)") + self.b_s_p = pybamm.Parameter( + "Positive electrode Bruggeman coefficient (electrode)" + ) + + def _set_dimensionless_parameters(self): + "Defines the dimensionless parameters" + + # Macroscale Geometry + self.l_cn = self.L_cn / self.L_x + self.l_n = self.L_n / self.L_x + self.l_s = self.L_s / self.L_x + self.l_p = self.L_p / self.L_x + self.l_cp = self.L_cp / self.L_x + self.l_x = self.L_x / self.L_x + self.l_y = self.L_y / self.L_z + self.l_z = self.L_z / self.L_z + self.a_cc = self.l_y * self.l_z + self.a_cooling = self.A_cooling / (self.L_z ** 2) + self.v_cell = self.V_cell / (self.L_x * self.L_z ** 2) + + self.l = self.L / self.L_x + self.delta = self.L_x / self.L_z # Aspect ratio + + # Tab geometry + self.l_tab_n = self.L_tab_n / self.L_z + self.centre_y_tab_n = self.Centre_y_tab_n / self.L_z + self.centre_z_tab_n = self.Centre_z_tab_n / self.L_z + self.l_tab_p = self.L_tab_p / self.L_z + self.centre_y_tab_p = self.Centre_y_tab_p / self.L_z + self.centre_z_tab_p = self.Centre_z_tab_p / self.L_z diff --git a/pybamm/parameters/lead_acid_parameters.py b/pybamm/parameters/lead_acid_parameters.py new file mode 100644 index 0000000000..31b6641b8c --- /dev/null +++ b/pybamm/parameters/lead_acid_parameters.py @@ -0,0 +1,728 @@ +# +# Standard parameters for lead-acid battery models +# + +import pybamm +import numpy as np + + +class LeadAcidParameters: + """ + Standard Parameters for lead-acid battery models + + Layout: + 1. Dimensional Parameters + 2. Dimensional Functions + 3. Scalings + 4. Dimensionless Parameters + 5. Dimensionless Functions + 6. Input Current + """ + + def __init__(self): + + # Get geometric, electrical and thermal parameters + self.geo = pybamm.GeometricParameters() + self.elec = pybamm.ElectricalParameters() + self.therm = pybamm.ThermalParameters() + + # Set parameters and scales + self._set_dimensional_parameters() + self._set_scales() + self._set_dimensionless_parameters() + + # Set input current + self._set_input_current() + + def _set_dimensional_parameters(self): + "Defines the dimensional parameters" + + # Physical constants + self.R = pybamm.constants.R + self.F = pybamm.constants.F + self.T_ref = self.therm.T_ref + + # Macroscale geometry + self.L_n = self.geo.L_n + self.L_s = self.geo.L_s + self.L_p = self.geo.L_p + self.L_x = self.geo.L_x + self.L_y = self.geo.L_y + self.L_z = self.geo.L_z + self.A_cc = self.geo.A_cc + self.A_cooling = self.geo.A_cooling + self.V_cell = self.geo.V_cell + self.W = self.L_y + self.H = self.L_z + self.A_cs = self.A_cc + self.delta = self.L_x / self.H + + # Electrical + self.I_typ = self.elec.I_typ + self.Q = self.elec.Q + self.C_rate = self.elec.C_rate + self.n_electrodes_parallel = self.elec.n_electrodes_parallel + self.n_cells = self.elec.n_cells + self.i_typ = self.elec.i_typ + self.voltage_low_cut_dimensional = self.elec.voltage_low_cut_dimensional + self.voltage_high_cut_dimensional = self.elec.voltage_high_cut_dimensional + + # Electrolyte properties + self.c_e_typ = pybamm.Parameter("Typical electrolyte concentration [mol.m-3]") + self.V_w = pybamm.Parameter("Partial molar volume of water [m3.mol-1]") + self.V_plus = pybamm.Parameter("Partial molar volume of cations [m3.mol-1]") + self.V_minus = pybamm.Parameter("Partial molar volume of anions [m3.mol-1]") + self.V_e = ( + self.V_minus + self.V_plus + ) # Partial molar volume of electrolyte [m3.mol-1] + self.nu_plus = pybamm.Parameter("Cation stoichiometry") + self.nu_minus = pybamm.Parameter("Anion stoichiometry") + self.nu = self.nu_plus + self.nu_minus + + # Other species properties + self.c_ox_init_dim = pybamm.Parameter("Initial oxygen concentration [mol.m-3]") + self.c_ox_typ = ( + self.c_e_typ + ) # pybamm.Parameter("Typical oxygen concentration [mol.m-3]") + + # Microstructure + self.a_n_dim = self.geo.a_n_dim + self.a_p_dim = self.geo.a_p_dim + self.b_e_n = self.geo.b_e_n + self.b_e_s = self.geo.b_e_s + self.b_e_p = self.geo.b_e_p + self.b_s_n = self.geo.b_s_n + self.b_s_s = self.geo.b_s_s + self.b_s_p = self.geo.b_s_p + self.xi_n = pybamm.Parameter("Negative electrode morphological parameter") + self.xi_p = pybamm.Parameter("Positive electrode morphological parameter") + # no binder + self.epsilon_inactive_n = pybamm.Scalar(0) + self.epsilon_inactive_s = pybamm.Scalar(0) + self.epsilon_inactive_p = pybamm.Scalar(0) + + # Electrode properties + self.V_Pb = pybamm.Parameter("Molar volume of lead [m3.mol-1]") + self.V_PbO2 = pybamm.Parameter("Molar volume of lead-dioxide [m3.mol-1]") + self.V_PbSO4 = pybamm.Parameter("Molar volume of lead sulfate [m3.mol-1]") + self.DeltaVsurf_n = ( + self.V_Pb - self.V_PbSO4 + ) # Net Molar Volume consumed in neg electrode [m3.mol-1] + self.DeltaVsurf_p = ( + self.V_PbSO4 - self.V_PbO2 + ) # Net Molar Volume consumed in pos electrode [m3.mol-1] + self.d_n = pybamm.Parameter("Negative electrode pore size [m]") + self.d_p = pybamm.Parameter("Positive electrode pore size [m]") + self.eps_n_max = pybamm.Parameter("Maximum porosity of negative electrode") + self.eps_s_max = pybamm.Parameter("Maximum porosity of separator") + self.eps_p_max = pybamm.Parameter("Maximum porosity of positive electrode") + self.Q_n_max_dimensional = pybamm.Parameter( + "Negative electrode volumetric capacity [C.m-3]" + ) + self.Q_p_max_dimensional = pybamm.Parameter( + "Positive electrode volumetric capacity [C.m-3]" + ) + self.sigma_n_dim = pybamm.Parameter("Negative electrode conductivity [S.m-1]") + self.sigma_p_dim = pybamm.Parameter("Positive electrode conductivity [S.m-1]") + # In lead-acid the current collector and electrodes are the same (same + # conductivity) but we correct here for Bruggeman + self.sigma_cn_dimensional = ( + self.sigma_n_dim * (1 - self.eps_n_max) ** self.b_s_n + ) + self.sigma_cp_dimensional = ( + self.sigma_p_dim * (1 - self.eps_p_max) ** self.b_s_p + ) + + # Electrochemical reactions + # Main + self.s_plus_n_S_dim = pybamm.Parameter( + "Negative electrode cation signed stoichiometry" + ) + self.s_plus_p_S_dim = pybamm.Parameter( + "Positive electrode cation signed stoichiometry" + ) + self.ne_n_S = pybamm.Parameter("Negative electrode electrons in reaction") + self.ne_p_S = pybamm.Parameter("Positive electrode electrons in reaction") + self.C_dl_n_dimensional = pybamm.Parameter( + "Negative electrode double-layer capacity [F.m-2]" + ) + self.C_dl_p_dimensional = pybamm.Parameter( + "Positive electrode double-layer capacity [F.m-2]" + ) + # Oxygen + self.s_plus_Ox_dim = pybamm.Parameter( + "Signed stoichiometry of cations (oxygen reaction)" + ) + self.s_w_Ox_dim = pybamm.Parameter( + "Signed stoichiometry of water (oxygen reaction)" + ) + self.s_ox_Ox_dim = pybamm.Parameter( + "Signed stoichiometry of oxygen (oxygen reaction)" + ) + self.ne_Ox = pybamm.Parameter("Electrons in oxygen reaction") + self.U_Ox_dim = pybamm.Parameter("Oxygen reference OCP vs SHE [V]") + # Hydrogen + self.s_plus_Hy_dim = pybamm.Parameter( + "Signed stoichiometry of cations (hydrogen reaction)" + ) + self.s_hy_Hy_dim = pybamm.Parameter( + "Signed stoichiometry of hydrogen (hydrogen reaction)" + ) + self.ne_Hy = pybamm.Parameter("Electrons in hydrogen reaction") + self.U_Hy_dim = pybamm.Parameter("Hydrogen reference OCP vs SHE [V]") + + # Electrolyte properties + self.M_w = pybamm.Parameter("Molar mass of water [kg.mol-1]") + self.M_plus = pybamm.Parameter("Molar mass of cations [kg.mol-1]") + self.M_minus = pybamm.Parameter("Molar mass of anions [kg.mol-1]") + self.M_e = self.M_minus + self.M_plus # Molar mass of electrolyte [kg.mol-1] + + self.DeltaVliq_n = ( + self.V_minus - self.V_plus + ) # Net Molar Volume consumed in electrolyte (neg) [m3.mol-1] + self.DeltaVliq_p = ( + 2 * self.V_w - self.V_minus - 3 * self.V_plus + ) # Net Molar Volume consumed in electrolyte (neg) [m3.mol-1] + + # Other species properties + self.D_ox_dimensional = pybamm.Parameter("Oxygen diffusivity [m2.s-1]") + self.D_hy_dimensional = pybamm.Parameter("Hydrogen diffusivity [m2.s-1]") + self.V_ox = pybamm.Parameter( + "Partial molar volume of oxygen molecules [m3.mol-1]" + ) + self.V_hy = pybamm.Parameter( + "Partial molar volume of hydrogen molecules [m3.mol-1]" + ) + self.M_ox = pybamm.Parameter("Molar mass of oxygen molecules [kg.mol-1]") + self.M_hy = pybamm.Parameter("Molar mass of hydrogen molecules [kg.mol-1]") + + # Electrode properties + self.V_Pb = pybamm.Parameter("Molar volume of lead [m3.mol-1]") + self.V_PbO2 = pybamm.Parameter("Molar volume of lead-dioxide [m3.mol-1]") + self.V_PbSO4 = pybamm.Parameter("Molar volume of lead sulfate [m3.mol-1]") + self.DeltaVsurf_n = ( + self.V_Pb - self.V_PbSO4 + ) # Net Molar Volume consumed in neg electrode [m3.mol-1] + self.DeltaVsurf_p = ( + self.V_PbSO4 - self.V_PbO2 + ) # Net Molar Volume consumed in pos electrode [m3.mol-1] + self.d_n = pybamm.Parameter("Negative electrode pore size [m]") + self.d_p = pybamm.Parameter("Positive electrode pore size [m]") + self.eps_n_max = pybamm.Parameter("Maximum porosity of negative electrode") + self.eps_s_max = pybamm.Parameter("Maximum porosity of separator") + self.eps_p_max = pybamm.Parameter("Maximum porosity of positive electrode") + self.Q_n_max_dimensional = pybamm.Parameter( + "Negative electrode volumetric capacity [C.m-3]" + ) + self.Q_p_max_dimensional = pybamm.Parameter( + "Positive electrode volumetric capacity [C.m-3]" + ) + + # Thermal + self.Delta_T = self.therm.Delta_T + + # SEI parameters (for compatibility) + self.R_sei_dimensional = pybamm.Scalar(0) + self.beta_sei_n = pybamm.Scalar(0) + + def t_plus(self, c_e): + "Dimensionless transference number (i.e. c_e is dimensionless)" + inputs = {"Electrolyte concentration [mol.m-3]": c_e * self.c_e_typ} + return pybamm.FunctionParameter("Cation transference number", inputs) + + def D_e_dimensional(self, c_e, T): + "Dimensional diffusivity in electrolyte" + inputs = {"Electrolyte concentration [mol.m-3]": c_e} + return pybamm.FunctionParameter("Electrolyte diffusivity [m2.s-1]", inputs) + + def kappa_e_dimensional(self, c_e, T): + "Dimensional electrolyte conductivity" + inputs = {"Electrolyte concentration [mol.m-3]": c_e} + return pybamm.FunctionParameter("Electrolyte conductivity [S.m-1]", inputs) + + def chi_dimensional(self, c_e): + inputs = {"Electrolyte concentration [mol.m-3]": c_e} + return pybamm.FunctionParameter("Darken thermodynamic factor", inputs) + + def c_T(self, c_e, c_ox=0, c_hy=0): + """ + Total liquid molarity [mol.m-3], from thermodynamics. c_k in [mol.m-3]. + """ + return ( + 1 + + (2 * self.V_w - self.V_e) * c_e + + (self.V_w - self.V_ox) * c_ox + + (self.V_w - self.V_hy) * c_hy + ) / self.V_w + + def rho_dimensional(self, c_e, c_ox=0, c_hy=0): + """ + Dimensional density of electrolyte [kg.m-3], from thermodynamics. + c_k in [mol.m-3]. + """ + return ( + self.M_w / self.V_w + + (self.M_e - self.V_e * self.M_w / self.V_w) * c_e + + (self.M_ox - self.V_ox * self.M_w / self.V_w) * c_ox + + (self.M_hy - self.V_hy * self.M_w / self.V_w) * c_hy + ) + + def m_dimensional(self, c_e): + """ + Dimensional electrolyte molar mass [mol.kg-1], from thermodynamics. + c_e in [mol.m-3]. + """ + return c_e * self.V_w / ((1 - c_e * self.V_e) * self.M_w) + + def mu_dimensional(self, c_e): + """ + Dimensional viscosity of electrolyte [kg.m-1.s-1]. + """ + inputs = {"Electrolyte concentration [mol.m-3]": c_e} + return pybamm.FunctionParameter("Electrolyte viscosity [kg.m-1.s-1]", inputs) + + def U_n_dimensional(self, c_e, T): + "Dimensional open-circuit voltage in the negative electrode [V]" + inputs = {"Electrolyte molar mass [mol.kg-1]": self.m_dimensional(c_e)} + return pybamm.FunctionParameter( + "Negative electrode open-circuit potential [V]", inputs + ) + + def U_p_dimensional(self, c_e, T): + "Dimensional open-circuit voltage in the positive electrode [V]" + inputs = {"Electrolyte molar mass [mol.kg-1]": self.m_dimensional(c_e)} + return pybamm.FunctionParameter( + "Positive electrode open-circuit potential [V]", inputs + ) + + def j0_n_dimensional(self, c_e, T): + "Dimensional negative electrode exchange-current density [A.m-2]" + inputs = {"Electrolyte concentration [mol.m-3]": c_e, "Temperature [K]": T} + return pybamm.FunctionParameter( + "Negative electrode exchange-current density [A.m-2]", inputs + ) + + def j0_p_dimensional(self, c_e, T): + "Dimensional positive electrode exchange-current density [A.m-2]" + inputs = {"Electrolyte concentration [mol.m-3]": c_e, "Temperature [K]": T} + return pybamm.FunctionParameter( + "Positive electrode exchange-current density [A.m-2]", inputs + ) + + def j0_p_Ox_dimensional(self, c_e, T): + "Dimensional oxygen positive electrode exchange-current density [A.m-2]" + inputs = {"Electrolyte concentration [mol.m-3]": c_e, "Temperature [K]": T} + return pybamm.FunctionParameter( + "Positive electrode oxygen exchange-current density [A.m-2]", inputs + ) + + def _set_scales(self): + "Define the scales used in the non-dimensionalisation scheme" + + # Concentrations + self.electrolyte_concentration_scale = self.c_e_typ + + # Electrical + self.potential_scale = self.R * self.T_ref / self.F + self.current_scale = self.i_typ + self.j_scale_n = self.i_typ / (self.a_n_dim * self.L_x) + self.j_scale_p = self.i_typ / (self.a_p_dim * self.L_x) + + # Reaction velocity scale + self.velocity_scale = self.i_typ / (self.c_e_typ * self.F) + + # Discharge timescale + self.tau_discharge = self.F * self.c_e_typ * self.L_x / self.i_typ + + # Electrolyte diffusion timescale + self.D_e_typ = self.D_e_dimensional(self.c_e_typ, self.T_ref) + self.tau_diffusion_e = self.L_x ** 2 / self.D_e_typ + + # Thermal diffusion timescale + self.tau_th_yz = self.therm.tau_th_yz + + # Choose discharge timescale + self.timescale = self.tau_discharge + + # Density + self.rho_typ = self.rho_dimensional(self.c_e_typ) + + # Viscosity + self.mu_typ = self.mu_dimensional(self.c_e_typ) + + # Reference OCP + inputs = {"Electrolyte concentration [mol.m-3]": pybamm.Scalar(1)} + self.U_n_ref = pybamm.FunctionParameter( + "Negative electrode open-circuit potential [V]", inputs + ) + inputs = {"Electrolyte concentration [mol.m-3]": pybamm.Scalar(1)} + self.U_p_ref = pybamm.FunctionParameter( + "Positive electrode open-circuit potential [V]", inputs + ) + + def _set_dimensionless_parameters(self): + "Defines the dimensionless parameters" + + # Timescale ratios + self.C_th = self.tau_th_yz / self.tau_discharge + + # Macroscale Geometry + self.l_n = self.geo.l_n + self.l_s = self.geo.l_s + self.l_p = self.geo.l_p + self.l_x = self.geo.l_x + self.l_y = self.geo.l_y + self.l_z = self.geo.l_z + self.a_cc = self.geo.a_cc + self.a_cooling = self.geo.a_cooling + self.v_cell = self.geo.v_cell + self.l = self.geo.l + self.delta = self.geo.delta + # In lead-acid the current collector and electrodes are the same (same + # thickness) + self.l_cn = self.l_n + self.l_cp = self.l_p + + # Tab geometry + self.l_tab_n = self.geo.l_tab_n + self.centre_y_tab_n = self.geo.centre_y_tab_n + self.centre_z_tab_n = self.geo.centre_z_tab_n + self.l_tab_p = self.geo.l_tab_p + self.centre_y_tab_p = self.geo.centre_y_tab_p + self.centre_z_tab_p = self.geo.centre_z_tab_p + + # Diffusive kinematic relationship coefficient + self.omega_i = ( + self.c_e_typ + * self.M_e + / self.rho_typ + * (self.t_plus(1) + self.M_minus / self.M_e) + ) + # Migrative kinematic relationship coefficient (electrolyte) + self.omega_c_e = ( + self.c_e_typ + * self.M_e + / self.rho_typ + * (1 - self.M_w * self.V_e / self.V_w * self.M_e) + ) + self.C_e = self.tau_diffusion_e / self.tau_discharge + # Ratio of viscous pressure scale to osmotic pressure scale (electrolyte) + self.pi_os_e = ( + self.mu_typ + * self.velocity_scale + * self.L_x + / (self.d_n ** 2 * self.R * self.T_ref * self.c_e_typ) + ) + # ratio of electrolyte concentration to electrode concentration, undefined + self.gamma_e = pybamm.Scalar(1) + # Reynolds number + self.Re = self.rho_typ * self.velocity_scale * self.L_x / self.mu_typ + + # Other species properties + # Oxygen + self.curlyD_ox = self.D_ox_dimensional / self.D_e_typ + self.omega_c_ox = ( + self.c_e_typ + * self.M_ox + / self.rho_typ + * (1 - self.M_w * self.V_ox / self.V_w * self.M_ox) + ) + # Hydrogen + self.curlyD_hy = self.D_hy_dimensional / self.D_e_typ + self.omega_c_hy = ( + self.c_e_typ + * self.M_hy + / self.rho_typ + * (1 - self.M_w * self.V_hy / self.V_w * self.M_hy) + ) + + # Electrode Properties + self.sigma_cn = ( + self.sigma_cn_dimensional * self.potential_scale / self.i_typ / self.L_x + ) + self.sigma_n = ( + self.sigma_n_dim * self.potential_scale / self.current_scale / self.L_x + ) + self.sigma_p = ( + self.sigma_p_dim * self.potential_scale / self.current_scale / self.L_x + ) + self.sigma_cp = ( + self.sigma_cp_dimensional * self.potential_scale / self.i_typ / self.L_x + ) + self.sigma_n_prime = self.sigma_n * self.delta ** 2 + self.sigma_p_prime = self.sigma_p * self.delta ** 2 + self.sigma_cn_prime = self.sigma_cn * self.delta ** 2 + self.sigma_cp_prime = self.sigma_cp * self.delta ** 2 + self.delta_pore_n = 1 / (self.a_n_dim * self.L_x) + self.delta_pore_p = 1 / (self.a_p_dim * self.L_x) + self.Q_n_max = self.Q_n_max_dimensional / (self.c_e_typ * self.F) + self.Q_p_max = self.Q_p_max_dimensional / (self.c_e_typ * self.F) + self.beta_U_n = 1 / self.Q_n_max + self.beta_U_p = -1 / self.Q_p_max + + # Electrochemical reactions + # Main + self.s_plus_n_S = self.s_plus_n_S_dim / self.ne_n_S + self.s_plus_p_S = self.s_plus_p_S_dim / self.ne_p_S + self.s_plus_S = pybamm.Concatenation( + pybamm.FullBroadcast( + self.s_plus_n_S, ["negative electrode"], "current collector" + ), + pybamm.FullBroadcast(0, ["separator"], "current collector"), + pybamm.FullBroadcast( + self.s_plus_p_S, ["positive electrode"], "current collector" + ), + ) + self.C_dl_n = ( + self.C_dl_n_dimensional + * self.potential_scale + / self.j_scale_n + / self.tau_discharge + ) + self.C_dl_p = ( + self.C_dl_p_dimensional + * self.potential_scale + / self.j_scale_p + / self.tau_discharge + ) + self.ne_n = self.ne_n_S + self.ne_p = self.ne_p_S + # Oxygen + self.s_plus_Ox = self.s_plus_Ox_dim / self.ne_Ox + self.s_w_Ox = self.s_w_Ox_dim / self.ne_Ox + self.s_ox_Ox = self.s_ox_Ox_dim / self.ne_Ox + # j0_n_Ox_ref = j0_n_Ox_ref_dimensional / j_scale_n + self.U_n_Ox = (self.U_Ox_dim - self.U_n_ref) / self.potential_scale + self.U_p_Ox = (self.U_Ox_dim - self.U_p_ref) / self.potential_scale + # Hydrogen + self.s_plus_Hy = self.s_plus_Hy_dim / self.ne_Hy + self.s_hy_Hy = self.s_hy_Hy_dim / self.ne_Hy + # j0_n_Hy_ref = j0_n_Hy_ref_dimensional / j_scale_n + # j0_p_Hy_ref = j0_p_Hy_ref_dimensional / j_scale_p + self.U_n_Hy = (self.U_Hy_dim - self.U_n_ref) / self.potential_scale + self.U_p_Hy = (self.U_Hy_dim - self.U_p_ref) / self.potential_scale + + # Electrolyte properties + self.beta_surf_n = ( + -self.c_e_typ * self.DeltaVsurf_n / self.ne_n_S + ) # Molar volume change (lead) + self.beta_surf_p = ( + -self.c_e_typ * self.DeltaVsurf_p / self.ne_p_S + ) # Molar volume change (lead dioxide) + self.beta_surf = pybamm.Concatenation( + pybamm.FullBroadcast( + self.beta_surf_n, ["negative electrode"], "current collector" + ), + pybamm.FullBroadcast(0, ["separator"], "current collector"), + pybamm.FullBroadcast( + self.beta_surf_p, ["positive electrode"], "current collector" + ), + ) + self.beta_liq_n = ( + -self.c_e_typ * self.DeltaVliq_n / self.ne_n_S + ) # Molar volume change (electrolyte, neg) + self.beta_liq_p = ( + -self.c_e_typ * self.DeltaVliq_p / self.ne_p_S + ) # Molar volume change (electrolyte, pos) + self.beta_n = (self.beta_surf_n + self.beta_liq_n) * pybamm.Parameter( + "Volume change factor" + ) + self.beta_p = (self.beta_surf_p + self.beta_liq_p) * pybamm.Parameter( + "Volume change factor" + ) + self.beta = pybamm.Concatenation( + pybamm.FullBroadcast( + self.beta_n, "negative electrode", "current collector" + ), + pybamm.FullBroadcast(0, "separator", "current collector"), + pybamm.FullBroadcast( + self.beta_p, "positive electrode", "current collector" + ), + ) + self.beta_Ox = -self.c_e_typ * ( + self.s_plus_Ox * self.V_plus + + self.s_w_Ox * self.V_w + + self.s_ox_Ox * self.V_ox + ) + self.beta_Hy = -self.c_e_typ * ( + self.s_plus_Hy * self.V_plus + self.s_hy_Hy * self.V_hy + ) + + # Electrical + self.voltage_low_cut = ( + self.voltage_low_cut_dimensional - (self.U_p_ref - self.U_n_ref) + ) / self.potential_scale + self.voltage_high_cut = ( + self.voltage_high_cut_dimensional - (self.U_p_ref - self.U_n_ref) + ) / self.potential_scale + + # Electrolyte volumetric capacity + self.Q_e_max = ( + self.l_n * self.eps_n_max + + self.l_s * self.eps_s_max + + self.l_p * self.eps_p_max + ) / (self.s_plus_p_S - self.s_plus_n_S) + self.Q_e_max_dimensional = self.Q_e_max * self.c_e_typ * self.F + self.capacity = ( + self.Q_e_max_dimensional * self.n_electrodes_parallel * self.A_cs * self.L_x + ) + + # Thermal + self.rho_cn = self.therm.rho_cn + self.rho_n = self.therm.rho_n + self.rho_s = self.therm.rho_s + self.rho_p = self.therm.rho_p + self.rho_cp = self.therm.rho_cp + self.rho_k = self.therm.rho_k + self.rho = ( + self.rho_cn * self.l_cn + + self.rho_n * self.l_n + + self.rho_s * self.l_s + + self.rho_p * self.l_p + + self.rho_cp * self.l_cp + ) / self.l # effective volumetric heat capacity + + self.lambda_cn = self.therm.lambda_cn + self.lambda_n = self.therm.lambda_n + self.lambda_s = self.therm.lambda_s + self.lambda_p = self.therm.lambda_p + self.lambda_cp = self.therm.lambda_cp + self.lambda_k = self.therm.lambda_k + + self.Theta = self.therm.Theta + + self.h_edge = self.therm.h_edge + self.h_tab_n = self.therm.h_tab_n + self.h_tab_p = self.therm.h_tab_p + self.h_cn = self.therm.h_cn + self.h_cp = self.therm.h_cp + self.h_total = self.therm.h_total + + self.B = ( + self.i_typ + * self.R + * self.T_ref + * self.tau_th_yz + / (self.therm.rho_eff_dim * self.F * self.Delta_T * self.L_x) + ) + + self.T_amb_dim = self.therm.T_amb_dim + self.T_amb = self.therm.T_amb + + # Initial conditions + self.T_init = self.therm.T_init + self.q_init = pybamm.Parameter("Initial State of Charge") + self.c_e_init = self.q_init + self.c_ox_init = self.c_ox_init_dim / self.c_ox_typ + self.epsilon_n_init = ( + self.eps_n_max + - self.beta_surf_n * self.Q_e_max / self.l_n * (1 - self.q_init) + ) + self.epsilon_s_init = self.eps_s_max + self.epsilon_p_init = ( + self.eps_p_max + + self.beta_surf_p * self.Q_e_max / self.l_p * (1 - self.q_init) + ) + self.epsilon_init = pybamm.Concatenation( + pybamm.FullBroadcast( + self.epsilon_n_init, ["negative electrode"], "current collector" + ), + pybamm.FullBroadcast( + self.epsilon_s_init, ["separator"], "current collector" + ), + pybamm.FullBroadcast( + self.epsilon_p_init, ["positive electrode"], "current collector" + ), + ) + self.curlyU_n_init = ( + self.Q_e_max * (1.2 - self.q_init) / (self.Q_n_max * self.l_n) + ) + self.curlyU_p_init = ( + self.Q_e_max * (1.2 - self.q_init) / (self.Q_p_max * self.l_p) + ) + + def D_e(self, c_e, T): + "Dimensionless electrolyte diffusivity" + c_e_dimensional = c_e * self.c_e_typ + return self.D_e_dimensional(c_e_dimensional, self.T_ref) / self.D_e_typ + + def kappa_e(self, c_e, T): + "Dimensionless electrolyte conductivity" + c_e_dimensional = c_e * self.c_e_typ + kappa_scale = self.F ** 2 * self.D_e_typ * self.c_e_typ / (self.R * self.T_ref) + return self.kappa_e_dimensional(c_e_dimensional, self.T_ref) / kappa_scale + + def chi(self, c_e, c_ox=0, c_hy=0): + "Thermodynamic factor" + return ( + self.chi_dimensional(self.c_e_typ * c_e) + * (2 * (1 - self.t_plus(c_e))) + / ( + self.V_w + * self.c_T(self.c_e_typ * c_e, self.c_e_typ * c_ox, self.c_e_typ * c_hy) + ) + ) + + def U_n(self, c_e_n, T): + "Dimensionless open-circuit voltage in the negative electrode" + c_e_n_dimensional = c_e_n * self.c_e_typ + T_dim = self.Delta_T * T + self.T_ref + return ( + self.U_n_dimensional(c_e_n_dimensional, T_dim) - self.U_n_ref + ) / self.potential_scale + + def U_p(self, c_e_p, T): + "Dimensionless open-circuit voltage in the positive electrode" + c_e_p_dimensional = c_e_p * self.c_e_typ + T_dim = self.Delta_T * T + self.T_ref + return ( + self.U_p_dimensional(c_e_p_dimensional, T_dim) - self.U_p_ref + ) / self.potential_scale + + def j0_n(self, c_e, T): + "Dimensionless exchange-current density in the negative electrode" + c_e_dim = c_e * self.c_e_typ + T_dim = self.Delta_T * T + self.T_ref + return self.j0_n_dimensional(c_e_dim, T_dim) / self.j_scale_n + + def j0_p(self, c_e, T): + "Dimensionless exchange-current density in the positive electrode" + c_e_dim = c_e * self.c_e_typ + T_dim = self.Delta_T * T + self.T_ref + return self.j0_p_dimensional(c_e_dim, T_dim) / self.j_scale_p + + def j0_p_Ox(self, c_e, T): + "Dimensionless oxygen exchange-current density in the positive electrode" + c_e_dim = c_e * self.c_e_typ + T_dim = self.Delta_T * T + self.T_ref + return self.j0_p_Ox_dimensional(c_e_dim, T_dim) / self.j_scale_p + + def c_n_init(self, x): + """ + Dimensionless initial concentration (as a function of dimensionless position x + to be consistent with lithium-ion) + """ + return self.c_e_init + + def c_p_init(self, x): + """ + Dimensionless initial concentration (as a function of dimensionless position x + to be consistent with lithium-ion) + """ + return self.c_e_init + + def _set_input_current(self): + "Set the input current" + + self.dimensional_current_with_time = pybamm.FunctionParameter( + "Current function [A]", {"Time [s]": pybamm.t * self.timescale} + ) + self.dimensional_current_density_with_time = ( + self.dimensional_current_with_time + / (self.n_electrodes_parallel * self.geo.A_cc) + ) + self.current_with_time = ( + self.dimensional_current_with_time + / self.I_typ + * pybamm.Function(np.sign, self.I_typ) + ) diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py new file mode 100644 index 0000000000..1dffc08adb --- /dev/null +++ b/pybamm/parameters/lithium_ion_parameters.py @@ -0,0 +1,734 @@ +# +# Standard parameters for lithium-ion battery models +# +import pybamm +import numpy as np + + +class LithiumIonParameters: + """ + Standard parameters for lithium-ion battery models + + Layout: + 1. Dimensional Parameters + 2. Dimensional Functions + 3. Scalings + 4. Dimensionless Parameters + 5. Dimensionless Functions + 6. Input Current + + Parameters + ---------- + + options : dict, optional + A dictionary of options to be passed to the parameters. The options that + can be set are listed below. + + * "particle shape" : str, optional + Sets the model shape of the particles in the electrode. This is + used to calculate the surface area per unit volume. Can be "speherical" + (default) or "user". For the "user" option the surface area per + unit volume can be passed as a parameter, and is therefore not + necessarily consistent with the particle shape. + """ + + def __init__(self, options=None): + + # Get geometric, electrical and thermal parameters + self.geo = pybamm.GeometricParameters() + self.elec = pybamm.ElectricalParameters() + self.therm = pybamm.ThermalParameters() + + # Set parameters and scales + self._set_dimensional_parameters() + self._set_scales() + self._set_dimensionless_parameters() + + # Set input current + self._set_input_current() + + def _set_dimensional_parameters(self): + "Defines the dimensional parameters" + + # Physical constants + self.R = pybamm.constants.R + self.F = pybamm.constants.F + self.T_ref = self.therm.T_ref + + # Macroscale geometry + self.L_cn = self.geo.L_cn + self.L_n = self.geo.L_n + self.L_s = self.geo.L_s + self.L_p = self.geo.L_p + self.L_cp = self.geo.L_cp + self.L_x = self.geo.L_x + self.L_y = self.geo.L_y + self.L_z = self.geo.L_z + self.L = self.geo.L + self.A_cc = self.geo.A_cc + self.A_cooling = self.geo.A_cooling + self.V_cell = self.geo.V_cell + + # Tab geometry + self.L_tab_n = self.geo.L_tab_n + self.Centre_y_tab_n = self.geo.Centre_y_tab_n + self.Centre_z_tab_n = self.geo.Centre_z_tab_n + self.L_tab_p = self.geo.L_tab_p + self.Centre_y_tab_p = self.geo.Centre_y_tab_p + self.Centre_z_tab_p = self.geo.Centre_z_tab_p + self.A_tab_n = self.geo.A_tab_n + self.A_tab_p = self.geo.A_tab_p + + # Electrical + self.I_typ = self.elec.I_typ + self.Q = self.elec.Q + self.C_rate = self.elec.C_rate + self.n_electrodes_parallel = self.elec.n_electrodes_parallel + self.n_cells = self.elec.n_cells + self.i_typ = self.elec.i_typ + self.voltage_low_cut_dimensional = self.elec.voltage_low_cut_dimensional + self.voltage_high_cut_dimensional = self.elec.voltage_high_cut_dimensional + + # Electrolyte properties + self.c_e_typ = pybamm.Parameter("Typical electrolyte concentration [mol.m-3]") + + # Electrode properties + self.c_n_max = pybamm.Parameter( + "Maximum concentration in negative electrode [mol.m-3]" + ) + self.c_p_max = pybamm.Parameter( + "Maximum concentration in positive electrode [mol.m-3]" + ) + self.sigma_cn_dimensional = pybamm.Parameter( + "Negative current collector conductivity [S.m-1]" + ) + self.sigma_n_dim = pybamm.Parameter("Negative electrode conductivity [S.m-1]") + self.sigma_p_dim = pybamm.Parameter("Positive electrode conductivity [S.m-1]") + self.sigma_cp_dimensional = pybamm.Parameter( + "Positive current collector conductivity [S.m-1]" + ) + + # Microscale geometry + self.a_n_dim = self.geo.a_n_dim + self.a_p_dim = self.geo.a_p_dim + self.a_k_dim = pybamm.Concatenation( + pybamm.FullBroadcast( + self.a_n_dim, ["negative electrode"], "current collector" + ), + pybamm.FullBroadcast(0, ["separator"], "current collector"), + pybamm.FullBroadcast( + self.a_p_dim, ["positive electrode"], "current collector" + ), + ) + self.R_n = self.geo.R_n + self.R_p = self.geo.R_p + self.b_e_n = self.geo.b_e_n + self.b_e_s = self.geo.b_e_s + self.b_e_p = self.geo.b_e_p + self.b_s_n = self.geo.b_s_n + self.b_s_s = self.geo.b_s_s + self.b_s_p = self.geo.b_s_p + + # Electrochemical reactions + self.ne_n = pybamm.Parameter("Negative electrode electrons in reaction") + self.ne_p = pybamm.Parameter("Positive electrode electrons in reaction") + self.C_dl_n_dimensional = pybamm.Parameter( + "Negative electrode double-layer capacity [F.m-2]" + ) + self.C_dl_p_dimensional = pybamm.Parameter( + "Positive electrode double-layer capacity [F.m-2]" + ) + + # SEI parameters + self.V_bar_inner_dimensional = pybamm.Parameter( + "Inner SEI partial molar volume [m3.mol-1]" + ) + self.V_bar_outer_dimensional = pybamm.Parameter( + "Outer SEI partial molar volume [m3.mol-1]" + ) + + self.m_sei_dimensional = pybamm.Parameter( + "SEI reaction exchange current density [A.m-2]" + ) + + self.R_sei_dimensional = pybamm.Parameter("SEI resistivity [Ohm.m]") + self.D_sol_dimensional = pybamm.Parameter( + "Outer SEI solvent diffusivity [m2.s-1]" + ) + self.c_sol_dimensional = pybamm.Parameter( + "Bulk solvent concentration [mol.m-3]" + ) + self.m_ratio = pybamm.Parameter( + "Ratio of inner and outer SEI exchange current densities" + ) + self.U_inner_dimensional = pybamm.Parameter( + "Inner SEI open-circuit potential [V]" + ) + self.U_outer_dimensional = pybamm.Parameter( + "Outer SEI open-circuit potential [V]" + ) + self.kappa_inner_dimensional = pybamm.Parameter( + "Inner SEI electron conductivity [S.m-1]" + ) + self.D_li_dimensional = pybamm.Parameter( + "Inner SEI lithium interstitial diffusivity [m2.s-1]" + ) + self.c_li_0_dimensional = pybamm.Parameter( + "Lithium interstitial reference concentration [mol.m-3]" + ) + self.L_inner_0_dim = pybamm.Parameter("Initial inner SEI thickness [m]") + self.L_outer_0_dim = pybamm.Parameter("Initial outer SEI thickness [m]") + self.L_sei_0_dim = self.L_inner_0_dim + self.L_outer_0_dim + + # EC reaction + self.c_ec_0_dim = pybamm.Parameter( + "EC initial concentration in electrolyte [mol.m-3]" + ) + self.D_ec_dim = pybamm.Parameter("EC diffusivity [m2.s-1]") + self.k_sei_dim = pybamm.Parameter("SEI kinetic rate constant [m.s-1]") + self.U_sei_dim = pybamm.Parameter("SEI open-circuit potential [V]") + + # Initial conditions + # Note: the initial concentration in the electrodes can be set as a function + # of through-cell position, so is defined later as a function + self.c_e_init_dimensional = pybamm.Parameter( + "Initial concentration in electrolyte [mol.m-3]" + ) + + def D_e_dimensional(self, c_e, T): + "Dimensional diffusivity in electrolyte" + inputs = {"Electrolyte concentration [mol.m-3]": c_e, "Temperature [K]": T} + return pybamm.FunctionParameter("Electrolyte diffusivity [m2.s-1]", inputs) + + def kappa_e_dimensional(self, c_e, T): + "Dimensional electrolyte conductivity" + inputs = {"Electrolyte concentration [mol.m-3]": c_e, "Temperature [K]": T} + return pybamm.FunctionParameter("Electrolyte conductivity [S.m-1]", inputs) + + def D_n_dimensional(self, sto, T): + """Dimensional diffusivity in negative particle. Note this is defined as a + function of stochiometry""" + inputs = {"Negative particle stoichiometry": sto, "Temperature [K]": T} + return pybamm.FunctionParameter( + "Negative electrode diffusivity [m2.s-1]", inputs + ) + + def D_p_dimensional(self, sto, T): + """Dimensional diffusivity in positive particle. Note this is defined as a + function of stochiometry""" + inputs = {"Positive particle stoichiometry": sto, "Temperature [K]": T} + return pybamm.FunctionParameter( + "Positive electrode diffusivity [m2.s-1]", inputs + ) + + def j0_n_dimensional(self, c_e, c_s_surf, T): + "Dimensional negative exchange-current density [A.m-2]" + inputs = { + "Electrolyte concentration [mol.m-3]": c_e, + "Negative particle surface concentration [mol.m-3]": c_s_surf, + "Temperature [K]": T, + } + return pybamm.FunctionParameter( + "Negative electrode exchange-current density [A.m-2]", inputs + ) + + def j0_p_dimensional(self, c_e, c_s_surf, T): + "Dimensional negative exchange-current density [A.m-2]" + inputs = { + "Electrolyte concentration [mol.m-3]": c_e, + "Positive particle surface concentration [mol.m-3]": c_s_surf, + "Temperature [K]": T, + } + return pybamm.FunctionParameter( + "Positive electrode exchange-current density [A.m-2]", inputs + ) + + def U_n_dimensional(self, sto, T): + "Dimensional open-circuit potential in the negative electrode [V]" + inputs = {"Negative particle stoichiometry": sto} + u_ref = pybamm.FunctionParameter("Negative electrode OCP [V]", inputs) + return u_ref + (T - self.T_ref) * self.dUdT_n_dimensional(sto) + + def U_p_dimensional(self, sto, T): + "Dimensional open-circuit potential in the positive electrode [V]" + inputs = {"Positive particle stoichiometry": sto} + u_ref = pybamm.FunctionParameter("Positive electrode OCP [V]", inputs) + return u_ref + (T - self.T_ref) * self.dUdT_p_dimensional(sto) + + def dUdT_n_dimensional(self, sto): + """ + Dimensional entropic change of the negative electrode open-circuit + potential [V.K-1] + """ + inputs = { + "Negative particle stoichiometry": sto, + "Max negative particle concentration [mol.m-3]": self.c_n_max, + } + return pybamm.FunctionParameter( + "Negative electrode OCP entropic change [V.K-1]", inputs + ) + + def dUdT_p_dimensional(self, sto): + """ + Dimensional entropic change of the positive electrode open-circuit + potential [V.K-1] + """ + inputs = { + "Positive particle stoichiometry": sto, + "Max positive particle concentration [mol.m-3]": self.c_p_max, + } + return pybamm.FunctionParameter( + "Positive electrode OCP entropic change [V.K-1]", inputs + ) + + def c_n_init_dimensional(self, x): + "Initial concentration as a function of dimensionless position x" + inputs = {"Dimensionless through-cell position (x_n)": x} + return pybamm.FunctionParameter( + "Initial concentration in negative electrode [mol.m-3]", inputs + ) + + def c_p_init_dimensional(self, x): + "Initial concentration as a function of dimensionless position x" + inputs = {"Dimensionless through-cell position (x_p)": x} + return pybamm.FunctionParameter( + "Initial concentration in positive electrode [mol.m-3]", inputs + ) + + def _set_scales(self): + "Define the scales used in the non-dimensionalisation scheme" + + # Concentration + self.electrolyte_concentration_scale = self.c_e_typ + self.negative_particle_concentration_scale = self.c_n_max + self.positive_particle_concentration_scale = self.c_n_max + + # Electrical + self.potential_scale = self.R * self.T_ref / self.F + self.current_scale = self.i_typ + self.j_scale_n = self.i_typ / (self.a_n_dim * self.L_x) + self.j_scale_p = self.i_typ / (self.a_p_dim * self.L_x) + + # Reference OCP based on initial concentration at + # current collector/electrode interface + sto_n_init = self.c_n_init_dimensional(0) / self.c_n_max + self.U_n_ref = self.U_n_dimensional(sto_n_init, self.T_ref) + + # Reference OCP based on initial concentration at + # current collector/electrode interface + sto_p_init = self.c_p_init_dimensional(1) / self.c_p_max + self.U_p_ref = self.U_p_dimensional(sto_p_init, self.T_ref) + + # Reference exchange-current density + self.j0_n_ref_dimensional = ( + self.j0_n_dimensional(self.c_e_typ, self.c_n_max / 2, self.T_ref) * 2 + ) + self.j0_p_ref_dimensional = ( + self.j0_p_dimensional(self.c_e_typ, self.c_p_max / 2, self.T_ref) * 2 + ) + + # Thermal + self.Delta_T = self.therm.Delta_T + + # Velocity scale + self.velocity_scale = pybamm.Scalar(1) + + # Discharge timescale + self.tau_discharge = self.F * self.c_n_max * self.L_x / self.i_typ + + # Reaction timescales + self.tau_r_n = ( + self.F * self.c_n_max / (self.j0_n_ref_dimensional * self.a_n_dim) + ) + self.tau_r_p = ( + self.F * self.c_p_max / (self.j0_p_ref_dimensional * self.a_p_dim) + ) + + # Electrolyte diffusion timescale + self.D_e_typ = self.D_e_dimensional(self.c_e_typ, self.T_ref) + self.tau_diffusion_e = self.L_x ** 2 / self.D_e_typ + + # Particle diffusion timescales + self.tau_diffusion_n = self.R_n ** 2 / self.D_n_dimensional( + pybamm.Scalar(1), self.T_ref + ) + self.tau_diffusion_p = self.R_p ** 2 / self.D_p_dimensional( + pybamm.Scalar(1), self.T_ref + ) + + # Thermal diffusion timescale + self.tau_th_yz = self.therm.tau_th_yz + + # Choose discharge timescale + self.timescale = self.tau_discharge + + def _set_dimensionless_parameters(self): + "Defines the dimensionless parameters" + + # Timescale ratios + self.C_n = self.tau_diffusion_n / self.tau_discharge + self.C_p = self.tau_diffusion_p / self.tau_discharge + self.C_e = self.tau_diffusion_e / self.tau_discharge + self.C_r_n = self.tau_r_n / self.tau_discharge + self.C_r_p = self.tau_r_p / self.tau_discharge + self.C_th = self.tau_th_yz / self.tau_discharge + + # Concentration ratios + self.gamma_e = self.c_e_typ / self.c_n_max + self.gamma_p = self.c_p_max / self.c_n_max + + # Macroscale Geometry + self.l_cn = self.geo.l_cn + self.l_n = self.geo.l_n + self.l_s = self.geo.l_s + self.l_p = self.geo.l_p + self.l_cp = self.geo.l_cp + self.l_x = self.geo.l_x + self.l_y = self.geo.l_y + self.l_z = self.geo.l_z + self.a_cc = self.geo.a_cc + self.a_cooling = self.geo.a_cooling + self.v_cell = self.geo.v_cell + self.l = self.geo.l + self.delta = self.geo.delta + + # Tab geometry + self.l_tab_n = self.geo.l_tab_n + self.centre_y_tab_n = self.geo.centre_y_tab_n + self.centre_z_tab_n = self.geo.centre_z_tab_n + self.l_tab_p = self.geo.l_tab_p + self.centre_y_tab_p = self.geo.centre_y_tab_p + self.centre_z_tab_p = self.geo.centre_z_tab_p + + # Microscale geometry + inputs = {"Through-cell distance (x_n) [m]": pybamm.standard_spatial_vars.x_n} + self.epsilon_n = pybamm.FunctionParameter("Negative electrode porosity", inputs) + + inputs = {"Through-cell distance (x_s) [m]": pybamm.standard_spatial_vars.x_s} + self.epsilon_s = pybamm.FunctionParameter("Separator porosity", inputs) + + inputs = {"Through-cell distance (x_p) [m]": pybamm.standard_spatial_vars.x_p} + self.epsilon_p = pybamm.FunctionParameter("Positive electrode porosity", inputs) + + self.epsilon = pybamm.Concatenation( + self.epsilon_n, self.epsilon_s, self.epsilon_p + ) + self.epsilon_n_init = pybamm.Parameter("Negative electrode porosity") + self.epsilon_s_init = pybamm.Parameter("Separator porosity") + self.epsilon_p_init = pybamm.Parameter("Positive electrode porosity") + self.epsilon_init = pybamm.Concatenation( + self.epsilon_n, self.epsilon_s, self.epsilon_p + ) + self.epsilon_s_n = pybamm.Parameter( + "Negative electrode active material volume fraction" + ) + self.epsilon_s_p = pybamm.Parameter( + "Positive electrode active material volume fraction" + ) + self.epsilon_inactive_n = 1 - self.epsilon_n - self.epsilon_s_n + self.epsilon_inactive_s = 1 - self.epsilon_s + self.epsilon_inactive_p = 1 - self.epsilon_p - self.epsilon_s_p + self.a_n = self.a_n_dim * self.R_n + self.a_p = self.a_p_dim * self.R_p + + # Electrode Properties + self.sigma_cn = ( + self.sigma_cn_dimensional * self.potential_scale / self.i_typ / self.L_x + ) + self.sigma_n = self.sigma_n_dim * self.potential_scale / self.i_typ / self.L_x + self.sigma_p = self.sigma_p_dim * self.potential_scale / self.i_typ / self.L_x + self.sigma_cp = ( + self.sigma_cp_dimensional * self.potential_scale / self.i_typ / self.L_x + ) + self.sigma_cn_prime = self.sigma_cn * self.delta ** 2 + self.sigma_n_prime = self.sigma_n * self.delta + self.sigma_p_prime = self.sigma_p * self.delta + self.sigma_cp_prime = self.sigma_cp * self.delta ** 2 + self.sigma_cn_dbl_prime = self.sigma_cn_prime * self.delta + self.sigma_cp_dbl_prime = self.sigma_cp_prime * self.delta + + # Electrolyte Properties + self.beta_surf = pybamm.Scalar(0) + self.beta_surf_n = pybamm.Scalar(0) + self.beta_surf_p = pybamm.Scalar(0) + + # Electrochemical Reactions + self.C_dl_n = ( + self.C_dl_n_dimensional + * self.potential_scale + / self.j_scale_n + / self.tau_discharge + ) + self.C_dl_p = ( + self.C_dl_p_dimensional + * self.potential_scale + / self.j_scale_p + / self.tau_discharge + ) + + # Electrical + self.voltage_low_cut = ( + self.voltage_low_cut_dimensional - (self.U_p_ref - self.U_n_ref) + ) / self.potential_scale + self.voltage_high_cut = ( + self.voltage_high_cut_dimensional - (self.U_p_ref - self.U_n_ref) + ) / self.potential_scale + + # Thermal + self.rho_cn = self.therm.rho_cn + self.rho_n = self.therm.rho_n + self.rho_s = self.therm.rho_s + self.rho_p = self.therm.rho_p + self.rho_cp = self.therm.rho_cp + self.rho_k = self.therm.rho_k + self.rho = ( + self.rho_cn * self.l_cn + + self.rho_n * self.l_n + + self.rho_s * self.l_s + + self.rho_p * self.l_p + + self.rho_cp * self.l_cp + ) / self.l # effective volumetric heat capacity + + self.lambda_cn = self.therm.lambda_cn + self.lambda_n = self.therm.lambda_n + self.lambda_s = self.therm.lambda_s + self.lambda_p = self.therm.lambda_p + self.lambda_cp = self.therm.lambda_cp + self.lambda_k = self.therm.lambda_k + + self.Theta = self.therm.Theta + + self.h_edge = self.therm.h_edge + self.h_tab_n = self.therm.h_tab_n + self.h_tab_p = self.therm.h_tab_p + self.h_cn = self.therm.h_cn + self.h_cp = self.therm.h_cp + self.h_total = self.therm.h_total + + self.B = ( + self.i_typ + * self.R + * self.T_ref + * self.tau_th_yz + / (self.therm.rho_eff_dim * self.F * self.Delta_T * self.L_x) + ) + + self.T_amb_dim = self.therm.T_amb_dim + self.T_amb = self.therm.T_amb + + # SEI parameters + self.C_sei_reaction_n = (self.j_scale_n / self.m_sei_dimensional) * pybamm.exp( + -(self.F * self.U_n_ref / (2 * self.R * self.T_ref)) + ) + self.C_sei_reaction_p = (self.j_scale_p / self.m_sei_dimensional) * pybamm.exp( + -(self.F * self.U_n_ref / (2 * self.R * self.T_ref)) + ) + + self.C_sei_solvent_n = ( + self.j_scale_n + * self.L_sei_0_dim + / (self.c_sol_dimensional * self.F * self.D_sol_dimensional) + ) + self.C_sei_solvent_p = ( + self.j_scale_p + * self.L_sei_0_dim + / (self.c_sol_dimensional * self.F * self.D_sol_dimensional) + ) + + self.C_sei_electron_n = ( + self.j_scale_n + * self.F + * self.L_sei_0_dim + / (self.kappa_inner_dimensional * self.R * self.T_ref) + ) + self.C_sei_electron_p = ( + self.j_scale_p + * self.F + * self.L_sei_0_dim + / (self.kappa_inner_dimensional * self.R * self.T_ref) + ) + + self.C_sei_inter_n = ( + self.j_scale_n + * self.L_sei_0_dim + / (self.D_li_dimensional * self.c_li_0_dimensional * self.F) + ) + self.C_sei_inter_p = ( + self.j_scale_p + * self.L_sei_0_dim + / (self.D_li_dimensional * self.c_li_0_dimensional * self.F) + ) + + self.U_inner_electron = self.F * self.U_inner_dimensional / self.R / self.T_ref + + self.R_sei_n = ( + self.F + * self.j_scale_n + * self.R_sei_dimensional + * self.L_sei_0_dim + / self.R + / self.T_ref + ) + self.R_sei_p = ( + self.F + * self.j_scale_p + * self.R_sei_dimensional + * self.L_sei_0_dim + / self.R + / self.T_ref + ) + + self.v_bar = self.V_bar_outer_dimensional / self.V_bar_inner_dimensional + + self.L_inner_0 = self.L_inner_0_dim / self.L_sei_0_dim + self.L_outer_0 = self.L_outer_0_dim / self.L_sei_0_dim + + # ratio of SEI reaction scale to intercalation reaction + self.Gamma_SEI_n = ( + self.V_bar_inner_dimensional * self.j_scale_n * self.tau_discharge + ) / (self.F * self.L_sei_0_dim) + self.Gamma_SEI_p = ( + self.V_bar_inner_dimensional * self.j_scale_p * self.tau_discharge + ) / (self.F * self.L_sei_0_dim) + + # EC reaction + self.C_ec_n = ( + self.L_sei_0_dim + * self.j_scale_n + / (self.F * self.c_ec_0_dim * self.D_ec_dim) + ) + self.C_sei_ec_n = ( + self.F + * self.k_sei_dim + * self.c_ec_0_dim + / self.j_scale_n + * ( + pybamm.exp( + -( + self.F + * (self.U_n_ref - self.U_sei_dim) + / (2 * self.R * self.T_ref) + ) + ) + ) + ) + self.beta_sei_n = self.a_n_dim * self.L_sei_0_dim * self.Gamma_SEI_n + + # Initial conditions + self.T_init = self.therm.T_init + self.c_e_init = self.c_e_init_dimensional / self.c_e_typ + + def chi(self, c_e): + """ + Thermodynamic factor: + (1-2*t_plus) is for Nernst-Planck, + 2*(1-t_plus) for Stefan-Maxwell, + see Bizeray et al (2016) "Resolving a discrepancy ...". + """ + return (2 * (1 - self.t_plus(c_e))) * (self.one_plus_dlnf_dlnc(c_e)) + + def t_plus(self, c_e): + "Dimensionless transference number (i.e. c_e is dimensionless)" + inputs = {"Electrolyte concentration [mol.m-3]": c_e * self.c_e_typ} + return pybamm.FunctionParameter("Cation transference number", inputs) + + def one_plus_dlnf_dlnc(self, c_e): + inputs = {"Electrolyte concentration [mol.m-3]": c_e * self.c_e_typ} + return pybamm.FunctionParameter("1 + dlnf/dlnc", inputs) + + def D_e(self, c_e, T): + "Dimensionless electrolyte diffusivity" + c_e_dimensional = c_e * self.c_e_typ + T_dim = self.Delta_T * T + self.T_ref + return self.D_e_dimensional(c_e_dimensional, T_dim) / self.D_e_typ + + def kappa_e(self, c_e, T): + "Dimensionless electrolyte conductivity" + c_e_dimensional = c_e * self.c_e_typ + kappa_scale = self.F ** 2 * self.D_e_typ * self.c_e_typ / (self.R * self.T_ref) + T_dim = self.Delta_T * T + self.T_ref + return self.kappa_e_dimensional(c_e_dimensional, T_dim) / kappa_scale + + def D_n(self, c_s_n, T): + "Dimensionless negative particle diffusivity" + sto = c_s_n + T_dim = self.Delta_T * T + self.T_ref + return self.D_n_dimensional(sto, T_dim) / self.D_n_dimensional( + pybamm.Scalar(1), self.T_ref + ) + + def D_p(self, c_s_p, T): + "Dimensionless positive particle diffusivity" + sto = c_s_p + T_dim = self.Delta_T * T + self.T_ref + return self.D_p_dimensional(sto, T_dim) / self.D_p_dimensional( + pybamm.Scalar(1), self.T_ref + ) + + def j0_n(self, c_e, c_s_surf, T): + "Dimensionless negative exchange-current density" + c_e_dim = c_e * self.c_e_typ + c_s_surf_dim = c_s_surf * self.c_n_max + T_dim = self.Delta_T * T + self.T_ref + + return ( + self.j0_n_dimensional(c_e_dim, c_s_surf_dim, T_dim) + / self.j0_n_ref_dimensional + ) + + def j0_p(self, c_e, c_s_surf, T): + "Dimensionless positive exchange-current density" + c_e_dim = c_e * self.c_e_typ + c_s_surf_dim = c_s_surf * self.c_p_max + T_dim = self.Delta_T * T + self.T_ref + + return ( + self.j0_p_dimensional(c_e_dim, c_s_surf_dim, T_dim) + / self.j0_p_ref_dimensional + ) + + def U_n(self, c_s_n, T): + "Dimensionless open-circuit potential in the negative electrode" + sto = c_s_n + T_dim = self.Delta_T * T + self.T_ref + return (self.U_n_dimensional(sto, T_dim) - self.U_n_ref) / self.potential_scale + + def U_p(self, c_s_p, T): + "Dimensionless open-circuit potential in the positive electrode" + sto = c_s_p + T_dim = self.Delta_T * T + self.T_ref + return (self.U_p_dimensional(sto, T_dim) - self.U_p_ref) / self.potential_scale + + def dUdT_n(self, c_s_n): + "Dimensionless entropic change in negative open-circuit potential" + sto = c_s_n + return self.dUdT_n_dimensional(sto) * self.Delta_T / self.potential_scale + + def dUdT_p(self, c_s_p): + "Dimensionless entropic change in positive open-circuit potential" + sto = c_s_p + return self.dUdT_p_dimensional(sto) * self.Delta_T / self.potential_scale + + def c_n_init(self, x): + "Dimensionless initial concentration as a function of dimensionless position x" + return self.c_n_init_dimensional(x) / self.c_n_max + + def c_p_init(self, x): + "Dimensionless initial concentration as a function of dimensionless position x" + return self.c_p_init_dimensional(x) / self.c_p_max + + def _set_input_current(self): + "Set the input current" + + self.dimensional_current_with_time = pybamm.FunctionParameter( + "Current function [A]", {"Time [s]": pybamm.t * self.timescale} + ) + self.dimensional_current_density_with_time = ( + self.dimensional_current_with_time + / (self.n_electrodes_parallel * self.geo.A_cc) + ) + self.current_with_time = ( + self.dimensional_current_with_time + / self.I_typ + * pybamm.Function(np.sign, self.I_typ) + ) diff --git a/pybamm/parameters/standard_parameters_lead_acid.py b/pybamm/parameters/standard_parameters_lead_acid.py deleted file mode 100644 index 2f7b4739f0..0000000000 --- a/pybamm/parameters/standard_parameters_lead_acid.py +++ /dev/null @@ -1,579 +0,0 @@ -# -# Standard parameters for lead-acid battery models -# -""" -Standard Parameters for lead-acid battery models -""" -import pybamm -import numpy as np - - -# -------------------------------------------------------------------------------------- -"File Layout:" -# 1. Dimensional Parameters -# 2. Dimensional Functions -# 3. Scalings -# 4. Dimensionless Parameters -# 5. Dimensionless Functions -# 6. Input current - -# -------------------------------------------------------------------------------------- -"1. Dimensional Parameters" -# Physical constants -R = pybamm.constants.R -F = pybamm.constants.F -T_ref = pybamm.Parameter("Reference temperature [K]") - -# Macroscale geometry -L_n = pybamm.geometric_parameters.L_n -L_s = pybamm.geometric_parameters.L_s -L_p = pybamm.geometric_parameters.L_p -L_x = pybamm.geometric_parameters.L_x -L_y = pybamm.geometric_parameters.L_y -L_z = pybamm.geometric_parameters.L_z -A_cc = pybamm.geometric_parameters.A_cc -A_cooling = pybamm.geometric_parameters.A_cooling -V_cell = pybamm.geometric_parameters.V_cell -W = L_y -H = L_z -A_cs = A_cc -delta = L_x / H - -# Electrical -I_typ = pybamm.electrical_parameters.I_typ -Q = pybamm.electrical_parameters.Q -C_rate = pybamm.electrical_parameters.C_rate -n_electrodes_parallel = pybamm.electrical_parameters.n_electrodes_parallel -i_typ = pybamm.electrical_parameters.i_typ -voltage_low_cut_dimensional = pybamm.electrical_parameters.voltage_low_cut_dimensional -voltage_high_cut_dimensional = pybamm.electrical_parameters.voltage_high_cut_dimensional - -# Electrolyte properties -c_e_typ = pybamm.Parameter("Typical electrolyte concentration [mol.m-3]") -V_w = pybamm.Parameter("Partial molar volume of water [m3.mol-1]") -V_plus = pybamm.Parameter("Partial molar volume of cations [m3.mol-1]") -V_minus = pybamm.Parameter("Partial molar volume of anions [m3.mol-1]") -V_e = V_minus + V_plus # Partial molar volume of electrolyte [m3.mol-1] -nu_plus = pybamm.Parameter("Cation stoichiometry") -nu_minus = pybamm.Parameter("Anion stoichiometry") -nu = nu_plus + nu_minus - -# Other species properties -c_ox_init_dim = pybamm.Parameter("Initial oxygen concentration [mol.m-3]") -c_ox_typ = c_e_typ # pybamm.Parameter("Typical oxygen concentration [mol.m-3]") - -# Microstructure -a_n_dim = pybamm.geometric_parameters.a_n_dim -a_p_dim = pybamm.geometric_parameters.a_p_dim -b_e_n = pybamm.geometric_parameters.b_e_n -b_e_s = pybamm.geometric_parameters.b_e_s -b_e_p = pybamm.geometric_parameters.b_e_p -b_s_n = pybamm.geometric_parameters.b_s_n -b_s_s = pybamm.geometric_parameters.b_s_s -b_s_p = pybamm.geometric_parameters.b_s_p -xi_n = pybamm.Parameter("Negative electrode morphological parameter") -xi_p = pybamm.Parameter("Positive electrode morphological parameter") -# no binder -epsilon_inactive_n = pybamm.Scalar(0) -epsilon_inactive_s = pybamm.Scalar(0) -epsilon_inactive_p = pybamm.Scalar(0) - -# Electrode properties -V_Pb = pybamm.Parameter("Molar volume of lead [m3.mol-1]") -V_PbO2 = pybamm.Parameter("Molar volume of lead-dioxide [m3.mol-1]") -V_PbSO4 = pybamm.Parameter("Molar volume of lead sulfate [m3.mol-1]") -DeltaVsurf_n = V_Pb - V_PbSO4 # Net Molar Volume consumed in neg electrode [m3.mol-1] -DeltaVsurf_p = V_PbSO4 - V_PbO2 # Net Molar Volume consumed in pos electrode [m3.mol-1] -d_n = pybamm.Parameter("Negative electrode pore size [m]") -d_p = pybamm.Parameter("Positive electrode pore size [m]") -eps_n_max = pybamm.Parameter("Maximum porosity of negative electrode") -eps_s_max = pybamm.Parameter("Maximum porosity of separator") -eps_p_max = pybamm.Parameter("Maximum porosity of positive electrode") -Q_n_max_dimensional = pybamm.Parameter("Negative electrode volumetric capacity [C.m-3]") -Q_p_max_dimensional = pybamm.Parameter("Positive electrode volumetric capacity [C.m-3]") -sigma_n_dim = pybamm.Parameter("Negative electrode conductivity [S.m-1]") -sigma_p_dim = pybamm.Parameter("Positive electrode conductivity [S.m-1]") -# In lead-acid the current collector and electrodes are the same (same conductivity) -# but we correct here for Bruggeman -sigma_cn_dimensional = sigma_n_dim * (1 - eps_n_max) ** b_s_n -sigma_cp_dimensional = sigma_p_dim * (1 - eps_p_max) ** b_s_p - -# Electrochemical reactions -# Main -s_plus_n_S_dim = pybamm.Parameter("Negative electrode cation signed stoichiometry") -s_plus_p_S_dim = pybamm.Parameter("Positive electrode cation signed stoichiometry") -ne_n_S = pybamm.Parameter("Negative electrode electrons in reaction") -ne_p_S = pybamm.Parameter("Positive electrode electrons in reaction") -C_dl_n_dimensional = pybamm.Parameter( - "Negative electrode double-layer capacity [F.m-2]" -) -C_dl_p_dimensional = pybamm.Parameter( - "Positive electrode double-layer capacity [F.m-2]" -) -# Oxygen -s_plus_Ox_dim = pybamm.Parameter("Signed stoichiometry of cations (oxygen reaction)") -s_w_Ox_dim = pybamm.Parameter("Signed stoichiometry of water (oxygen reaction)") -s_ox_Ox_dim = pybamm.Parameter("Signed stoichiometry of oxygen (oxygen reaction)") -ne_Ox = pybamm.Parameter("Electrons in oxygen reaction") -U_Ox_dim = pybamm.Parameter("Oxygen reference OCP vs SHE [V]") -# Hydrogen -s_plus_Hy_dim = pybamm.Parameter("Signed stoichiometry of cations (hydrogen reaction)") -s_hy_Hy_dim = pybamm.Parameter("Signed stoichiometry of hydrogen (hydrogen reaction)") -ne_Hy = pybamm.Parameter("Electrons in hydrogen reaction") -U_Hy_dim = pybamm.Parameter("Hydrogen reference OCP vs SHE [V]") - - -# Electrolyte properties -M_w = pybamm.Parameter("Molar mass of water [kg.mol-1]") -M_plus = pybamm.Parameter("Molar mass of cations [kg.mol-1]") -M_minus = pybamm.Parameter("Molar mass of anions [kg.mol-1]") -M_e = M_minus + M_plus # Molar mass of electrolyte [kg.mol-1] - -DeltaVliq_n = ( - V_minus - V_plus -) # Net Molar Volume consumed in electrolyte (neg) [m3.mol-1] -DeltaVliq_p = ( - 2 * V_w - V_minus - 3 * V_plus -) # Net Molar Volume consumed in electrolyte (neg) [m3.mol-1] - -# Other species properties -D_ox_dimensional = pybamm.Parameter("Oxygen diffusivity [m2.s-1]") -D_hy_dimensional = pybamm.Parameter("Hydrogen diffusivity [m2.s-1]") -V_ox = pybamm.Parameter("Partial molar volume of oxygen molecules [m3.mol-1]") -V_hy = pybamm.Parameter("Partial molar volume of hydrogen molecules [m3.mol-1]") -M_ox = pybamm.Parameter("Molar mass of oxygen molecules [kg.mol-1]") -M_hy = pybamm.Parameter("Molar mass of hydrogen molecules [kg.mol-1]") - -# Electrode properties -V_Pb = pybamm.Parameter("Molar volume of lead [m3.mol-1]") -V_PbO2 = pybamm.Parameter("Molar volume of lead-dioxide [m3.mol-1]") -V_PbSO4 = pybamm.Parameter("Molar volume of lead sulfate [m3.mol-1]") -DeltaVsurf_n = V_Pb - V_PbSO4 # Net Molar Volume consumed in neg electrode [m3.mol-1] -DeltaVsurf_p = V_PbSO4 - V_PbO2 # Net Molar Volume consumed in pos electrode [m3.mol-1] -d_n = pybamm.Parameter("Negative electrode pore size [m]") -d_p = pybamm.Parameter("Positive electrode pore size [m]") -eps_n_max = pybamm.Parameter("Maximum porosity of negative electrode") -eps_s_max = pybamm.Parameter("Maximum porosity of separator") -eps_p_max = pybamm.Parameter("Maximum porosity of positive electrode") -Q_n_max_dimensional = pybamm.Parameter("Negative electrode volumetric capacity [C.m-3]") -Q_p_max_dimensional = pybamm.Parameter("Positive electrode volumetric capacity [C.m-3]") - - -# thermal -Delta_T = pybamm.thermal_parameters.Delta_T - -# SEI parameters (for compatibility) -R_sei_dimensional = pybamm.Scalar(0) -beta_sei_n = pybamm.Scalar(0) - -# -------------------------------------------------------------------------------------- -"2. Dimensional Functions" - - -def t_plus(c_e): - "Dimensionless transference number (i.e. c_e is dimensionless)" - inputs = {"Electrolyte concentration [mol.m-3]": c_e * c_e_typ} - return pybamm.FunctionParameter("Cation transference number", inputs) - - -def D_e_dimensional(c_e, T): - "Dimensional diffusivity in electrolyte" - inputs = {"Electrolyte concentration [mol.m-3]": c_e} - return pybamm.FunctionParameter("Electrolyte diffusivity [m2.s-1]", inputs) - - -def kappa_e_dimensional(c_e, T): - "Dimensional electrolyte conductivity" - inputs = {"Electrolyte concentration [mol.m-3]": c_e} - return pybamm.FunctionParameter("Electrolyte conductivity [S.m-1]", inputs) - - -def chi_dimensional(c_e): - inputs = {"Electrolyte concentration [mol.m-3]": c_e} - return pybamm.FunctionParameter("Darken thermodynamic factor", inputs) - - -def c_T(c_e, c_ox=0, c_hy=0): - """ - Total liquid molarity [mol.m-3], from thermodynamics. c_k in [mol.m-3]. - """ - return (1 + (2 * V_w - V_e) * c_e + (V_w - V_ox) * c_ox + (V_w - V_hy) * c_hy) / V_w - - -def rho_dimensional(c_e, c_ox=0, c_hy=0): - """ - Dimensional density of electrolyte [kg.m-3], from thermodynamics. c_k in [mol.m-3]. - """ - return ( - M_w / V_w - + (M_e - V_e * M_w / V_w) * c_e - + (M_ox - V_ox * M_w / V_w) * c_ox - + (M_hy - V_hy * M_w / V_w) * c_hy - ) - - -def m_dimensional(c_e): - """ - Dimensional electrolyte molar mass [mol.kg-1], from thermodynamics. - c_e in [mol.m-3]. - """ - return c_e * V_w / ((1 - c_e * V_e) * M_w) - - -def mu_dimensional(c_e): - """ - Dimensional viscosity of electrolyte [kg.m-1.s-1]. - """ - inputs = {"Electrolyte concentration [mol.m-3]": c_e} - return pybamm.FunctionParameter("Electrolyte viscosity [kg.m-1.s-1]", inputs) - - -def U_n_dimensional(c_e, T): - "Dimensional open-circuit voltage in the negative electrode [V]" - inputs = {"Electrolyte molar mass [mol.kg-1]": m_dimensional(c_e)} - return pybamm.FunctionParameter( - "Negative electrode open-circuit potential [V]", inputs - ) - - -def U_p_dimensional(c_e, T): - "Dimensional open-circuit voltage in the positive electrode [V]" - inputs = {"Electrolyte molar mass [mol.kg-1]": m_dimensional(c_e)} - return pybamm.FunctionParameter( - "Positive electrode open-circuit potential [V]", inputs - ) - - -def j0_n_dimensional(c_e, T): - "Dimensional negative electrode exchange-current density [A.m-2]" - inputs = {"Electrolyte concentration [mol.m-3]": c_e, "Temperature [K]": T} - return pybamm.FunctionParameter( - "Negative electrode exchange-current density [A.m-2]", inputs - ) - - -def j0_p_dimensional(c_e, T): - "Dimensional positive electrode exchange-current density [A.m-2]" - inputs = {"Electrolyte concentration [mol.m-3]": c_e, "Temperature [K]": T} - return pybamm.FunctionParameter( - "Positive electrode exchange-current density [A.m-2]", inputs - ) - - -def j0_p_Ox_dimensional(c_e, T): - "Dimensional oxygen positive electrode exchange-current density [A.m-2]" - inputs = {"Electrolyte concentration [mol.m-3]": c_e, "Temperature [K]": T} - return pybamm.FunctionParameter( - "Positive electrode oxygen exchange-current density [A.m-2]", inputs - ) - - -D_e_typ = D_e_dimensional(c_e_typ, T_ref) -rho_typ = rho_dimensional(c_e_typ) -mu_typ = mu_dimensional(c_e_typ) - -inputs = {"Electrolyte concentration [mol.m-3]": pybamm.Scalar(1)} -U_n_ref = pybamm.FunctionParameter( - "Negative electrode open-circuit potential [V]", inputs -) -inputs = {"Electrolyte concentration [mol.m-3]": pybamm.Scalar(1)} -U_p_ref = pybamm.FunctionParameter( - "Positive electrode open-circuit potential [V]", inputs -) - - -# -------------------------------------------------------------------------------------- -"3. Scales" - -# concentrations -electrolyte_concentration_scale = c_e_typ - -# electrical -potential_scale = R * T_ref / F -current_scale = i_typ -j_scale_n = i_typ / (a_n_dim * L_x) -j_scale_p = i_typ / (a_p_dim * L_x) - -velocity_scale = i_typ / (c_e_typ * F) # Reaction velocity scale - -# Discharge timescale -tau_discharge = F * c_e_typ * L_x / i_typ - -# Electrolyte diffusion timescale -tau_diffusion_e = L_x ** 2 / D_e_typ - -# Thermal diffusion timescale -tau_th_yz = pybamm.thermal_parameters.tau_th_yz - -# Choose discharge timescale -timescale = tau_discharge - -# -------------------------------------------------------------------------------------- -"4. Dimensionless Parameters" -# Timescale ratios -C_th = tau_th_yz / tau_discharge - -# Macroscale Geometry -l_n = pybamm.geometric_parameters.l_n -l_s = pybamm.geometric_parameters.l_s -l_p = pybamm.geometric_parameters.l_p -l_x = pybamm.geometric_parameters.l_x -l_y = pybamm.geometric_parameters.l_y -l_z = pybamm.geometric_parameters.l_z -a_cc = pybamm.geometric_parameters.a_cc -a_cooling = pybamm.geometric_parameters.a_cooling -v_cell = pybamm.geometric_parameters.v_cell -l = pybamm.geometric_parameters.l -delta = pybamm.geometric_parameters.delta -# In lead-acid the current collector and electrodes are the same (same thickness) -l_cn = l_n -l_cp = l_p - -# Tab geometry -l_tab_n = pybamm.geometric_parameters.l_tab_n -centre_y_tab_n = pybamm.geometric_parameters.centre_y_tab_n -centre_z_tab_n = pybamm.geometric_parameters.centre_z_tab_n -l_tab_p = pybamm.geometric_parameters.l_tab_p -centre_y_tab_p = pybamm.geometric_parameters.centre_y_tab_p -centre_z_tab_p = pybamm.geometric_parameters.centre_z_tab_p - -# Diffusive kinematic relationship coefficient -omega_i = c_e_typ * M_e / rho_typ * (t_plus(1) + M_minus / M_e) -# Migrative kinematic relationship coefficient (electrolyte) -omega_c_e = c_e_typ * M_e / rho_typ * (1 - M_w * V_e / V_w * M_e) -C_e = tau_diffusion_e / tau_discharge -# Ratio of viscous pressure scale to osmotic pressure scale (electrolyte) -pi_os_e = mu_typ * velocity_scale * L_x / (d_n ** 2 * R * T_ref * c_e_typ) -# ratio of electrolyte concentration to electrode concentration, undefined -gamma_e = pybamm.Scalar(1) -# Reynolds number -Re = rho_typ * velocity_scale * L_x / mu_typ - -# Other species properties -# Oxygen -curlyD_ox = D_ox_dimensional / D_e_typ -omega_c_ox = c_e_typ * M_ox / rho_typ * (1 - M_w * V_ox / V_w * M_ox) -# Hydrogen -curlyD_hy = D_hy_dimensional / D_e_typ -omega_c_hy = c_e_typ * M_hy / rho_typ * (1 - M_w * V_hy / V_w * M_hy) - -# Electrode Properties -sigma_cn = sigma_cn_dimensional * potential_scale / i_typ / L_x -sigma_n = sigma_n_dim * potential_scale / current_scale / L_x -sigma_p = sigma_p_dim * potential_scale / current_scale / L_x -sigma_cp = sigma_cp_dimensional * potential_scale / i_typ / L_x -sigma_n_prime = sigma_n * delta ** 2 -sigma_p_prime = sigma_p * delta ** 2 -sigma_cn_prime = sigma_cn * delta ** 2 -sigma_cp_prime = sigma_cp * delta ** 2 -delta_pore_n = 1 / (a_n_dim * L_x) -delta_pore_p = 1 / (a_p_dim * L_x) -Q_n_max = Q_n_max_dimensional / (c_e_typ * F) -Q_p_max = Q_p_max_dimensional / (c_e_typ * F) -beta_U_n = 1 / Q_n_max -beta_U_p = -1 / Q_p_max - -# Electrochemical reactions -# Main -s_plus_n_S = s_plus_n_S_dim / ne_n_S -s_plus_p_S = s_plus_p_S_dim / ne_p_S -s_plus_S = pybamm.Concatenation( - pybamm.FullBroadcast(s_plus_n_S, ["negative electrode"], "current collector"), - pybamm.FullBroadcast(0, ["separator"], "current collector"), - pybamm.FullBroadcast(s_plus_p_S, ["positive electrode"], "current collector"), -) -C_dl_n = C_dl_n_dimensional * potential_scale / j_scale_n / tau_discharge -C_dl_p = C_dl_p_dimensional * potential_scale / j_scale_p / tau_discharge -ne_n = ne_n_S -ne_p = ne_p_S -# Oxygen -s_plus_Ox = s_plus_Ox_dim / ne_Ox -s_w_Ox = s_w_Ox_dim / ne_Ox -s_ox_Ox = s_ox_Ox_dim / ne_Ox -# j0_n_Ox_ref = j0_n_Ox_ref_dimensional / j_scale_n -U_n_Ox = (U_Ox_dim - U_n_ref) / potential_scale -U_p_Ox = (U_Ox_dim - U_p_ref) / potential_scale -# Hydrogen -s_plus_Hy = s_plus_Hy_dim / ne_Hy -s_hy_Hy = s_hy_Hy_dim / ne_Hy -# j0_n_Hy_ref = j0_n_Hy_ref_dimensional / j_scale_n -# j0_p_Hy_ref = j0_p_Hy_ref_dimensional / j_scale_p -U_n_Hy = (U_Hy_dim - U_n_ref) / potential_scale -U_p_Hy = (U_Hy_dim - U_p_ref) / potential_scale - -# Electrolyte properties -beta_surf_n = -c_e_typ * DeltaVsurf_n / ne_n_S # Molar volume change (lead) -beta_surf_p = -c_e_typ * DeltaVsurf_p / ne_p_S # Molar volume change (lead dioxide) -beta_surf = pybamm.Concatenation( - pybamm.FullBroadcast(beta_surf_n, ["negative electrode"], "current collector"), - pybamm.FullBroadcast(0, ["separator"], "current collector"), - pybamm.FullBroadcast(beta_surf_p, ["positive electrode"], "current collector"), -) -beta_liq_n = -c_e_typ * DeltaVliq_n / ne_n_S # Molar volume change (electrolyte, neg) -beta_liq_p = -c_e_typ * DeltaVliq_p / ne_p_S # Molar volume change (electrolyte, pos) -beta_n = (beta_surf_n + beta_liq_n) * pybamm.Parameter("Volume change factor") -beta_p = (beta_surf_p + beta_liq_p) * pybamm.Parameter("Volume change factor") -beta = pybamm.Concatenation( - pybamm.FullBroadcast(beta_n, "negative electrode", "current collector"), - pybamm.FullBroadcast(0, "separator", "current collector"), - pybamm.FullBroadcast(beta_p, "positive electrode", "current collector"), -) -beta_Ox = -c_e_typ * (s_plus_Ox * V_plus + s_w_Ox * V_w + s_ox_Ox * V_ox) -beta_Hy = -c_e_typ * (s_plus_Hy * V_plus + s_hy_Hy * V_hy) - -# Electrical -voltage_low_cut = (voltage_low_cut_dimensional - (U_p_ref - U_n_ref)) / potential_scale -voltage_high_cut = ( - voltage_high_cut_dimensional - (U_p_ref - U_n_ref) -) / potential_scale - -# Electrolyte volumetric capacity -Q_e_max = (l_n * eps_n_max + l_s * eps_s_max + l_p * eps_p_max) / ( - s_plus_p_S - s_plus_n_S -) -Q_e_max_dimensional = Q_e_max * c_e_typ * F -capacity = Q_e_max_dimensional * n_electrodes_parallel * A_cs * L_x - -# Thermal -rho_cn = pybamm.thermal_parameters.rho_cn -rho_n = pybamm.thermal_parameters.rho_n -rho_s = pybamm.thermal_parameters.rho_s -rho_p = pybamm.thermal_parameters.rho_p -rho_cp = pybamm.thermal_parameters.rho_cp - -rho_k = pybamm.thermal_parameters.rho_k -# effective volumetric heat capacity -rho = (rho_cn * l_cn + rho_n * l_n + rho_s * l_s + rho_p * l_p + rho_cp * l_cp) / l - -lambda_cn = pybamm.thermal_parameters.lambda_cn -lambda_n = pybamm.thermal_parameters.lambda_n -lambda_s = pybamm.thermal_parameters.lambda_s -lambda_p = pybamm.thermal_parameters.lambda_p -lambda_cp = pybamm.thermal_parameters.lambda_cp - -lambda_k = pybamm.thermal_parameters.lambda_k - -Theta = pybamm.thermal_parameters.Theta - -h_edge = pybamm.thermal_parameters.h_edge -h_tab_n = pybamm.thermal_parameters.h_tab_n -h_tab_p = pybamm.thermal_parameters.h_tab_p -h_cn = pybamm.thermal_parameters.h_cn -h_cp = pybamm.thermal_parameters.h_cp -h_total = pybamm.thermal_parameters.h_total - -B = ( - i_typ - * R - * T_ref - * tau_th_yz - / (pybamm.thermal_parameters.rho_eff_dim * F * Delta_T * L_x) -) - -T_amb_dim = pybamm.thermal_parameters.T_amb_dim -T_amb = pybamm.thermal_parameters.T_amb - -# Initial conditions -T_init = pybamm.thermal_parameters.T_init -q_init = pybamm.Parameter("Initial State of Charge") -c_e_init = q_init -c_ox_init = c_ox_init_dim / c_ox_typ -epsilon_n_init = eps_n_max - beta_surf_n * Q_e_max / l_n * (1 - q_init) -epsilon_s_init = eps_s_max -epsilon_p_init = eps_p_max + beta_surf_p * Q_e_max / l_p * (1 - q_init) -epsilon_init = pybamm.Concatenation( - pybamm.FullBroadcast(epsilon_n_init, ["negative electrode"], "current collector"), - pybamm.FullBroadcast(epsilon_s_init, ["separator"], "current collector"), - pybamm.FullBroadcast(epsilon_p_init, ["positive electrode"], "current collector"), -) -curlyU_n_init = Q_e_max * (1.2 - q_init) / (Q_n_max * l_n) -curlyU_p_init = Q_e_max * (1.2 - q_init) / (Q_p_max * l_p) - - -# hack to make consistent ic with lithium-ion -def c_n_init(x): - return c_e_init - - -def c_p_init(x): - return c_e_init - - -# -------------------------------------------------------------------------------------- -"5. Dimensionless Functions" - - -def D_e(c_e, T): - "Dimensionless electrolyte diffusivity" - c_e_dimensional = c_e * c_e_typ - return D_e_dimensional(c_e_dimensional, T_ref) / D_e_typ - - -def kappa_e(c_e, T): - "Dimensionless electrolyte conductivity" - c_e_dimensional = c_e * c_e_typ - kappa_scale = F ** 2 * D_e_typ * c_e_typ / (R * T_ref) - return kappa_e_dimensional(c_e_dimensional, T_ref) / kappa_scale - - -# (1-2*t_plus) is for Nernst-Planck -# 2*(1-t_plus) for Stefan-Maxwell -def chi(c_e, c_ox=0, c_hy=0): - return ( - chi_dimensional(c_e_typ * c_e) - * (2 * (1 - t_plus(c_e))) - / (V_w * c_T(c_e_typ * c_e, c_e_typ * c_ox, c_e_typ * c_hy)) - ) - - -def U_n(c_e_n, T): - "Dimensionless open-circuit voltage in the negative electrode" - c_e_n_dimensional = c_e_n * c_e_typ - T_dim = Delta_T * T + T_ref - return (U_n_dimensional(c_e_n_dimensional, T_dim) - U_n_ref) / potential_scale - - -def U_p(c_e_p, T): - "Dimensionless open-circuit voltage in the positive electrode" - c_e_p_dimensional = c_e_p * c_e_typ - T_dim = Delta_T * T + T_ref - return (U_p_dimensional(c_e_p_dimensional, T_dim) - U_p_ref) / potential_scale - - -def j0_n(c_e, T): - "Dimensionless exchange-current density in the negative electrode" - c_e_dim = c_e * c_e_typ - T_dim = Delta_T * T + T_ref - return j0_n_dimensional(c_e_dim, T_dim) / j_scale_n - - -def j0_p(c_e, T): - "Dimensionless exchange-current density in the positive electrode" - c_e_dim = c_e * c_e_typ - T_dim = Delta_T * T + T_ref - return j0_p_dimensional(c_e_dim, T_dim) / j_scale_p - - -def j0_p_Ox(c_e, T): - "Dimensionless oxygen exchange-current density in the positive electrode" - c_e_dim = c_e * c_e_typ - T_dim = Delta_T * T + T_ref - return j0_p_Ox_dimensional(c_e_dim, T_dim) / j_scale_p - - -# -------------------------------------------------------------------------------------- -# 6. Input current and voltage - -dimensional_current_with_time = pybamm.FunctionParameter( - "Current function [A]", {"Time [s]": pybamm.t * timescale} -) -dimensional_current_density_with_time = dimensional_current_with_time / ( - n_electrodes_parallel * pybamm.geometric_parameters.A_cc -) -current_with_time = ( - dimensional_current_with_time / I_typ * pybamm.Function(np.sign, I_typ) -) - - -"Remove any temporary variables" -del inputs diff --git a/pybamm/parameters/standard_parameters_lithium_ion.py b/pybamm/parameters/standard_parameters_lithium_ion.py deleted file mode 100644 index aea8467024..0000000000 --- a/pybamm/parameters/standard_parameters_lithium_ion.py +++ /dev/null @@ -1,610 +0,0 @@ -# -# Standard parameters for lithium-ion battery models -# -""" -Standard parameters for lithium-ion battery models -""" -import pybamm -import numpy as np - - -# -------------------------------------------------------------------------------------- -"File Layout:" -# 1. Dimensional Parameters -# 2. Dimensional Functions -# 3. Scalings -# 4. Dimensionless Parameters -# 5. Dimensionless Functions -# 6. Input Current - -# -------------------------------------------------------------------------------------- -"1. Dimensional Parameters" - -# Physical constants -R = pybamm.constants.R -F = pybamm.constants.F -T_ref = pybamm.Parameter("Reference temperature [K]") - -# Macroscale geometry -L_cn = pybamm.geometric_parameters.L_cn -L_n = pybamm.geometric_parameters.L_n -L_s = pybamm.geometric_parameters.L_s -L_p = pybamm.geometric_parameters.L_p -L_cp = pybamm.geometric_parameters.L_cp -L_x = pybamm.geometric_parameters.L_x -L_y = pybamm.geometric_parameters.L_y -L_z = pybamm.geometric_parameters.L_z -L = pybamm.geometric_parameters.L -A_cc = pybamm.geometric_parameters.A_cc -A_cooling = pybamm.geometric_parameters.A_cooling -V_cell = pybamm.geometric_parameters.V_cell - -# Tab geometry -L_tab_n = pybamm.geometric_parameters.L_tab_n -Centre_y_tab_n = pybamm.geometric_parameters.Centre_y_tab_n -Centre_z_tab_n = pybamm.geometric_parameters.Centre_z_tab_n -L_tab_p = pybamm.geometric_parameters.L_tab_p -Centre_y_tab_p = pybamm.geometric_parameters.Centre_y_tab_p -Centre_z_tab_p = pybamm.geometric_parameters.Centre_z_tab_p -A_tab_n = pybamm.geometric_parameters.A_tab_n -A_tab_p = pybamm.geometric_parameters.A_tab_p - -# Electrical -I_typ = pybamm.electrical_parameters.I_typ -Q = pybamm.electrical_parameters.Q -C_rate = pybamm.electrical_parameters.C_rate -n_electrodes_parallel = pybamm.electrical_parameters.n_electrodes_parallel -i_typ = pybamm.electrical_parameters.i_typ -voltage_low_cut_dimensional = pybamm.electrical_parameters.voltage_low_cut_dimensional -voltage_high_cut_dimensional = pybamm.electrical_parameters.voltage_high_cut_dimensional - -# Electrolyte properties -c_e_typ = pybamm.Parameter("Typical electrolyte concentration [mol.m-3]") - -# Electrode properties -c_n_max = pybamm.Parameter("Maximum concentration in negative electrode [mol.m-3]") -c_p_max = pybamm.Parameter("Maximum concentration in positive electrode [mol.m-3]") -sigma_cn_dimensional = pybamm.Parameter( - "Negative current collector conductivity [S.m-1]" -) -sigma_n_dim = pybamm.Parameter("Negative electrode conductivity [S.m-1]") -sigma_p_dim = pybamm.Parameter("Positive electrode conductivity [S.m-1]") -sigma_cp_dimensional = pybamm.Parameter( - "Positive current collector conductivity [S.m-1]" -) - -# Microscale geometry -a_n_dim = pybamm.geometric_parameters.a_n_dim -a_p_dim = pybamm.geometric_parameters.a_p_dim -a_k_dim = pybamm.Concatenation( - pybamm.FullBroadcast(a_n_dim, ["negative electrode"], "current collector"), - pybamm.FullBroadcast(0, ["separator"], "current collector"), - pybamm.FullBroadcast(a_p_dim, ["positive electrode"], "current collector"), -) -R_n = pybamm.geometric_parameters.R_n -R_p = pybamm.geometric_parameters.R_p -b_e_n = pybamm.geometric_parameters.b_e_n -b_e_s = pybamm.geometric_parameters.b_e_s -b_e_p = pybamm.geometric_parameters.b_e_p -b_s_n = pybamm.geometric_parameters.b_s_n -b_s_s = pybamm.geometric_parameters.b_s_s -b_s_p = pybamm.geometric_parameters.b_s_p - -# Electrochemical reactions -ne_n = pybamm.Parameter("Negative electrode electrons in reaction") -ne_p = pybamm.Parameter("Positive electrode electrons in reaction") -C_dl_n_dimensional = pybamm.Parameter( - "Negative electrode double-layer capacity [F.m-2]" -) -C_dl_p_dimensional = pybamm.Parameter( - "Positive electrode double-layer capacity [F.m-2]" -) - - -# SEI parameters - -V_bar_inner_dimensional = pybamm.Parameter("Inner SEI partial molar volume [m3.mol-1]") -V_bar_outer_dimensional = pybamm.Parameter("Outer SEI partial molar volume [m3.mol-1]") - -m_sei_dimensional = pybamm.Parameter("SEI reaction exchange current density [A.m-2]") - -R_sei_dimensional = pybamm.Parameter("SEI resistivity [Ohm.m]") - -D_sol_dimensional = pybamm.Parameter("Outer SEI solvent diffusivity [m2.s-1]") -c_sol_dimensional = pybamm.Parameter("Bulk solvent concentration [mol.m-3]") - -m_ratio = pybamm.Parameter("Ratio of inner and outer SEI exchange current densities") - -U_inner_dimensional = pybamm.Parameter("Inner SEI open-circuit potential [V]") -U_outer_dimensional = pybamm.Parameter("Outer SEI open-circuit potential [V]") - -kappa_inner_dimensional = pybamm.Parameter("Inner SEI electron conductivity [S.m-1]") - -D_li_dimensional = pybamm.Parameter( - "Inner SEI lithium interstitial diffusivity [m2.s-1]" -) - -c_li_0_dimensional = pybamm.Parameter( - "Lithium interstitial reference concentration [mol.m-3]" -) - -L_inner_0_dim = pybamm.Parameter("Initial inner SEI thickness [m]") -L_outer_0_dim = pybamm.Parameter("Initial outer SEI thickness [m]") - -L_sei_0_dim = L_inner_0_dim + L_outer_0_dim - -# EC reaction - -c_ec_0_dim = pybamm.Parameter("EC initial concentration in electrolyte [mol.m-3]") -D_ec_dim = pybamm.Parameter("EC diffusivity [m2.s-1]") -k_sei_dim = pybamm.Parameter("SEI kinetic rate constant [m.s-1]") -U_sei_dim = pybamm.Parameter("SEI open-circuit potential [V]") - -# Initial conditions -c_e_init_dimensional = pybamm.Parameter( - "Initial concentration in electrolyte [mol.m-3]" -) - - -def c_n_init_dimensional(x): - "Initial concentration as a function of dimensionless position x" - inputs = {"Dimensionless through-cell position (x_n)": x} - return pybamm.FunctionParameter( - "Initial concentration in negative electrode [mol.m-3]", inputs - ) - - -def c_p_init_dimensional(x): - "Initial concentration as a function of dimensionless position x" - inputs = {"Dimensionless through-cell position (x_p)": x} - return pybamm.FunctionParameter( - "Initial concentration in positive electrode [mol.m-3]", inputs - ) - - -# thermal -Delta_T = pybamm.thermal_parameters.Delta_T - -# velocity scale -velocity_scale = pybamm.Scalar(1) - -# -------------------------------------------------------------------------------------- -"2. Dimensional Functions" - - -def D_e_dimensional(c_e, T): - "Dimensional diffusivity in electrolyte" - inputs = {"Electrolyte concentration [mol.m-3]": c_e, "Temperature [K]": T} - return pybamm.FunctionParameter("Electrolyte diffusivity [m2.s-1]", inputs) - - -def kappa_e_dimensional(c_e, T): - "Dimensional electrolyte conductivity" - inputs = {"Electrolyte concentration [mol.m-3]": c_e, "Temperature [K]": T} - return pybamm.FunctionParameter("Electrolyte conductivity [S.m-1]", inputs) - - -def D_n_dimensional(sto, T): - """Dimensional diffusivity in negative particle. Note this is defined as a - function of stochiometry""" - inputs = {"Negative particle stoichiometry": sto, "Temperature [K]": T} - return pybamm.FunctionParameter("Negative electrode diffusivity [m2.s-1]", inputs) - - -def D_p_dimensional(sto, T): - """Dimensional diffusivity in positive particle. Note this is defined as a - function of stochiometry""" - inputs = {"Positive particle stoichiometry": sto, "Temperature [K]": T} - return pybamm.FunctionParameter("Positive electrode diffusivity [m2.s-1]", inputs) - - -def j0_n_dimensional(c_e, c_s_surf, T): - "Dimensional negative exchange-current density [A.m-2]" - inputs = { - "Electrolyte concentration [mol.m-3]": c_e, - "Negative particle surface concentration [mol.m-3]": c_s_surf, - "Temperature [K]": T, - } - return pybamm.FunctionParameter( - "Negative electrode exchange-current density [A.m-2]", inputs - ) - - -def j0_p_dimensional(c_e, c_s_surf, T): - "Dimensional negative exchange-current density [A.m-2]" - inputs = { - "Electrolyte concentration [mol.m-3]": c_e, - "Positive particle surface concentration [mol.m-3]": c_s_surf, - "Temperature [K]": T, - } - return pybamm.FunctionParameter( - "Positive electrode exchange-current density [A.m-2]", inputs - ) - - -def dUdT_n_dimensional(sto): - """ - Dimensional entropic change of the negative electrode open-circuit potential [V.K-1] - """ - inputs = { - "Negative particle stoichiometry": sto, - "Max negative particle concentration [mol.m-3]": c_n_max, - } - return pybamm.FunctionParameter( - "Negative electrode OCP entropic change [V.K-1]", inputs - ) - - -def dUdT_p_dimensional(sto): - """ - Dimensional entropic change of the positive electrode open-circuit potential [V.K-1] - """ - inputs = { - "Positive particle stoichiometry": sto, - "Max positive particle concentration [mol.m-3]": c_p_max, - } - return pybamm.FunctionParameter( - "Positive electrode OCP entropic change [V.K-1]", inputs - ) - - -def U_n_dimensional(sto, T): - "Dimensional open-circuit potential in the negative electrode [V]" - inputs = {"Negative particle stoichiometry": sto} - u_ref = pybamm.FunctionParameter("Negative electrode OCP [V]", inputs) - return u_ref + (T - T_ref) * dUdT_n_dimensional(sto) - - -def U_p_dimensional(sto, T): - "Dimensional open-circuit potential in the positive electrode [V]" - inputs = {"Positive particle stoichiometry": sto} - u_ref = pybamm.FunctionParameter("Positive electrode OCP [V]", inputs) - return u_ref + (T - T_ref) * dUdT_p_dimensional(sto) - - -# Reference OCP based on initial concentration at current collector/electrode interface -sto_n_init = c_n_init_dimensional(0) / c_n_max -U_n_ref = U_n_dimensional(sto_n_init, T_ref) - -# Reference OCP based on initial concentration at current collector/electrode interface -sto_p_init = c_p_init_dimensional(1) / c_p_max -U_p_ref = U_p_dimensional(sto_p_init, T_ref) - -j0_n_ref_dimensional = j0_n_dimensional(c_e_typ, c_n_max / 2, T_ref) * 2 -j0_p_ref_dimensional = j0_p_dimensional(c_e_typ, c_p_max / 2, T_ref) * 2 - -# ------------------------------------------------------------------------------------- -"3. Scales" -# concentration -electrolyte_concentration_scale = c_e_typ -negative_particle_concentration_scale = c_n_max -positive_particle_concentration_scale = c_n_max - -# electrical -potential_scale = R * T_ref / F -current_scale = i_typ -j_scale_n = i_typ / (a_n_dim * L_x) -j_scale_p = i_typ / (a_p_dim * L_x) - -# Discharge timescale -tau_discharge = F * c_n_max * L_x / i_typ - -# Reaction timescales -tau_r_n = F * c_n_max / (j0_n_ref_dimensional * a_n_dim) -tau_r_p = F * c_p_max / (j0_p_ref_dimensional * a_p_dim) - -# Electrolyte diffusion timescale -D_e_typ = D_e_dimensional(c_e_typ, T_ref) -tau_diffusion_e = L_x ** 2 / D_e_typ - -# Particle diffusion timescales -tau_diffusion_n = R_n ** 2 / D_n_dimensional(pybamm.Scalar(1), T_ref) -tau_diffusion_p = R_p ** 2 / D_p_dimensional(pybamm.Scalar(1), T_ref) - -# Thermal diffusion timescale -tau_th_yz = pybamm.thermal_parameters.tau_th_yz - -# Choose discharge timescale -timescale = tau_discharge - -# -------------------------------------------------------------------------------------- -"4. Dimensionless Parameters" -# Timescale ratios -C_n = tau_diffusion_n / tau_discharge -C_p = tau_diffusion_p / tau_discharge -C_e = tau_diffusion_e / tau_discharge -C_r_n = tau_r_n / tau_discharge -C_r_p = tau_r_p / tau_discharge -C_th = tau_th_yz / tau_discharge - -# Concentration ratios -gamma_e = c_e_typ / c_n_max -gamma_p = c_p_max / c_n_max - -# Macroscale Geometry -l_cn = pybamm.geometric_parameters.l_cn -l_n = pybamm.geometric_parameters.l_n -l_s = pybamm.geometric_parameters.l_s -l_p = pybamm.geometric_parameters.l_p -l_cp = pybamm.geometric_parameters.l_cp -l_x = pybamm.geometric_parameters.l_x -l_y = pybamm.geometric_parameters.l_y -l_z = pybamm.geometric_parameters.l_z -a_cc = pybamm.geometric_parameters.a_cc -a_cooling = pybamm.geometric_parameters.a_cooling -v_cell = pybamm.geometric_parameters.v_cell -l = pybamm.geometric_parameters.l -delta = pybamm.geometric_parameters.delta - -# Tab geometry -l_tab_n = pybamm.geometric_parameters.l_tab_n -centre_y_tab_n = pybamm.geometric_parameters.centre_y_tab_n -centre_z_tab_n = pybamm.geometric_parameters.centre_z_tab_n -l_tab_p = pybamm.geometric_parameters.l_tab_p -centre_y_tab_p = pybamm.geometric_parameters.centre_y_tab_p -centre_z_tab_p = pybamm.geometric_parameters.centre_z_tab_p - -# Microscale geometry - -inputs = {"Through-cell distance (x_n) [m]": pybamm.standard_spatial_vars.x_n} -epsilon_n = pybamm.FunctionParameter("Negative electrode porosity", inputs) - -inputs = {"Through-cell distance (x_s) [m]": pybamm.standard_spatial_vars.x_s} -epsilon_s = pybamm.FunctionParameter("Separator porosity", inputs) - -inputs = {"Through-cell distance (x_p) [m]": pybamm.standard_spatial_vars.x_p} -epsilon_p = pybamm.FunctionParameter("Positive electrode porosity", inputs) - -epsilon = pybamm.Concatenation(epsilon_n, epsilon_s, epsilon_p) -epsilon_n_init = pybamm.Parameter("Negative electrode porosity") -epsilon_s_init = pybamm.Parameter("Separator porosity") -epsilon_p_init = pybamm.Parameter("Positive electrode porosity") -epsilon_init = pybamm.Concatenation(epsilon_n, epsilon_s, epsilon_p) -epsilon_s_n = pybamm.Parameter("Negative electrode active material volume fraction") -epsilon_s_p = pybamm.Parameter("Positive electrode active material volume fraction") -epsilon_inactive_n = 1 - epsilon_n - epsilon_s_n -epsilon_inactive_s = 1 - epsilon_s -epsilon_inactive_p = 1 - epsilon_p - epsilon_s_p -a_n = a_n_dim * R_n -a_p = a_p_dim * R_p - -# Electrode Properties -sigma_cn = sigma_cn_dimensional * potential_scale / i_typ / L_x -sigma_n = sigma_n_dim * potential_scale / i_typ / L_x -sigma_p = sigma_p_dim * potential_scale / i_typ / L_x -sigma_cp = sigma_cp_dimensional * potential_scale / i_typ / L_x -sigma_cn_prime = sigma_cn * delta ** 2 -sigma_n_prime = sigma_n * delta -sigma_p_prime = sigma_p * delta -sigma_cp_prime = sigma_cp * delta ** 2 -sigma_cn_dbl_prime = sigma_cn_prime * delta -sigma_cp_dbl_prime = sigma_cp_prime * delta -# should rename this to avoid confusion with Butler-Volmer -alpha = 1 / (sigma_cn * delta ** 2 * l_cn) + 1 / (sigma_cp * delta ** 2 * l_cp) -alpha_prime = alpha / delta - -# Electrolyte Properties - - -def t_plus(c_e): - "Dimensionless transference number (i.e. c_e is dimensionless)" - inputs = {"Electrolyte concentration [mol.m-3]": c_e * c_e_typ} - return pybamm.FunctionParameter("Cation transference number", inputs) - - -def one_plus_dlnf_dlnc(c_e): - inputs = {"Electrolyte concentration [mol.m-3]": c_e * c_e_typ} - return pybamm.FunctionParameter("1 + dlnf/dlnc", inputs) - - -beta_surf = pybamm.Scalar(0) -beta_surf_n = pybamm.Scalar(0) -beta_surf_p = pybamm.Scalar(0) - - -# (1-2*t_plus) is for Nernst-Planck -# 2*(1-t_plus) for Stefan-Maxwell -# Bizeray et al (2016) "Resolving a discrepancy ..." -def chi(c_e): - return (2 * (1 - t_plus(c_e))) * (one_plus_dlnf_dlnc(c_e)) - - -# Electrochemical Reactions -C_dl_n = C_dl_n_dimensional * potential_scale / j_scale_n / tau_discharge -C_dl_p = C_dl_p_dimensional * potential_scale / j_scale_p / tau_discharge - -# Electrical -voltage_low_cut = (voltage_low_cut_dimensional - (U_p_ref - U_n_ref)) / potential_scale -voltage_high_cut = ( - voltage_high_cut_dimensional - (U_p_ref - U_n_ref) -) / potential_scale - -# Thermal -rho_cn = pybamm.thermal_parameters.rho_cn -rho_n = pybamm.thermal_parameters.rho_n -rho_s = pybamm.thermal_parameters.rho_s -rho_p = pybamm.thermal_parameters.rho_p -rho_cp = pybamm.thermal_parameters.rho_cp - -rho_k = pybamm.thermal_parameters.rho_k -# effective volumetric heat capacity -rho = (rho_cn * l_cn + rho_n * l_n + rho_s * l_s + rho_p * l_p + rho_cp * l_cp) / l - -lambda_cn = pybamm.thermal_parameters.lambda_cn -lambda_n = pybamm.thermal_parameters.lambda_n -lambda_s = pybamm.thermal_parameters.lambda_s -lambda_p = pybamm.thermal_parameters.lambda_p -lambda_cp = pybamm.thermal_parameters.lambda_cp - -lambda_k = pybamm.thermal_parameters.lambda_k - -Theta = pybamm.thermal_parameters.Theta - -h_edge = pybamm.thermal_parameters.h_edge -h_tab_n = pybamm.thermal_parameters.h_tab_n -h_tab_p = pybamm.thermal_parameters.h_tab_p -h_cn = pybamm.thermal_parameters.h_cn -h_cp = pybamm.thermal_parameters.h_cp -h_total = pybamm.thermal_parameters.h_total - -B = ( - i_typ - * R - * T_ref - * tau_th_yz - / (pybamm.thermal_parameters.rho_eff_dim * F * Delta_T * L_x) -) - -T_amb_dim = pybamm.thermal_parameters.T_amb_dim -T_amb = pybamm.thermal_parameters.T_amb - -# SEI parameters -C_sei_reaction_n = (j_scale_n / m_sei_dimensional) * pybamm.exp( - -(F * U_n_ref / (2 * R * T_ref)) -) -C_sei_reaction_p = (j_scale_p / m_sei_dimensional) * pybamm.exp( - -(F * U_n_ref / (2 * R * T_ref)) -) - -C_sei_solvent_n = j_scale_n * L_sei_0_dim / (c_sol_dimensional * F * D_sol_dimensional) -C_sei_solvent_p = j_scale_p * L_sei_0_dim / (c_sol_dimensional * F * D_sol_dimensional) - -C_sei_electron_n = j_scale_n * F * L_sei_0_dim / (kappa_inner_dimensional * R * T_ref) -C_sei_electron_p = j_scale_p * F * L_sei_0_dim / (kappa_inner_dimensional * R * T_ref) - -C_sei_inter_n = j_scale_n * L_sei_0_dim / (D_li_dimensional * c_li_0_dimensional * F) -C_sei_inter_p = j_scale_p * L_sei_0_dim / (D_li_dimensional * c_li_0_dimensional * F) - -U_inner_electron = F * U_inner_dimensional / R / T_ref - -R_sei_n = F * j_scale_n * R_sei_dimensional * L_sei_0_dim / R / T_ref -R_sei_p = F * j_scale_p * R_sei_dimensional * L_sei_0_dim / R / T_ref - -v_bar = V_bar_outer_dimensional / V_bar_inner_dimensional - -L_inner_0 = L_inner_0_dim / L_sei_0_dim -L_outer_0 = L_outer_0_dim / L_sei_0_dim - -# ratio of SEI reaction scale to intercalation reaction -Gamma_SEI_n = (V_bar_inner_dimensional * j_scale_n * tau_discharge) / (F * L_sei_0_dim) -Gamma_SEI_p = (V_bar_inner_dimensional * j_scale_p * tau_discharge) / (F * L_sei_0_dim) - -# EC reaction -C_ec_n = L_sei_0_dim * j_scale_n / (F * c_ec_0_dim * D_ec_dim) -C_sei_ec_n = ( - F - * k_sei_dim - * c_ec_0_dim - / j_scale_n - * (pybamm.exp(-(F * (U_n_ref - U_sei_dim) / (2 * R * T_ref)))) -) -beta_sei_n = a_n_dim * L_sei_0_dim * Gamma_SEI_n - -# Initial conditions -T_init = pybamm.thermal_parameters.T_init -c_e_init = c_e_init_dimensional / c_e_typ - - -def c_n_init(x): - "Dimensionless initial concentration as a function of dimensionless position x" - return c_n_init_dimensional(x) / c_n_max - - -def c_p_init(x): - "Dimensionless initial concentration as a function of dimensionless position x" - return c_p_init_dimensional(x) / c_p_max - - -# -------------------------------------------------------------------------------------- -"5. Dimensionless Functions" - - -def D_e(c_e, T): - "Dimensionless electrolyte diffusivity" - c_e_dimensional = c_e * c_e_typ - T_dim = Delta_T * T + T_ref - return D_e_dimensional(c_e_dimensional, T_dim) / D_e_typ - - -def kappa_e(c_e, T): - "Dimensionless electrolyte conductivity" - c_e_dimensional = c_e * c_e_typ - kappa_scale = F ** 2 * D_e_typ * c_e_typ / (R * T_ref) - T_dim = Delta_T * T + T_ref - return kappa_e_dimensional(c_e_dimensional, T_dim) / kappa_scale - - -def D_n(c_s_n, T): - "Dimensionless negative particle diffusivity" - sto = c_s_n - T_dim = Delta_T * T + T_ref - return D_n_dimensional(sto, T_dim) / D_n_dimensional(pybamm.Scalar(1), T_ref) - - -def D_p(c_s_p, T): - "Dimensionless positive particle diffusivity" - sto = c_s_p - T_dim = Delta_T * T + T_ref - return D_p_dimensional(sto, T_dim) / D_p_dimensional(pybamm.Scalar(1), T_ref) - - -def j0_n(c_e, c_s_surf, T): - "Dimensionless negative exchange-current density" - c_e_dim = c_e * c_e_typ - c_s_surf_dim = c_s_surf * c_n_max - T_dim = Delta_T * T + T_ref - - return j0_n_dimensional(c_e_dim, c_s_surf_dim, T_dim) / j0_n_ref_dimensional - - -def j0_p(c_e, c_s_surf, T): - "Dimensionless positive exchange-current density" - c_e_dim = c_e * c_e_typ - c_s_surf_dim = c_s_surf * c_p_max - T_dim = Delta_T * T + T_ref - - return j0_p_dimensional(c_e_dim, c_s_surf_dim, T_dim) / j0_p_ref_dimensional - - -def U_n(c_s_n, T): - "Dimensionless open-circuit potential in the negative electrode" - sto = c_s_n - T_dim = Delta_T * T + T_ref - return (U_n_dimensional(sto, T_dim) - U_n_ref) / potential_scale - - -def U_p(c_s_p, T): - "Dimensionless open-circuit potential in the positive electrode" - sto = c_s_p - T_dim = Delta_T * T + T_ref - return (U_p_dimensional(sto, T_dim) - U_p_ref) / potential_scale - - -def dUdT_n(c_s_n): - "Dimensionless entropic change in negative open-circuit potential" - sto = c_s_n - return dUdT_n_dimensional(sto) * Delta_T / potential_scale - - -def dUdT_p(c_s_p): - "Dimensionless entropic change in positive open-circuit potential" - sto = c_s_p - return dUdT_p_dimensional(sto) * Delta_T / potential_scale - - -# -------------------------------------------------------------------------------------- -# 6. Input current and voltage - -dimensional_current_with_time = pybamm.FunctionParameter( - "Current function [A]", {"Time [s]": pybamm.t * timescale} -) -dimensional_current_density_with_time = dimensional_current_with_time / ( - n_electrodes_parallel * pybamm.geometric_parameters.A_cc -) -current_with_time = ( - dimensional_current_with_time / I_typ * pybamm.Function(np.sign, I_typ) -) - - -"Remove any temporary variables" -del inputs diff --git a/pybamm/parameters/thermal_parameters.py b/pybamm/parameters/thermal_parameters.py index 9662e426b5..81c9880274 100644 --- a/pybamm/parameters/thermal_parameters.py +++ b/pybamm/parameters/thermal_parameters.py @@ -3,127 +3,178 @@ # import pybamm -# -------------------------------------------------------------------------------------- -# Dimensional parameters - -# Reference temperature -T_ref = pybamm.Parameter("Reference temperature [K]") - -# Density -rho_cn_dim = pybamm.Parameter("Negative current collector density [kg.m-3]") -rho_n_dim = pybamm.Parameter("Negative electrode density [kg.m-3]") -rho_s_dim = pybamm.Parameter("Separator density [kg.m-3]") -rho_p_dim = pybamm.Parameter("Positive electrode density [kg.m-3]") -rho_cp_dim = pybamm.Parameter("Positive current collector density [kg.m-3]") - -# Specific heat capacity -c_p_cn_dim = pybamm.Parameter( - "Negative current collector specific heat capacity [J.kg-1.K-1]" -) -c_p_n_dim = pybamm.Parameter("Negative electrode specific heat capacity [J.kg-1.K-1]") -c_p_s_dim = pybamm.Parameter("Separator specific heat capacity [J.kg-1.K-1]") -c_p_p_dim = pybamm.Parameter("Negative electrode specific heat capacity [J.kg-1.K-1]") -c_p_cp_dim = pybamm.Parameter( - "Positive current collector specific heat capacity [J.kg-1.K-1]" -) - -# Thermal conductivity -lambda_cn_dim = pybamm.Parameter( - "Negative current collector thermal conductivity [W.m-1.K-1]" -) -lambda_n_dim = pybamm.Parameter("Negative electrode thermal conductivity [W.m-1.K-1]") -lambda_s_dim = pybamm.Parameter("Separator thermal conductivity [W.m-1.K-1]") -lambda_p_dim = pybamm.Parameter("Positive electrode thermal conductivity [W.m-1.K-1]") -lambda_cp_dim = pybamm.Parameter( - "Positive current collector thermal conductivity [W.m-1.K-1]" -) - -# Effective thermal properties -rho_eff_dim = ( - rho_cn_dim * c_p_cn_dim * pybamm.geometric_parameters.L_cn - + rho_n_dim * c_p_n_dim * pybamm.geometric_parameters.L_n - + rho_s_dim * c_p_s_dim * pybamm.geometric_parameters.L_s - + rho_p_dim * c_p_p_dim * pybamm.geometric_parameters.L_p - + rho_cp_dim * c_p_cp_dim * pybamm.geometric_parameters.L_cp -) / pybamm.geometric_parameters.L -lambda_eff_dim = ( - lambda_cn_dim * pybamm.geometric_parameters.L_cn - + lambda_n_dim * pybamm.geometric_parameters.L_n - + lambda_s_dim * pybamm.geometric_parameters.L_s - + lambda_p_dim * pybamm.geometric_parameters.L_p - + lambda_cp_dim * pybamm.geometric_parameters.L_cp -) / pybamm.geometric_parameters.L - -# Cooling coefficient -h_cn_dim = pybamm.Parameter( - "Negative current collector surface heat transfer coefficient [W.m-2.K-1]" -) -h_cp_dim = pybamm.Parameter( - "Positive current collector surface heat transfer coefficient [W.m-2.K-1]" -) -h_tab_n_dim = pybamm.Parameter("Negative tab heat transfer coefficient [W.m-2.K-1]") -h_tab_p_dim = pybamm.Parameter("Positive tab heat transfer coefficient [W.m-2.K-1]") -h_edge_dim = pybamm.Parameter("Edge heat transfer coefficient [W.m-2.K-1]") - -h_total_dim = pybamm.Parameter("Total heat transfer coefficient [W.m-2.K-1]") - -# Typical temperature rise -Delta_T = pybamm.Scalar(1) - -# Initial temperature -T_init_dim = pybamm.Parameter("Initial temperature [K]") - -# -------------------------------------------------------------------------------------- -# Timescales -tau_th_yz = rho_eff_dim * (pybamm.geometric_parameters.L_z ** 2) / lambda_eff_dim - -# -------------------------------------------------------------------------------------- -# Dimensionless parameters - -rho_cn = rho_cn_dim * c_p_cn_dim / rho_eff_dim -rho_n = rho_n_dim * c_p_n_dim / rho_eff_dim -rho_s = rho_s_dim * c_p_s_dim / rho_eff_dim -rho_p = rho_p_dim * c_p_p_dim / rho_eff_dim -rho_cp = rho_cp_dim * c_p_cp_dim / rho_eff_dim - -rho_k = pybamm.Concatenation( - pybamm.FullBroadcast(rho_n, ["negative electrode"], "current collector"), - pybamm.FullBroadcast(rho_s, ["separator"], "current collector"), - pybamm.FullBroadcast(rho_p, ["positive electrode"], "current collector"), -) - -lambda_cn = lambda_cn_dim / lambda_eff_dim -lambda_n = lambda_n_dim / lambda_eff_dim -lambda_s = lambda_s_dim / lambda_eff_dim -lambda_p = lambda_p_dim / lambda_eff_dim -lambda_cp = lambda_cp_dim / lambda_eff_dim - -lambda_k = pybamm.Concatenation( - pybamm.FullBroadcast(lambda_n, ["negative electrode"], "current collector"), - pybamm.FullBroadcast(lambda_s, ["separator"], "current collector"), - pybamm.FullBroadcast(lambda_p, ["positive electrode"], "current collector"), -) - - -Theta = Delta_T / T_ref - -h_edge = h_edge_dim * pybamm.geometric_parameters.L_x / lambda_eff_dim -h_tab_n = h_tab_n_dim * pybamm.geometric_parameters.L_x / lambda_eff_dim -h_tab_p = h_tab_p_dim * pybamm.geometric_parameters.L_x / lambda_eff_dim -h_cn = h_cn_dim * pybamm.geometric_parameters.L_x / lambda_eff_dim -h_cp = h_cp_dim * pybamm.geometric_parameters.L_x / lambda_eff_dim -h_total = h_total_dim * pybamm.geometric_parameters.L_x / lambda_eff_dim - - -T_init = (T_init_dim - T_ref) / Delta_T - -# -------------------------------------------------------------------------------------- -# Ambient temperature - - -def T_amb_dim(t): - return pybamm.FunctionParameter("Ambient temperature [K]", {"Times [s]": t}) - - -def T_amb(t): - return (T_amb_dim(t) - T_ref) / Delta_T # dimensionless T_amb + +class ThermalParameters: + """ + Standard thermal parameters + + Layout: + 1. Dimensional Parameters + 2. Dimensional Functions + 3. Dimensionless Parameters + 4. Dimensionless Functions + """ + + def __init__(self): + + # Get geometric parameters + self.geo = pybamm.GeometricParameters() + + # Set parameters + self._set_dimensional_parameters() + self._set_dimensionless_parameters() + + def _set_dimensional_parameters(self): + "Defines the dimensional parameters" + + # Reference temperature + self.T_ref = pybamm.Parameter("Reference temperature [K]") + + # Density + self.rho_cn_dim = pybamm.Parameter( + "Negative current collector density [kg.m-3]" + ) + self.rho_n_dim = pybamm.Parameter("Negative electrode density [kg.m-3]") + self.rho_s_dim = pybamm.Parameter("Separator density [kg.m-3]") + self.rho_p_dim = pybamm.Parameter("Positive electrode density [kg.m-3]") + self.rho_cp_dim = pybamm.Parameter( + "Positive current collector density [kg.m-3]" + ) + + # Specific heat capacity + self.c_p_cn_dim = pybamm.Parameter( + "Negative current collector specific heat capacity [J.kg-1.K-1]" + ) + self.c_p_n_dim = pybamm.Parameter( + "Negative electrode specific heat capacity [J.kg-1.K-1]" + ) + self.c_p_s_dim = pybamm.Parameter( + "Separator specific heat capacity [J.kg-1.K-1]" + ) + self.c_p_p_dim = pybamm.Parameter( + "Negative electrode specific heat capacity [J.kg-1.K-1]" + ) + self.c_p_cp_dim = pybamm.Parameter( + "Positive current collector specific heat capacity [J.kg-1.K-1]" + ) + + # Thermal conductivity + self.lambda_cn_dim = pybamm.Parameter( + "Negative current collector thermal conductivity [W.m-1.K-1]" + ) + self.lambda_n_dim = pybamm.Parameter( + "Negative electrode thermal conductivity [W.m-1.K-1]" + ) + self.lambda_s_dim = pybamm.Parameter( + "Separator thermal conductivity [W.m-1.K-1]" + ) + self.lambda_p_dim = pybamm.Parameter( + "Positive electrode thermal conductivity [W.m-1.K-1]" + ) + self.lambda_cp_dim = pybamm.Parameter( + "Positive current collector thermal conductivity [W.m-1.K-1]" + ) + + # Effective volumetic heat capacity + self.rho_eff_dim = ( + self.rho_cn_dim * self.c_p_cn_dim * self.geo.L_cn + + self.rho_n_dim * self.c_p_n_dim * self.geo.L_n + + self.rho_s_dim * self.c_p_s_dim * self.geo.L_s + + self.rho_p_dim * self.c_p_p_dim * self.geo.L_p + + self.rho_cp_dim * self.c_p_cp_dim * self.geo.L_cp + ) / self.geo.L + + # Effective thermal conductivity + self.lambda_eff_dim = ( + self.lambda_cn_dim * self.geo.L_cn + + self.lambda_n_dim * self.geo.L_n + + self.lambda_s_dim * self.geo.L_s + + self.lambda_p_dim * self.geo.L_p + + self.lambda_cp_dim * self.geo.L_cp + ) / self.geo.L + + # Cooling coefficient + self.h_cn_dim = pybamm.Parameter( + "Negative current collector surface heat transfer coefficient [W.m-2.K-1]" + ) + self.h_cp_dim = pybamm.Parameter( + "Positive current collector surface heat transfer coefficient [W.m-2.K-1]" + ) + self.h_tab_n_dim = pybamm.Parameter( + "Negative tab heat transfer coefficient [W.m-2.K-1]" + ) + self.h_tab_p_dim = pybamm.Parameter( + "Positive tab heat transfer coefficient [W.m-2.K-1]" + ) + self.h_edge_dim = pybamm.Parameter("Edge heat transfer coefficient [W.m-2.K-1]") + self.h_total_dim = pybamm.Parameter( + "Total heat transfer coefficient [W.m-2.K-1]" + ) + + # Typical temperature rise + self.Delta_T = pybamm.Scalar(1) + + # Initial temperature + self.T_init_dim = pybamm.Parameter("Initial temperature [K]") + + # Planar (y,z) thermal diffusion timescale + self.tau_th_yz = self.rho_eff_dim * (self.geo.L_z ** 2) / self.lambda_eff_dim + + def T_amb_dim(self, t): + "Dimensional ambient temperature" + return pybamm.FunctionParameter("Ambient temperature [K]", {"Times [s]": t}) + + def _set_dimensionless_parameters(self): + "Defines the dimensionless parameters" + + # Density + self.rho_cn = self.rho_cn_dim * self.c_p_cn_dim / self.rho_eff_dim + self.rho_n = self.rho_n_dim * self.c_p_n_dim / self.rho_eff_dim + self.rho_s = self.rho_s_dim * self.c_p_s_dim / self.rho_eff_dim + self.rho_p = self.rho_p_dim * self.c_p_p_dim / self.rho_eff_dim + self.rho_cp = self.rho_cp_dim * self.c_p_cp_dim / self.rho_eff_dim + + self.rho_k = pybamm.Concatenation( + pybamm.FullBroadcast( + self.rho_n, ["negative electrode"], "current collector" + ), + pybamm.FullBroadcast(self.rho_s, ["separator"], "current collector"), + pybamm.FullBroadcast( + self.rho_p, ["positive electrode"], "current collector" + ), + ) + + # Thermal conductivity + self.lambda_cn = self.lambda_cn_dim / self.lambda_eff_dim + self.lambda_n = self.lambda_n_dim / self.lambda_eff_dim + self.lambda_s = self.lambda_s_dim / self.lambda_eff_dim + self.lambda_p = self.lambda_p_dim / self.lambda_eff_dim + self.lambda_cp = self.lambda_cp_dim / self.lambda_eff_dim + + self.lambda_k = pybamm.Concatenation( + pybamm.FullBroadcast( + self.lambda_n, ["negative electrode"], "current collector" + ), + pybamm.FullBroadcast(self.lambda_s, ["separator"], "current collector"), + pybamm.FullBroadcast( + self.lambda_p, ["positive electrode"], "current collector" + ), + ) + + # Relative temperature rise + self.Theta = self.Delta_T / self.T_ref + + # Cooling coefficient + self.h_cn = self.h_cn_dim * self.geo.L_x / self.lambda_eff_dim + self.h_cp = self.h_cp_dim * self.geo.L_x / self.lambda_eff_dim + self.h_tab_n = self.h_tab_n_dim * self.geo.L_x / self.lambda_eff_dim + self.h_tab_p = self.h_tab_p_dim * self.geo.L_x / self.lambda_eff_dim + self.h_edge = self.h_edge_dim * self.geo.L_x / self.lambda_eff_dim + self.h_total = self.h_total_dim * self.geo.L_x / self.lambda_eff_dim + + # Initial temperature + self.T_init = (self.T_init_dim - self.T_ref) / self.Delta_T + + def T_amb(self, t): + "Dimensionless ambient temperature" + return (self.T_amb_dim(t) - self.T_ref) / self.Delta_T diff --git a/pybamm/simulation.py b/pybamm/simulation.py index e2dec93842..fe8071fb0b 100644 --- a/pybamm/simulation.py +++ b/pybamm/simulation.py @@ -33,7 +33,7 @@ def constant_current_constant_voltage_constant_power(variables): s_I = pybamm.InputParameter("Current switch") s_V = pybamm.InputParameter("Voltage switch") s_P = pybamm.InputParameter("Power switch") - n_cells = pybamm.electrical_parameters.n_cells + n_cells = pybamm.Parameter("Number of cells connected in series to make a battery") return ( s_I * (I - pybamm.InputParameter("Current input [A]")) + s_V * (V - pybamm.InputParameter("Voltage input [V]") / n_cells) diff --git a/test.py b/test.py new file mode 100644 index 0000000000..58d3b29652 --- /dev/null +++ b/test.py @@ -0,0 +1,26 @@ +import pybamm + +model = pybamm.lithium_ion.DFN() +param = pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Marquis2019) +param.update( + { + "Negative electrode active material volume fraction": "[input]", + "Positive electrode active material volume fraction": "[input]", + } +) + +sim = pybamm.Simulation(model, parameter_values=param) + +inputs = { + "Negative electrode active material volume fraction": 0.7, + "Positive electrode active material volume fraction": 0.7, +} +sol_old = sim.solve(t_eval=[0, 3600], inputs=inputs) + +inputs = { + "Negative electrode active material volume fraction": 0.6, + "Positive electrode active material volume fraction": 0.5, +} +sol_new = sim.solve(t_eval=[0, 3600], inputs=inputs) + +pybamm.dynamic_plot([sol_old, sol_new]) diff --git a/tests/integration/test_models/standard_output_tests.py b/tests/integration/test_models/standard_output_tests.py index 4a48d27915..6dd2cd35da 100644 --- a/tests/integration/test_models/standard_output_tests.py +++ b/tests/integration/test_models/standard_output_tests.py @@ -67,8 +67,9 @@ def __init__(self, model, param, disc, solution, operating_condition): # Use dimensional time and space self.t = solution.t * model.timescale_eval + geo = pybamm.GeometricParameters() - L_x = param.evaluate(pybamm.geometric_parameters.L_x) + L_x = param.evaluate(geo.L_x) self.x_n = disc.mesh["negative electrode"].nodes * L_x self.x_s = disc.mesh["separator"].nodes * L_x self.x_p = disc.mesh["positive electrode"].nodes * L_x @@ -80,23 +81,18 @@ def __init__(self, model, param, disc, solution, operating_condition): self.x_edge = disc.mesh.combine_submeshes(*whole_cell).edges * L_x if isinstance(self.model, pybamm.lithium_ion.BaseModel): - R_n = param.evaluate(pybamm.geometric_parameters.R_n) - R_p = param.evaluate(pybamm.geometric_parameters.R_p) + R_n = param.evaluate(geo.R_n) + R_p = param.evaluate(geo.R_p) self.r_n = disc.mesh["negative particle"].nodes * R_n self.r_p = disc.mesh["positive particle"].nodes * R_p self.r_n_edge = disc.mesh["negative particle"].edges * R_n self.r_p_edge = disc.mesh["positive particle"].edges * R_p # Useful parameters - self.l_n = param.evaluate(pybamm.geometric_parameters.l_n) - self.l_p = param.evaluate(pybamm.geometric_parameters.l_p) + self.l_n = param.evaluate(geo.l_n) + self.l_p = param.evaluate(geo.l_p) - if isinstance(self.model, pybamm.lithium_ion.BaseModel): - current_param = pybamm.standard_parameters_lithium_ion.current_with_time - elif isinstance(self.model, pybamm.lead_acid.BaseModel): - current_param = pybamm.standard_parameters_lead_acid.current_with_time - else: - current_param = pybamm.electrical_parameters.current_with_time + current_param = self.model.param.current_with_time self.i_cell = param.process_symbol(current_param).evaluate(solution.t) @@ -574,12 +570,7 @@ def test_conservation(self): current density""" t, x_n, x_s, x_p = self.t, self.x_n, self.x_s, self.x_p - if isinstance(self.model, pybamm.lithium_ion.BaseModel): - current_param = pybamm.standard_parameters_lithium_ion.current_with_time - elif isinstance(self.model, pybamm.lead_acid.BaseModel): - current_param = pybamm.standard_parameters_lead_acid.current_with_time - else: - current_param = pybamm.electrical_parameters.current_with_time + current_param = self.model.param.current_with_time i_cell = self.param.process_symbol(current_param).evaluate(t=t) for x in [x_n, x_s, x_p]: @@ -597,12 +588,14 @@ def test_current_density_boundaries(self): """Test the boundary values of the current densities""" t, x_n, x_p = self.t, self.x_n_edge, self.x_p_edge - if isinstance(self.model, pybamm.lithium_ion.BaseModel): - current_param = pybamm.standard_parameters_lithium_ion.current_with_time - elif isinstance(self.model, pybamm.lead_acid.BaseModel): - current_param = pybamm.standard_parameters_lead_acid.current_with_time - else: - current_param = pybamm.electrical_parameters.current_with_time + # if isinstance(self.model, pybamm.lithium_ion.BaseModel): + # current_param = pybamm.standard_parameters_lithium_ion.current_with_time + # elif isinstance(self.model, pybamm.lead_acid.BaseModel): + # current_param = pybamm.standard_parameters_lead_acid.current_with_time + # else: + # current_param = pybamm.electrical_parameters.current_with_time + + current_param = self.model.param.current_with_time i_cell = self.param.process_symbol(current_param).evaluate(t=t) np.testing.assert_array_almost_equal(self.i_s_n(t, x_n[0]), i_cell, decimal=2) @@ -644,9 +637,9 @@ def test_velocity_vs_current(self): """Test the boundary values of the current densities""" t, x_n, x_p = self.t, self.x_n, self.x_p - beta_n = pybamm.standard_parameters_lead_acid.beta_n + beta_n = self.model.param.beta_n beta_n = self.param.evaluate(beta_n) - beta_p = pybamm.standard_parameters_lead_acid.beta_p + beta_p = self.model.param.beta_p beta_p = self.param.evaluate(beta_p) np.testing.assert_array_almost_equal( diff --git a/tests/integration/test_models/test_submodels/test_interface/test_butler_volmer.py b/tests/integration/test_models/test_submodels/test_interface/test_butler_volmer.py index 3992426ec2..5ce91fe70b 100644 --- a/tests/integration/test_models/test_submodels/test_interface/test_butler_volmer.py +++ b/tests/integration/test_models/test_submodels/test_interface/test_butler_volmer.py @@ -72,7 +72,7 @@ def tearDown(self): del self.delta_phi_s_p def test_creation(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() model_n = pybamm.interface.ButlerVolmer(param, "Negative", "lithium-ion main") j_n = model_n.get_coupled_variables(self.variables)[ "Negative electrode interfacial current density" @@ -91,7 +91,7 @@ def test_creation(self): self.assertEqual(j_p.domain, ["positive electrode"]) def test_set_parameters(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() model_n = pybamm.interface.ButlerVolmer(param, "Negative", "lithium-ion main") j_n = model_n.get_coupled_variables(self.variables)[ "Negative electrode interfacial current density" @@ -113,7 +113,7 @@ def test_set_parameters(self): self.assertNotIsInstance(x, pybamm.Parameter) def test_discretisation(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() model_n = pybamm.interface.ButlerVolmer(param, "Negative", "lithium-ion main") j_n = model_n.get_coupled_variables(self.variables)[ "Negative electrode interfacial current density" @@ -163,7 +163,7 @@ def test_discretisation(self): def test_diff_c_e_lead_acid(self): # With intercalation - param = pybamm.standard_parameters_lead_acid + param = pybamm.LeadAcidParameters() model_n = pybamm.interface.ButlerVolmer(param, "Negative", "lead-acid main") model_p = pybamm.interface.ButlerVolmer(param, "Positive", "lead-acid main") parameter_values = pybamm.lead_acid.BaseModel().default_parameter_values @@ -216,7 +216,7 @@ def j_p(c_e): def test_diff_delta_phi_e_lead_acid(self): # With intercalation - param = pybamm.standard_parameters_lead_acid + param = pybamm.LeadAcidParameters() model_n = pybamm.interface.ButlerVolmer(param, "Negative", "lead-acid main") model_p = pybamm.interface.ButlerVolmer(param, "Positive", "lead-acid main") parameter_values = pybamm.lead_acid.BaseModel().default_parameter_values diff --git a/tests/integration/test_models/test_submodels/test_interface/test_lead_acid.py b/tests/integration/test_models/test_submodels/test_interface/test_lead_acid.py index 625a2458db..f9b42eae3c 100644 --- a/tests/integration/test_models/test_submodels/test_interface/test_lead_acid.py +++ b/tests/integration/test_models/test_submodels/test_interface/test_lead_acid.py @@ -30,7 +30,7 @@ def tearDown(self): def test_creation_main_reaction(self): # With intercalation - param = pybamm.standard_parameters_lead_acid + param = pybamm.LeadAcidParameters() model_n = pybamm.interface.BaseInterface(param, "Negative", "lead-acid main") j0_n = model_n._get_exchange_current_density(self.variables) model_p = pybamm.interface.BaseInterface(param, "Positive", "lead-acid main") @@ -40,7 +40,7 @@ def test_creation_main_reaction(self): def test_set_parameters_main_reaction(self): # With intercalation - param = pybamm.standard_parameters_lead_acid + param = pybamm.LeadAcidParameters() model_n = pybamm.interface.BaseInterface(param, "Negative", "lead-acid main") j0_n = model_n._get_exchange_current_density(self.variables) model_p = pybamm.interface.BaseInterface(param, "Positive", "lead-acid main") @@ -57,7 +57,7 @@ def test_set_parameters_main_reaction(self): def test_discretisation_main_reaction(self): # With intercalation - param = pybamm.standard_parameters_lead_acid + param = pybamm.LeadAcidParameters() model_n = pybamm.interface.BaseInterface(param, "Negative", "lead-acid main") j0_n = model_n._get_exchange_current_density(self.variables) model_p = pybamm.interface.BaseInterface(param, "Positive", "lead-acid main") @@ -80,7 +80,7 @@ def test_discretisation_main_reaction(self): def test_diff_main_reaction(self): # With intercalation - param = pybamm.standard_parameters_lead_acid + param = pybamm.LeadAcidParameters() model_n = pybamm.interface.BaseInterface(param, "Negative", "lead-acid main") model_p = pybamm.interface.BaseInterface(param, "Positive", "lead-acid main") parameter_values = pybamm.lead_acid.BaseModel().default_parameter_values diff --git a/tests/integration/test_models/test_submodels/test_interface/test_lithium_ion.py b/tests/integration/test_models/test_submodels/test_interface/test_lithium_ion.py index d28e55c485..e9038489d9 100644 --- a/tests/integration/test_models/test_submodels/test_interface/test_lithium_ion.py +++ b/tests/integration/test_models/test_submodels/test_interface/test_lithium_ion.py @@ -36,7 +36,7 @@ def tearDown(self): del self.c_s_p_surf def test_creation_lithium_ion(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() model_n = pybamm.interface.BaseInterface(param, "Negative", "lithium-ion main") j0_n = model_n._get_exchange_current_density(self.variables) model_p = pybamm.interface.BaseInterface(param, "Positive", "lithium-ion main") @@ -45,7 +45,7 @@ def test_creation_lithium_ion(self): self.assertEqual(j0_p.domain, ["positive electrode"]) def test_set_parameters_lithium_ion(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() model_n = pybamm.interface.BaseInterface(param, "Negative", "lithium-ion main") j0_n = model_n._get_exchange_current_density(self.variables) model_p = pybamm.interface.BaseInterface(param, "Positive", "lithium-ion main") @@ -61,7 +61,7 @@ def test_set_parameters_lithium_ion(self): self.assertNotIsInstance(x, pybamm.Parameter) def test_discretisation_lithium_ion(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() model_n = pybamm.interface.BaseInterface(param, "Negative", "lithium-ion main") j0_n = model_n._get_exchange_current_density(self.variables) model_p = pybamm.interface.BaseInterface(param, "Positive", "lithium-ion main") diff --git a/tests/unit/test_expression_tree/test_symbol.py b/tests/unit/test_expression_tree/test_symbol.py index 9a94ff97af..703ee7f009 100644 --- a/tests/unit/test_expression_tree/test_symbol.py +++ b/tests/unit/test_expression_tree/test_symbol.py @@ -285,7 +285,7 @@ def test_symbol_repr(self): def test_symbol_visualise(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() zero_n = pybamm.FullBroadcast(0, ["negative electrode"], "current collector") zero_s = pybamm.FullBroadcast(0, ["separator"], "current collector") diff --git a/tests/unit/test_expression_tree/test_unary_operators.py b/tests/unit/test_expression_tree/test_unary_operators.py index 3ba42e9325..38d88a4125 100644 --- a/tests/unit/test_expression_tree/test_unary_operators.py +++ b/tests/unit/test_expression_tree/test_unary_operators.py @@ -397,6 +397,10 @@ def test_x_average(self): pybamm.x_average(symbol_on_edges) # Particle domains + geo = pybamm.GeometricParameters() + l_n = geo.l_n + l_p = geo.l_p + a = pybamm.Symbol( "a", domain="negative particle", @@ -406,7 +410,7 @@ def test_x_average(self): self.assertEqual(a.domain, ["negative particle"]) self.assertIsInstance(av_a, pybamm.Division) self.assertIsInstance(av_a.children[0], pybamm.Integral) - self.assertEqual(av_a.children[1].id, pybamm.geometric_parameters.l_n.id) + self.assertEqual(av_a.children[1].id, l_n.id) a = pybamm.Symbol( "a", @@ -417,7 +421,7 @@ def test_x_average(self): self.assertEqual(a.domain, ["positive particle"]) self.assertIsInstance(av_a, pybamm.Division) self.assertIsInstance(av_a.children[0], pybamm.Integral) - self.assertEqual(av_a.children[1].id, pybamm.geometric_parameters.l_p.id) + self.assertEqual(av_a.children[1].id, l_p.id) def test_r_average(self): a = pybamm.Scalar(1) diff --git a/tests/unit/test_geometry/test_battery_geometry.py b/tests/unit/test_geometry/test_battery_geometry.py index 7ddbadc596..cf607447fc 100644 --- a/tests/unit/test_geometry/test_battery_geometry.py +++ b/tests/unit/test_geometry/test_battery_geometry.py @@ -17,6 +17,7 @@ def test_geometry_keys(self): def test_geometry(self): var = pybamm.standard_spatial_vars + geo = pybamm.GeometricParameters() for cc_dimension in [0, 1, 2]: geometry = pybamm.battery_geometry(current_collector_dimension=cc_dimension) self.assertIsInstance(geometry, pybamm.Geometry) @@ -24,8 +25,7 @@ def test_geometry(self): self.assertIn("negative particle", geometry) self.assertEqual(geometry["negative electrode"][var.x_n]["min"], 0) self.assertEqual( - geometry["negative electrode"][var.x_n]["max"].id, - pybamm.geometric_parameters.l_n.id, + geometry["negative electrode"][var.x_n]["max"].id, geo.l_n.id ) if cc_dimension == 1: self.assertIn("tabs", geometry["current collector"]) @@ -38,17 +38,18 @@ class TestReadParameters(unittest.TestCase): # This is the most complicated geometry and should test the parameters are # all returned for the deepest dict def test_read_parameters(self): - L_n = pybamm.geometric_parameters.L_n - L_s = pybamm.geometric_parameters.L_s - L_p = pybamm.geometric_parameters.L_p - L_y = pybamm.geometric_parameters.L_y - L_z = pybamm.geometric_parameters.L_z - tab_n_y = pybamm.geometric_parameters.Centre_y_tab_n - tab_n_z = pybamm.geometric_parameters.Centre_z_tab_n - L_tab_n = pybamm.geometric_parameters.L_tab_n - tab_p_y = pybamm.geometric_parameters.Centre_y_tab_p - tab_p_z = pybamm.geometric_parameters.Centre_z_tab_p - L_tab_p = pybamm.geometric_parameters.L_tab_p + geo = pybamm.GeometricParameters() + L_n = geo.L_n + L_s = geo.L_s + L_p = geo.L_p + L_y = geo.L_y + L_z = geo.L_z + tab_n_y = geo.Centre_y_tab_n + tab_n_z = geo.Centre_z_tab_n + L_tab_n = geo.L_tab_n + tab_p_y = geo.Centre_y_tab_p + tab_p_z = geo.Centre_z_tab_p + L_tab_p = geo.L_tab_p geometry = pybamm.battery_geometry(current_collector_dimension=2) diff --git a/tests/unit/test_models/test_submodels/test_convection/test_base_convection.py b/tests/unit/test_models/test_submodels/test_convection/test_base_convection.py index 1dcd8604a1..8e96e496a1 100644 --- a/tests/unit/test_models/test_submodels/test_convection/test_base_convection.py +++ b/tests/unit/test_models/test_submodels/test_convection/test_base_convection.py @@ -9,7 +9,7 @@ class TestBaseModel(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lead_acid + param = pybamm.LeadAcidParameters() submodel = pybamm.convection.through_cell.BaseThroughCellModel(param) a = pybamm.PrimaryBroadcast(0, "current collector") a_n = pybamm.PrimaryBroadcast(0, "negative electrode") diff --git a/tests/unit/test_models/test_submodels/test_convection/test_explicit_convection.py b/tests/unit/test_models/test_submodels/test_convection/test_explicit_convection.py index 42f2fe2f72..43c9aca466 100644 --- a/tests/unit/test_models/test_submodels/test_convection/test_explicit_convection.py +++ b/tests/unit/test_models/test_submodels/test_convection/test_explicit_convection.py @@ -9,7 +9,7 @@ class TestExplicit(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lead_acid + param = pybamm.LeadAcidParameters() a = pybamm.PrimaryBroadcast(0, "current collector") a_n = pybamm.PrimaryBroadcast(a, ["negative electrode"]) diff --git a/tests/unit/test_models/test_submodels/test_convection/test_full_convection.py b/tests/unit/test_models/test_submodels/test_convection/test_full_convection.py index 437a08f3c1..5c5f9d8153 100644 --- a/tests/unit/test_models/test_submodels/test_convection/test_full_convection.py +++ b/tests/unit/test_models/test_submodels/test_convection/test_full_convection.py @@ -9,7 +9,7 @@ class TestFull(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lead_acid + param = pybamm.LeadAcidParameters() a = pybamm.Scalar(0) variables = { diff --git a/tests/unit/test_models/test_submodels/test_convection/test_no_convection.py b/tests/unit/test_models/test_submodels/test_convection/test_no_convection.py index 0a94c0b120..217e0b51c1 100644 --- a/tests/unit/test_models/test_submodels/test_convection/test_no_convection.py +++ b/tests/unit/test_models/test_submodels/test_convection/test_no_convection.py @@ -9,7 +9,7 @@ class TestNoConvectionModel(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lead_acid + param = pybamm.LeadAcidParameters() a = pybamm.Scalar(0) a_s = pybamm.PrimaryBroadcast(a, "separator") diff --git a/tests/unit/test_models/test_submodels/test_current_collector/test_composite_potential_pair.py b/tests/unit/test_models/test_submodels/test_current_collector/test_composite_potential_pair.py index 8648edf21c..8bf216e65e 100644 --- a/tests/unit/test_models/test_submodels/test_current_collector/test_composite_potential_pair.py +++ b/tests/unit/test_models/test_submodels/test_current_collector/test_composite_potential_pair.py @@ -9,7 +9,7 @@ class TestBaseModel(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() submodel = pybamm.current_collector.CompositePotentialPair1plus1D(param) variables = { "Positive current collector potential": pybamm.PrimaryBroadcast( diff --git a/tests/unit/test_models/test_submodels/test_current_collector/test_homogeneous_current_collector.py b/tests/unit/test_models/test_submodels/test_current_collector/test_homogeneous_current_collector.py index 7fa012b450..d19ac1b048 100644 --- a/tests/unit/test_models/test_submodels/test_current_collector/test_homogeneous_current_collector.py +++ b/tests/unit/test_models/test_submodels/test_current_collector/test_homogeneous_current_collector.py @@ -9,7 +9,7 @@ class TestUniformModel(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() submodel = pybamm.current_collector.Uniform(param) variables = { diff --git a/tests/unit/test_models/test_submodels/test_current_collector/test_potential_pair.py b/tests/unit/test_models/test_submodels/test_current_collector/test_potential_pair.py index e28275ca08..b4179e57de 100644 --- a/tests/unit/test_models/test_submodels/test_current_collector/test_potential_pair.py +++ b/tests/unit/test_models/test_submodels/test_current_collector/test_potential_pair.py @@ -9,7 +9,7 @@ class TestBaseModel(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() variables = { "Positive current collector potential": pybamm.PrimaryBroadcast( 0, "current collector" diff --git a/tests/unit/test_models/test_submodels/test_electrode/test_ohm/test_composite_ohm.py b/tests/unit/test_models/test_submodels/test_electrode/test_ohm/test_composite_ohm.py index 968110157d..1030f9cd6c 100644 --- a/tests/unit/test_models/test_submodels/test_electrode/test_ohm/test_composite_ohm.py +++ b/tests/unit/test_models/test_submodels/test_electrode/test_ohm/test_composite_ohm.py @@ -9,7 +9,7 @@ class TestComposite(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lead_acid + param = pybamm.LeadAcidParameters() a = pybamm.Scalar(0) variables = { diff --git a/tests/unit/test_models/test_submodels/test_electrode/test_ohm/test_full_ohm.py b/tests/unit/test_models/test_submodels/test_electrode/test_ohm/test_full_ohm.py index 482f29cd66..8d588a17a7 100644 --- a/tests/unit/test_models/test_submodels/test_electrode/test_ohm/test_full_ohm.py +++ b/tests/unit/test_models/test_submodels/test_electrode/test_ohm/test_full_ohm.py @@ -9,7 +9,7 @@ class TestFull(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lead_acid + param = pybamm.LeadAcidParameters() a = pybamm.Scalar(0) variables = { diff --git a/tests/unit/test_models/test_submodels/test_electrode/test_ohm/test_leading_ohm.py b/tests/unit/test_models/test_submodels/test_electrode/test_ohm/test_leading_ohm.py index a477003b77..1101a39ebf 100644 --- a/tests/unit/test_models/test_submodels/test_electrode/test_ohm/test_leading_ohm.py +++ b/tests/unit/test_models/test_submodels/test_electrode/test_ohm/test_leading_ohm.py @@ -9,7 +9,7 @@ class TestLeadingOrder(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lead_acid + param = pybamm.LeadAcidParameters() a = pybamm.Scalar(0) variables = { diff --git a/tests/unit/test_models/test_submodels/test_electrode/test_ohm/test_surface_form_ohm.py b/tests/unit/test_models/test_submodels/test_electrode/test_ohm/test_surface_form_ohm.py index e899441e29..bc6f54e554 100644 --- a/tests/unit/test_models/test_submodels/test_electrode/test_ohm/test_surface_form_ohm.py +++ b/tests/unit/test_models/test_submodels/test_electrode/test_ohm/test_surface_form_ohm.py @@ -9,7 +9,7 @@ class TestSurfaceForm(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lead_acid + param = pybamm.LeadAcidParameters() a = pybamm.Scalar(0) variables = { diff --git a/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_composite_conductivity.py b/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_composite_conductivity.py index 5cf5c432d6..807ae68472 100644 --- a/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_composite_conductivity.py +++ b/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_composite_conductivity.py @@ -9,7 +9,7 @@ class TestComposite(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() a = pybamm.PrimaryBroadcast(0, "current collector") c_e_n = pybamm.standard_variables.c_e_n c_e_s = pybamm.standard_variables.c_e_s @@ -35,7 +35,7 @@ def test_public_functions(self): std_tests.test_all() def test_public_functions_first_order(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() a = pybamm.PrimaryBroadcast(0, "current collector") c_e_n = pybamm.standard_variables.c_e_n c_e_s = pybamm.standard_variables.c_e_s diff --git a/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_full_conductivity.py b/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_full_conductivity.py index a665ec08b5..47cda3d45d 100644 --- a/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_full_conductivity.py +++ b/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_full_conductivity.py @@ -9,7 +9,7 @@ class TestFull(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() a = pybamm.Scalar(0) variables = { "Electrolyte tortuosity": a, diff --git a/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_leading_order_conductivity.py b/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_leading_order_conductivity.py index 7d27313b2c..f0f153d51b 100644 --- a/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_leading_order_conductivity.py +++ b/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_leading_order_conductivity.py @@ -9,7 +9,7 @@ class TestLeadingOrder(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() a = pybamm.Scalar(0) variables = { "Current collector current density": a, diff --git a/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_surface_form/test_full_surface_form_conductivity.py b/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_surface_form/test_full_surface_form_conductivity.py index dc02c1c822..20a5ca4811 100644 --- a/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_surface_form/test_full_surface_form_conductivity.py +++ b/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_surface_form/test_full_surface_form_conductivity.py @@ -9,7 +9,7 @@ class TestFull(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() a = pybamm.Scalar(0) a_n = pybamm.FullBroadcast( pybamm.Scalar(0), ["negative electrode"], "current collector" diff --git a/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_surface_form/test_leading_surface_form_conductivity.py b/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_surface_form/test_leading_surface_form_conductivity.py index cf74f2d760..7f2de31c53 100644 --- a/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_surface_form/test_leading_surface_form_conductivity.py +++ b/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_surface_form/test_leading_surface_form_conductivity.py @@ -9,7 +9,7 @@ class TestLeadingOrderModel(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() a = pybamm.Scalar(0) a_n = pybamm.FullBroadcast( pybamm.Scalar(0), ["negative electrode"], "current collector" diff --git a/tests/unit/test_models/test_submodels/test_electrolyte_diffusion/test_constant_concentration.py b/tests/unit/test_models/test_submodels/test_electrolyte_diffusion/test_constant_concentration.py index ee1819d610..56765201c7 100644 --- a/tests/unit/test_models/test_submodels/test_electrolyte_diffusion/test_constant_concentration.py +++ b/tests/unit/test_models/test_submodels/test_electrolyte_diffusion/test_constant_concentration.py @@ -9,7 +9,7 @@ class TestConstantConcentration(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() submodel = pybamm.electrolyte_diffusion.ConstantConcentration( param ) diff --git a/tests/unit/test_models/test_submodels/test_electrolyte_diffusion/test_full_diffusion.py b/tests/unit/test_models/test_submodels/test_electrolyte_diffusion/test_full_diffusion.py index eb4d1f6bfc..65685a097d 100644 --- a/tests/unit/test_models/test_submodels/test_electrolyte_diffusion/test_full_diffusion.py +++ b/tests/unit/test_models/test_submodels/test_electrolyte_diffusion/test_full_diffusion.py @@ -9,7 +9,7 @@ class TestFull(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() a = pybamm.Scalar(0) full = pybamm.FullBroadcast( a, diff --git a/tests/unit/test_models/test_submodels/test_electrolyte_diffusion/test_leading_order_diffusion.py b/tests/unit/test_models/test_submodels/test_electrolyte_diffusion/test_leading_order_diffusion.py index 82fb1bdaec..fc442147c8 100644 --- a/tests/unit/test_models/test_submodels/test_electrolyte_diffusion/test_leading_order_diffusion.py +++ b/tests/unit/test_models/test_submodels/test_electrolyte_diffusion/test_leading_order_diffusion.py @@ -9,7 +9,7 @@ class TestLeadingOrder(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lead_acid + param = pybamm.LeadAcidParameters() a = pybamm.Scalar(0) variables = { "X-averaged negative electrode porosity": a, diff --git a/tests/unit/test_models/test_submodels/test_external_circuit/test_current_control.py b/tests/unit/test_models/test_submodels/test_external_circuit/test_current_control.py index c2dc1c19de..ceb41e62b6 100644 --- a/tests/unit/test_models/test_submodels/test_external_circuit/test_current_control.py +++ b/tests/unit/test_models/test_submodels/test_external_circuit/test_current_control.py @@ -9,7 +9,7 @@ class TestCurrentControl(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() submodel = pybamm.external_circuit.CurrentControl(param) std_tests = tests.StandardSubModelTests(submodel) std_tests.test_all() diff --git a/tests/unit/test_models/test_submodels/test_external_circuit/test_function_control.py b/tests/unit/test_models/test_submodels/test_external_circuit/test_function_control.py index 2315fac9ee..9cfd51cf99 100644 --- a/tests/unit/test_models/test_submodels/test_external_circuit/test_function_control.py +++ b/tests/unit/test_models/test_submodels/test_external_circuit/test_function_control.py @@ -9,19 +9,21 @@ def external_circuit_function(variables): I = variables["Current [A]"] V = variables["Terminal voltage [V]"] + liion_param = pybamm.LithiumIonParameters() + return ( V + I - pybamm.FunctionParameter( "Current plus voltage function", - {"Time [s]": pybamm.t * pybamm.standard_parameters_lithium_ion.timescale}, + {"Time [s]": pybamm.t * liion_param.timescale}, ) ) class TestFunctionControl(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() submodel = pybamm.external_circuit.FunctionControl( param, external_circuit_function ) diff --git a/tests/unit/test_models/test_submodels/test_external_circuit/test_power_control.py b/tests/unit/test_models/test_submodels/test_external_circuit/test_power_control.py index 414970a815..a6743ea2ff 100644 --- a/tests/unit/test_models/test_submodels/test_external_circuit/test_power_control.py +++ b/tests/unit/test_models/test_submodels/test_external_circuit/test_power_control.py @@ -9,7 +9,7 @@ class TestPowerControl(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() submodel = pybamm.external_circuit.PowerFunctionControl(param) variables = {"Terminal voltage [V]": pybamm.Scalar(0)} std_tests = tests.StandardSubModelTests(submodel, variables) diff --git a/tests/unit/test_models/test_submodels/test_interface/test_diffusion_limited.py b/tests/unit/test_models/test_submodels/test_interface/test_diffusion_limited.py index 867a4276ce..dd89e50c39 100644 --- a/tests/unit/test_models/test_submodels/test_interface/test_diffusion_limited.py +++ b/tests/unit/test_models/test_submodels/test_interface/test_diffusion_limited.py @@ -9,7 +9,7 @@ class TestBaseModel(unittest.TestCase): def test_public_function(self): - param = pybamm.standard_parameters_lead_acid + param = pybamm.LeadAcidParameters() a_n = pybamm.PrimaryBroadcast(pybamm.Scalar(0), ["negative electrode"]) a_s = pybamm.PrimaryBroadcast(pybamm.Scalar(0), ["separator"]) diff --git a/tests/unit/test_models/test_submodels/test_interface/test_inverse_kinetics/test_inverse_butler_volmer.py b/tests/unit/test_models/test_submodels/test_interface/test_inverse_kinetics/test_inverse_butler_volmer.py index fb0128b0b6..ff6f2ac3d1 100644 --- a/tests/unit/test_models/test_submodels/test_interface/test_inverse_kinetics/test_inverse_butler_volmer.py +++ b/tests/unit/test_models/test_submodels/test_interface/test_inverse_kinetics/test_inverse_butler_volmer.py @@ -9,7 +9,7 @@ class TestBaseModel(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() a = pybamm.Scalar(0) variables = { diff --git a/tests/unit/test_models/test_submodels/test_interface/test_kinetics/test_butler_volmer.py b/tests/unit/test_models/test_submodels/test_interface/test_kinetics/test_butler_volmer.py index 377d527498..d053e9feaf 100644 --- a/tests/unit/test_models/test_submodels/test_interface/test_kinetics/test_butler_volmer.py +++ b/tests/unit/test_models/test_submodels/test_interface/test_kinetics/test_butler_volmer.py @@ -9,7 +9,7 @@ class TestButlerVolmer(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() a_n = pybamm.FullBroadcast( pybamm.Scalar(0), ["negative electrode"], "current collector" diff --git a/tests/unit/test_models/test_submodels/test_interface/test_kinetics/test_tafel.py b/tests/unit/test_models/test_submodels/test_interface/test_kinetics/test_tafel.py index 2eacdf6be5..565d98d709 100644 --- a/tests/unit/test_models/test_submodels/test_interface/test_kinetics/test_tafel.py +++ b/tests/unit/test_models/test_submodels/test_interface/test_kinetics/test_tafel.py @@ -9,7 +9,7 @@ class TestTafel(unittest.TestCase): def test_public_function(self): # Test forward Tafel on the negative - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() a_n = pybamm.FullBroadcast( pybamm.Scalar(0), ["negative electrode"], "current collector" diff --git a/tests/unit/test_models/test_submodels/test_interface/test_lead_acid.py b/tests/unit/test_models/test_submodels/test_interface/test_lead_acid.py index 21abca6ac3..ba57b49ad3 100644 --- a/tests/unit/test_models/test_submodels/test_interface/test_lead_acid.py +++ b/tests/unit/test_models/test_submodels/test_interface/test_lead_acid.py @@ -9,7 +9,7 @@ class TestLeadAcid(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lead_acid + param = pybamm.LeadAcidParameters() a_n = pybamm.FullBroadcast( pybamm.Scalar(0), ["negative electrode"], "current collector" diff --git a/tests/unit/test_models/test_submodels/test_interface/test_lithium_ion.py b/tests/unit/test_models/test_submodels/test_interface/test_lithium_ion.py index d7524a0575..7fb5ce7183 100644 --- a/tests/unit/test_models/test_submodels/test_interface/test_lithium_ion.py +++ b/tests/unit/test_models/test_submodels/test_interface/test_lithium_ion.py @@ -9,7 +9,7 @@ class TestLithiumIon(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() a_n = pybamm.FullBroadcast( pybamm.Scalar(0), ["negative electrode"], "current collector" diff --git a/tests/unit/test_models/test_submodels/test_oxygen_diffusion/test_full_oxygen_diffusion.py b/tests/unit/test_models/test_submodels/test_oxygen_diffusion/test_full_oxygen_diffusion.py index 83873e804f..502ad6798d 100644 --- a/tests/unit/test_models/test_submodels/test_oxygen_diffusion/test_full_oxygen_diffusion.py +++ b/tests/unit/test_models/test_submodels/test_oxygen_diffusion/test_full_oxygen_diffusion.py @@ -9,7 +9,7 @@ class TestFull(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lead_acid + param = pybamm.LeadAcidParameters() a = pybamm.Scalar(0) variables = { "Porosity": pybamm.Concatenation( diff --git a/tests/unit/test_models/test_submodels/test_oxygen_diffusion/test_leading_oxygen_diffusion.py b/tests/unit/test_models/test_submodels/test_oxygen_diffusion/test_leading_oxygen_diffusion.py index 35c2917851..45e6f824e8 100644 --- a/tests/unit/test_models/test_submodels/test_oxygen_diffusion/test_leading_oxygen_diffusion.py +++ b/tests/unit/test_models/test_submodels/test_oxygen_diffusion/test_leading_oxygen_diffusion.py @@ -9,7 +9,7 @@ class TestLeadingOrder(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lead_acid + param = pybamm.LeadAcidParameters() a = pybamm.Scalar(0) variables = { "X-averaged negative electrode porosity": a, diff --git a/tests/unit/test_models/test_submodels/test_oxygen_diffusion/test_no_oxygen.py b/tests/unit/test_models/test_submodels/test_oxygen_diffusion/test_no_oxygen.py index 20f02ab142..e2279afbd2 100644 --- a/tests/unit/test_models/test_submodels/test_oxygen_diffusion/test_no_oxygen.py +++ b/tests/unit/test_models/test_submodels/test_oxygen_diffusion/test_no_oxygen.py @@ -9,7 +9,7 @@ class TestConstantConcentration(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lead_acid + param = pybamm.LeadAcidParameters() submodel = pybamm.oxygen_diffusion.NoOxygen(param) std_tests = tests.StandardSubModelTests(submodel) std_tests.test_all() diff --git a/tests/unit/test_models/test_submodels/test_particle/test_fast_many_particles.py b/tests/unit/test_models/test_submodels/test_particle/test_fast_many_particles.py index de87f3ca0e..c403718cc0 100644 --- a/tests/unit/test_models/test_submodels/test_particle/test_fast_many_particles.py +++ b/tests/unit/test_models/test_submodels/test_particle/test_fast_many_particles.py @@ -9,7 +9,7 @@ class TestManyParticles(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() a_n = pybamm.FullBroadcast( pybamm.Scalar(0), "negative electrode", {"secondary": "current collector"} diff --git a/tests/unit/test_models/test_submodels/test_particle/test_fast_single_particle.py b/tests/unit/test_models/test_submodels/test_particle/test_fast_single_particle.py index 28677bc9e6..a538547def 100644 --- a/tests/unit/test_models/test_submodels/test_particle/test_fast_single_particle.py +++ b/tests/unit/test_models/test_submodels/test_particle/test_fast_single_particle.py @@ -9,7 +9,7 @@ class TestSingleParticle(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() a = pybamm.PrimaryBroadcast(pybamm.Scalar(0), "current collector") variables = {"X-averaged negative electrode interfacial current density": a} diff --git a/tests/unit/test_models/test_submodels/test_particle/test_fickian_many_particles.py b/tests/unit/test_models/test_submodels/test_particle/test_fickian_many_particles.py index 8953203915..3ba0c71d9e 100644 --- a/tests/unit/test_models/test_submodels/test_particle/test_fickian_many_particles.py +++ b/tests/unit/test_models/test_submodels/test_particle/test_fickian_many_particles.py @@ -9,7 +9,7 @@ class TestManyParticles(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() a_n = pybamm.FullBroadcast( pybamm.Scalar(0), "negative electrode", {"secondary": "current collector"} diff --git a/tests/unit/test_models/test_submodels/test_particle/test_fickian_single_particle.py b/tests/unit/test_models/test_submodels/test_particle/test_fickian_single_particle.py index fbf27e700c..8d5292daa6 100644 --- a/tests/unit/test_models/test_submodels/test_particle/test_fickian_single_particle.py +++ b/tests/unit/test_models/test_submodels/test_particle/test_fickian_single_particle.py @@ -9,7 +9,7 @@ class TestSingleParticle(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() a = pybamm.PrimaryBroadcast(pybamm.Scalar(0), "current collector") variables = { diff --git a/tests/unit/test_models/test_submodels/test_porosity/test_constant_porosity.py b/tests/unit/test_models/test_submodels/test_porosity/test_constant_porosity.py index 85afcc924b..7e19a8250b 100644 --- a/tests/unit/test_models/test_submodels/test_porosity/test_constant_porosity.py +++ b/tests/unit/test_models/test_submodels/test_porosity/test_constant_porosity.py @@ -9,7 +9,7 @@ class TestConstantPorosity(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() submodel = pybamm.porosity.Constant(param) std_tests = tests.StandardSubModelTests(submodel) std_tests.test_all() diff --git a/tests/unit/test_models/test_submodels/test_porosity/test_full_reaction_driven_porosity.py b/tests/unit/test_models/test_submodels/test_porosity/test_full_reaction_driven_porosity.py index aba8b0895a..b95e76a693 100644 --- a/tests/unit/test_models/test_submodels/test_porosity/test_full_reaction_driven_porosity.py +++ b/tests/unit/test_models/test_submodels/test_porosity/test_full_reaction_driven_porosity.py @@ -9,7 +9,7 @@ class TestFull(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lead_acid + param = pybamm.LeadAcidParameters() a_n = pybamm.PrimaryBroadcast(pybamm.Scalar(0), ["negative electrode"]) a_p = pybamm.PrimaryBroadcast(pybamm.Scalar(0), ["positive electrode"]) variables = { diff --git a/tests/unit/test_models/test_submodels/test_porosity/test_leading_reaction_driven_porosity.py b/tests/unit/test_models/test_submodels/test_porosity/test_leading_reaction_driven_porosity.py index ca4a60a131..a2b0cb0b3e 100644 --- a/tests/unit/test_models/test_submodels/test_porosity/test_leading_reaction_driven_porosity.py +++ b/tests/unit/test_models/test_submodels/test_porosity/test_leading_reaction_driven_porosity.py @@ -9,7 +9,7 @@ class TestLeadingOrder(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lead_acid + param = pybamm.LeadAcidParameters() a = pybamm.PrimaryBroadcast(pybamm.Scalar(0), "current collector") variables = { "X-averaged negative electrode interfacial current density": a, diff --git a/tests/unit/test_models/test_submodels/test_thermal/test_base_thermal.py b/tests/unit/test_models/test_submodels/test_thermal/test_base_thermal.py index 416c286aa1..5226c67fa9 100644 --- a/tests/unit/test_models/test_submodels/test_thermal/test_base_thermal.py +++ b/tests/unit/test_models/test_submodels/test_thermal/test_base_thermal.py @@ -9,7 +9,7 @@ class TestBaseThermal(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() submodel = pybamm.thermal.BaseThermal(param) std_tests = tests.StandardSubModelTests(submodel) std_tests.test_all() diff --git a/tests/unit/test_models/test_submodels/test_thermal/test_isothermal.py b/tests/unit/test_models/test_submodels/test_thermal/test_isothermal.py index 6194703fbb..3449c82e97 100644 --- a/tests/unit/test_models/test_submodels/test_thermal/test_isothermal.py +++ b/tests/unit/test_models/test_submodels/test_thermal/test_isothermal.py @@ -9,7 +9,7 @@ class TestIsothermal(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() submodel = pybamm.thermal.isothermal.Isothermal(param) std_tests = tests.StandardSubModelTests(submodel) std_tests.test_all() diff --git a/tests/unit/test_models/test_submodels/test_thermal/test_lumped.py b/tests/unit/test_models/test_submodels/test_thermal/test_lumped.py index 7e9cf42c29..dbc8f0b24c 100644 --- a/tests/unit/test_models/test_submodels/test_thermal/test_lumped.py +++ b/tests/unit/test_models/test_submodels/test_thermal/test_lumped.py @@ -13,7 +13,7 @@ class TestLumped(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() submodel = pybamm.thermal.Lumped(param) std_tests = tests.StandardSubModelTests(submodel, coupled_variables) diff --git a/tests/unit/test_models/test_submodels/test_thermal/test_pouch_cell/test_pouch_1plus1D.py b/tests/unit/test_models/test_submodels/test_thermal/test_pouch_cell/test_pouch_1plus1D.py index cff13ad63e..c3e03a8290 100644 --- a/tests/unit/test_models/test_submodels/test_thermal/test_pouch_cell/test_pouch_1plus1D.py +++ b/tests/unit/test_models/test_submodels/test_thermal/test_pouch_cell/test_pouch_1plus1D.py @@ -13,7 +13,7 @@ class TestPouchCell1D(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() submodel = pybamm.thermal.pouch_cell.CurrentCollector1D(param) std_tests = tests.StandardSubModelTests(submodel, coupled_variables) std_tests.test_all() diff --git a/tests/unit/test_models/test_submodels/test_thermal/test_pouch_cell/test_pouch_2plus1D.py b/tests/unit/test_models/test_submodels/test_thermal/test_pouch_cell/test_pouch_2plus1D.py index d202d5f643..6bab47f332 100644 --- a/tests/unit/test_models/test_submodels/test_thermal/test_pouch_cell/test_pouch_2plus1D.py +++ b/tests/unit/test_models/test_submodels/test_thermal/test_pouch_cell/test_pouch_2plus1D.py @@ -13,7 +13,7 @@ class TestPouchCell2D(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() submodel = pybamm.thermal.pouch_cell.CurrentCollector2D(param) std_tests = tests.StandardSubModelTests(submodel, coupled_variables) std_tests.test_all() diff --git a/tests/unit/test_models/test_submodels/test_thermal/test_x_full.py b/tests/unit/test_models/test_submodels/test_thermal/test_x_full.py index 83129bc6fb..b738707ca7 100644 --- a/tests/unit/test_models/test_submodels/test_thermal/test_x_full.py +++ b/tests/unit/test_models/test_submodels/test_thermal/test_x_full.py @@ -13,7 +13,7 @@ class TestOneDimensionalX(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() submodel = pybamm.thermal.OneDimensionalX(param) std_tests = tests.StandardSubModelTests(submodel, coupled_variables) std_tests.test_all() diff --git a/tests/unit/test_models/test_submodels/test_tortuosity/test_bruggeman_tortuosity.py b/tests/unit/test_models/test_submodels/test_tortuosity/test_bruggeman_tortuosity.py index eed98a060b..1f2ca9030c 100644 --- a/tests/unit/test_models/test_submodels/test_tortuosity/test_bruggeman_tortuosity.py +++ b/tests/unit/test_models/test_submodels/test_tortuosity/test_bruggeman_tortuosity.py @@ -9,7 +9,7 @@ class TestBruggeman(unittest.TestCase): def test_public_functions(self): - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() a = pybamm.Concatenation( pybamm.FullBroadcast(0, ["negative electrode"], "current collector"), pybamm.FullBroadcast(0, ["separator"], "current collector"), diff --git a/tests/unit/test_parameters/test_current_functions.py b/tests/unit/test_parameters/test_current_functions.py index f70459c5ca..b640842d69 100644 --- a/tests/unit/test_parameters/test_current_functions.py +++ b/tests/unit/test_parameters/test_current_functions.py @@ -10,7 +10,8 @@ class TestCurrentFunctions(unittest.TestCase): def test_constant_current(self): # test simplify - current = pybamm.electrical_parameters.current_with_time + param = pybamm.ElectricalParameters() + current = param.current_with_time parameter_values = pybamm.ParameterValues( { "Typical current [A]": 2, @@ -23,7 +24,8 @@ def test_constant_current(self): def test_get_current_data(self): # test process parameters - dimensional_current = pybamm.electrical_parameters.dimensional_current_with_time + param = pybamm.ElectricalParameters() + dimensional_current = param.dimensional_current_with_time parameter_values = pybamm.ParameterValues( { "Typical current [A]": 2, @@ -45,7 +47,8 @@ def my_fun(t, A, omega): return A * pybamm.sin(2 * np.pi * omega * t) # choose amplitude and frequency - A = pybamm.electrical_parameters.I_typ + param = pybamm.ElectricalParameters() + A = param.I_typ omega = pybamm.Parameter("omega") def current(t): @@ -60,7 +63,7 @@ def current(t): "Current function [A]": current, } ) - dimensional_current = pybamm.electrical_parameters.dimensional_current_with_time + dimensional_current = param.dimensional_current_with_time dimensional_current_eval = parameter_values.process_symbol(dimensional_current) def user_current(t): diff --git a/tests/unit/test_parameters/test_dimensionless_parameter_values_lithium_ion.py b/tests/unit/test_parameters/test_dimensionless_parameter_values_lithium_ion.py index 183124b211..84516b0109 100644 --- a/tests/unit/test_parameters/test_dimensionless_parameter_values_lithium_ion.py +++ b/tests/unit/test_parameters/test_dimensionless_parameter_values_lithium_ion.py @@ -14,7 +14,7 @@ def test_lithium_ion(self): are those converted from those in Scott's transfer which previous versions of the DFN work with. A 1C rate corresponds to a 24A/m^2 current density""" values = pybamm.lithium_ion.BaseModel().default_parameter_values - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() c_rate = param.i_typ / 24 # roughly for the numbers I used before @@ -152,7 +152,7 @@ def test_lithium_ion(self): def test_thermal_parameters(self): values = pybamm.lithium_ion.BaseModel().default_parameter_values - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() c_rate = param.i_typ / 24 # Density @@ -190,7 +190,7 @@ def test_thermal_parameters(self): # values.evaluate(param.tau_th_yz), 1.4762 * 10 ** (3), 2 # ) - # thermal = pybamm.thermal_parameters + # thermal = pybamm.ThermalParameters() # np.testing.assert_almost_equal( # values.evaluate(thermal.rho_eff_dim), 1.8116 * 10 ** (6), 2 # ) @@ -200,7 +200,7 @@ def test_thermal_parameters(self): def test_parameter_functions(self): values = pybamm.lithium_ion.BaseModel().default_parameter_values - param = pybamm.standard_parameters_lithium_ion + param = pybamm.LithiumIonParameters() c_test = pybamm.Scalar(0.5) T_test = pybamm.Scalar(0) diff --git a/tests/unit/test_parameters/test_electrical_parameters.py b/tests/unit/test_parameters/test_electrical_parameters.py index c1aaf9259f..0a51f7bb9e 100644 --- a/tests/unit/test_parameters/test_electrical_parameters.py +++ b/tests/unit/test_parameters/test_electrical_parameters.py @@ -9,12 +9,11 @@ class TestElectricalParameters(unittest.TestCase): def test_current_functions(self): # create current functions - dimensional_current = pybamm.electrical_parameters.dimensional_current_with_time - dimensional_current_density = ( - pybamm.electrical_parameters.dimensional_current_density_with_time - ) - dimensionless_current = pybamm.electrical_parameters.current_with_time - dimensionless_current_density = pybamm.electrical_parameters.current_with_time + param = pybamm.ElectricalParameters() + dimensional_current = param.dimensional_current_with_time + dimensional_current_density = param.dimensional_current_density_with_time + dimensionless_current = param.current_with_time + dimensionless_current_density = param.current_with_time # process parameter_values = pybamm.ParameterValues( diff --git a/tests/unit/test_parameters/test_geometric_parameters.py b/tests/unit/test_parameters/test_geometric_parameters.py index 2711c76430..63df878fac 100644 --- a/tests/unit/test_parameters/test_geometric_parameters.py +++ b/tests/unit/test_parameters/test_geometric_parameters.py @@ -8,13 +8,14 @@ class TestGeometricParameters(unittest.TestCase): def test_macroscale_parameters(self): - L_n = pybamm.geometric_parameters.L_n - L_s = pybamm.geometric_parameters.L_s - L_p = pybamm.geometric_parameters.L_p - L_x = pybamm.geometric_parameters.L_x - l_n = pybamm.geometric_parameters.l_n - l_s = pybamm.geometric_parameters.l_s - l_p = pybamm.geometric_parameters.l_p + geo = pybamm.GeometricParameters() + L_n = geo.L_n + L_s = geo.L_s + L_p = geo.L_p + L_x = geo.L_x + l_n = geo.l_n + l_s = geo.l_s + l_p = geo.l_p parameter_values = pybamm.ParameterValues( values={ diff --git a/tests/unit/test_parameters/test_standard_parameters_lead_acid.py b/tests/unit/test_parameters/test_standard_parameters_lead_acid.py index 39d84690d3..787e304f81 100644 --- a/tests/unit/test_parameters/test_standard_parameters_lead_acid.py +++ b/tests/unit/test_parameters/test_standard_parameters_lead_acid.py @@ -9,27 +9,23 @@ class TestStandardParametersLeadAcid(unittest.TestCase): def test_scipy_constants(self): - R = pybamm.standard_parameters_lead_acid.R - self.assertAlmostEqual(R.evaluate(), 8.314, places=3) - F = pybamm.standard_parameters_lead_acid.F - self.assertAlmostEqual(F.evaluate(), 96485, places=0) + param = pybamm.LeadAcidParameters() + self.assertAlmostEqual(param.R.evaluate(), 8.314, places=3) + self.assertAlmostEqual(param.F.evaluate(), 96485, places=0) def test_all_defined(self): - parameters = pybamm.standard_parameters_lead_acid + parameters = pybamm.LeadAcidParameters() parameter_values = pybamm.lead_acid.BaseModel().default_parameter_values output_file = "lead_acid_parameters.txt" parameter_values.print_parameters(parameters, output_file) # test print_parameters with dict and without C-rate del parameter_values["Cell capacity [A.h]"] - parameters = { - "C_e": pybamm.standard_parameters_lead_acid.C_e, - "sigma_n": pybamm.standard_parameters_lead_acid.sigma_n, - } + parameters = {"C_e": parameters.C_e, "sigma_n": parameters.sigma_n} parameter_values.print_parameters(parameters) def test_parameters_defaults_lead_acid(self): # Load parameters to be tested - parameters = pybamm.standard_parameters_lead_acid + parameters = pybamm.LeadAcidParameters() parameter_values = pybamm.lead_acid.BaseModel().default_parameter_values param_eval = parameter_values.print_parameters(parameters) param_eval = {k: v[0] for k, v in param_eval.items()} @@ -50,7 +46,8 @@ def test_parameters_defaults_lead_acid(self): def test_concatenated_parameters(self): # create - s_param = pybamm.standard_parameters_lead_acid.s_plus_S + param = pybamm.LeadAcidParameters() + s_param = param.s_plus_S self.assertIsInstance(s_param, pybamm.Concatenation) self.assertEqual( s_param.domain, ["negative electrode", "separator", "positive electrode"] @@ -71,12 +68,9 @@ def test_concatenated_parameters(self): def test_current_functions(self): # create current functions - dimensional_current_density = ( - pybamm.standard_parameters_lead_acid.dimensional_current_density_with_time - ) - dimensionless_current_density = ( - pybamm.standard_parameters_lead_acid.current_with_time - ) + param = pybamm.LeadAcidParameters() + dimensional_current_density = param.dimensional_current_density_with_time + dimensionless_current_density = param.current_with_time # process parameter_values = pybamm.ParameterValues( @@ -105,27 +99,16 @@ def test_current_functions(self): def test_functions_lead_acid(self): # Load parameters to be tested + param = pybamm.LeadAcidParameters() parameters = { - "D_e_1": pybamm.standard_parameters_lead_acid.D_e( - pybamm.Scalar(1), pybamm.Scalar(0) - ), - "kappa_e_0": pybamm.standard_parameters_lead_acid.kappa_e( - pybamm.Scalar(0), pybamm.Scalar(0) - ), - "chi_1": pybamm.standard_parameters_lead_acid.chi(pybamm.Scalar(1)), - "chi_0.5": pybamm.standard_parameters_lead_acid.chi(pybamm.Scalar(0.5)), - "U_n_1": pybamm.standard_parameters_lead_acid.U_n( - pybamm.Scalar(1), pybamm.Scalar(0) - ), - "U_n_0.5": pybamm.standard_parameters_lead_acid.U_n( - pybamm.Scalar(0.5), pybamm.Scalar(0) - ), - "U_p_1": pybamm.standard_parameters_lead_acid.U_p( - pybamm.Scalar(1), pybamm.Scalar(0) - ), - "U_p_0.5": pybamm.standard_parameters_lead_acid.U_p( - pybamm.Scalar(0.5), pybamm.Scalar(0) - ), + "D_e_1": param.D_e(pybamm.Scalar(1), pybamm.Scalar(0)), + "kappa_e_0": param.kappa_e(pybamm.Scalar(0), pybamm.Scalar(0)), + "chi_1": param.chi(pybamm.Scalar(1)), + "chi_0.5": param.chi(pybamm.Scalar(0.5)), + "U_n_1": param.U_n(pybamm.Scalar(1), pybamm.Scalar(0)), + "U_n_0.5": param.U_n(pybamm.Scalar(0.5), pybamm.Scalar(0)), + "U_p_1": param.U_p(pybamm.Scalar(1), pybamm.Scalar(0)), + "U_p_0.5": param.U_p(pybamm.Scalar(0.5), pybamm.Scalar(0)), } # Process parameter_values = pybamm.ParameterValues( @@ -144,7 +127,7 @@ def test_functions_lead_acid(self): def test_update_initial_state_of_charge(self): # Load parameters to be tested - parameters = pybamm.standard_parameters_lead_acid + parameters = pybamm.LeadAcidParameters() parameter_values = pybamm.lead_acid.BaseModel().default_parameter_values param_eval = parameter_values.print_parameters(parameters) param_eval = {k: v[0] for k, v in param_eval.items()} diff --git a/tests/unit/test_quick_plot.py b/tests/unit/test_quick_plot.py index fde665c3b7..6308864f9f 100644 --- a/tests/unit/test_quick_plot.py +++ b/tests/unit/test_quick_plot.py @@ -295,7 +295,7 @@ def test_loqs_spme(self): t = solution["Time [s]"].entries c_e_var = solution["Electrolyte concentration [mol.m-3]"] # 1D variables should be evaluated on edges - L_x = param.evaluate(pybamm.geometric_parameters.L_x) + L_x = param.evaluate(model.param.L_x) c_e = c_e_var(t=t, x=mesh.combine_submeshes(*c_e_var.domain).edges * L_x) for unit, scale in zip(["seconds", "minutes", "hours"], [1, 60, 3600]): From d518289ed6ec1ec10209876696faa70d16b858fd Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Tue, 28 Jul 2020 17:16:46 +0100 Subject: [PATCH 02/10] #1115 fix unit tests --- pybamm/models/base_model.py | 1 - pybamm/parameters/parameter_values.py | 3 +++ 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py index 8e9184b77c..d693dcb4c2 100644 --- a/pybamm/models/base_model.py +++ b/pybamm/models/base_model.py @@ -1,7 +1,6 @@ # # Base model class # -import inspect import numbers import pybamm import warnings diff --git a/pybamm/parameters/parameter_values.py b/pybamm/parameters/parameter_values.py index 025ec1a488..c97e499277 100644 --- a/pybamm/parameters/parameter_values.py +++ b/pybamm/parameters/parameter_values.py @@ -667,6 +667,9 @@ def print_parameters(self, parameters, output_file=None): "pybamm", "constants", "np", + "geo", + "elec", + "therm", ] # If 'parameters' is a class, extract the dict From f4ab4d3a8213fcc42c6df382c75194d5adf993a0 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Wed, 29 Jul 2020 17:11:56 +0100 Subject: [PATCH 03/10] #1115 automatically compute surface area per unit volume --- basic_test.py | 52 ++++++ examples/notebooks/change-input-current.ipynb | 18 +- .../full_battery_models/base_battery_model.py | 11 ++ .../lithium_ion/basic_dfn.py | 9 +- .../submodels/electrode/ohm/full_ohm.py | 6 +- .../full_conductivity.py | 10 +- .../submodels/interface/base_interface.py | 30 ++- .../submodels/particle/base_particle.py | 4 +- .../submodels/particle/fast_many_particles.py | 16 +- .../particle/fickian_many_particles.py | 23 +-- pybamm/parameters/geometric_parameters.py | 3 + pybamm/parameters/lead_acid_parameters.py | 24 +++ pybamm/parameters/lithium_ion_parameters.py | 171 ++++++++++++++---- .../test_models/standard_output_tests.py | 7 - .../test_full_conductivity.py | 5 + 15 files changed, 303 insertions(+), 86 deletions(-) create mode 100644 basic_test.py diff --git a/basic_test.py b/basic_test.py new file mode 100644 index 0000000000..589b386d24 --- /dev/null +++ b/basic_test.py @@ -0,0 +1,52 @@ +import pybamm + +models = [ + # pybamm.lithium_ion.DFN( + # options={"particle": "fast diffusion"}, name="fastDFN particle" + # ), + pybamm.lithium_ion.DFN(name="DFN particle"), + pybamm.lithium_ion.DFN(name="DFN"), +] +parameters = pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Ecker2015) + + +def negative_distribution(x): + return 1 + x + + +def positive_distribution(x): + return 1 + (x - (1 - models[1].param.l_p)) + + +parameters["Negative particle distribution in x"] = negative_distribution +parameters["Positive particle distribution in x"] = positive_distribution +parameters["Negative electrode diffusivity [m2.s-1]"] = 1e-1 +parameters["Positive electrode diffusivity [m2.s-1]"] = 1e-1 +sols = [] + +for model in models: + if model.name == "DFN": + params = pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Ecker2015) + params["Negative electrode diffusivity [m2.s-1]"] = 1e-1 + params["Positive electrode diffusivity [m2.s-1]"] = 1e-1 + else: + params = parameters + sim = pybamm.Simulation(model, parameter_values=params) + sol = sim.solve([0, 3600]) + sols.append(sol) + + +output_variables = [ + "Negative particle surface concentration", + "Electrolyte concentration", + "Positive particle surface concentration", + "Current [A]", + "Negative electrode potential [V]", + "Electrolyte potential [V]", + "Positive electrode potential [V]", + "Terminal voltage [V]", + "Negative particle distribution in x", + "Positive particle distribution in x", +] + +pybamm.dynamic_plot(sols, output_variables=output_variables) diff --git a/examples/notebooks/change-input-current.ipynb b/examples/notebooks/change-input-current.ipynb index 3bc1a17255..408a4b6b00 100644 --- a/examples/notebooks/change-input-current.ipynb +++ b/examples/notebooks/change-input-current.ipynb @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -75,7 +75,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "807f6dec221b4226aea64326d51c751b", + "model_id": "25ef01f7a8ab4fe0bbcb8a2c7533d5b6", "version_major": 2, "version_minor": 0 }, @@ -124,7 +124,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "eaab57697cef4852b4fbab5ee1f7efd7", + "model_id": "fe35354104aa4417948c367b054f0886", "version_major": 2, "version_minor": 0 }, @@ -162,7 +162,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a72be467b6f84733927cb2e7a5c4b3f9", + "model_id": "2555072f8d654c76a9aed15e2ef6ba2e", "version_major": 2, "version_minor": 0 }, @@ -244,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -257,7 +257,7 @@ "param = model.default_parameter_values\n", "\n", "# set user defined current function\n", - "A = pybamm.electrical_parameters.I_typ\n", + "A = model.param.I_typ\n", "omega = 0.1\n", "param[\"Current function [A]\"] = my_fun(A,omega)\n", "\n", @@ -275,13 +275,13 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7dff98a2d9a44f87b71abeb0a94e16c9", + "model_id": "88d5d89540c643709f60d486535c8da7", "version_major": 2, "version_minor": 0 }, @@ -338,7 +338,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.7" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py index 03d7de92c9..9aebc56f2b 100644 --- a/pybamm/models/full_battery_models/base_battery_model.py +++ b/pybamm/models/full_battery_models/base_battery_model.py @@ -41,6 +41,12 @@ class BaseBatteryModel(pybamm.BaseModel): * "particle" : str, optional Sets the submodel to use to describe behaviour within the particle. Can be "Fickian diffusion" (default) or "fast diffusion". + * "particle shape" : str, optional + Sets the model shape of the electrode particles. This is used to + calculate the surface area per unit volume. Can be "spherical" + (default) or "user". For the "user" option the surface area per + unit volume can be passed as a parameter, and is therefore not + necessarily consistent with the particle shape. * "thermal" : str, optional Sets the thermal model to use. Can be "isothermal" (default), "lumped", "x-lumped", or "x-full". @@ -184,6 +190,7 @@ def options(self, extra_options): "interfacial surface area": "constant", "current collector": "uniform", "particle": "Fickian diffusion", + "particle shape": "spherical", "thermal": "isothermal", "cell_geometry": None, "external submodels": [], @@ -338,6 +345,10 @@ def options(self, extra_options): raise pybamm.OptionError( "particle model '{}' not recognised".format(options["particle"]) ) + if options["particle shape"] not in ["spherical", "user"]: + raise pybamm.OptionError( + "particle shape '{}' not recognised".format(options["particle shape"]) + ) if options["thermal"] == "x-lumped" and options["dimensionality"] == 1: warnings.warn( diff --git a/pybamm/models/full_battery_models/lithium_ion/basic_dfn.py b/pybamm/models/full_battery_models/lithium_ion/basic_dfn.py index 3c76715ccd..4e246d6fa1 100644 --- a/pybamm/models/full_battery_models/lithium_ion/basic_dfn.py +++ b/pybamm/models/full_battery_models/lithium_ion/basic_dfn.py @@ -109,6 +109,10 @@ def __init__(self, name="Doyle-Fuller-Newman model"): ) eps = pybamm.Concatenation(eps_n, eps_s, eps_p) + # Active material volume fraction (eps + eps_s + eps_inactive = 1) + eps_s_n = pybamm.Parameter("Negative electrode active material volume fraction") + eps_s_p = pybamm.Parameter("Positive electrode active material volume fraction") + # Tortuosity tor = pybamm.Concatenation( eps_n ** param.b_e_n, eps_s ** param.b_e_s, eps_p ** param.b_e_p @@ -206,8 +210,9 @@ def __init__(self, name="Doyle-Fuller-Newman model"): ###################### # Current in the solid ###################### - i_s_n = -param.sigma_n * (1 - eps_n) ** param.b_s_n * pybamm.grad(phi_s_n) - sigma_eff_p = param.sigma_p * (1 - eps_p) ** param.b_s_p + sigma_eff_n = param.sigma_n * eps_s_n ** param.b_s_n + i_s_n = -sigma_eff_n * pybamm.grad(phi_s_n) + sigma_eff_p = param.sigma_p * eps_s_p ** param.b_s_p i_s_p = -sigma_eff_p * pybamm.grad(phi_s_p) # The `algebraic` dictionary contains differential equations, with the key being # the main scalar variable of interest in the equation diff --git a/pybamm/models/submodels/electrode/ohm/full_ohm.py b/pybamm/models/submodels/electrode/ohm/full_ohm.py index 0ab4b0ff4e..a6476407c4 100644 --- a/pybamm/models/submodels/electrode/ohm/full_ohm.py +++ b/pybamm/models/submodels/electrode/ohm/full_ohm.py @@ -60,12 +60,16 @@ def set_algebraic(self, variables): phi_s = variables[self.domain + " electrode potential"] i_s = variables[self.domain + " electrode current density"] + # Get surface area per unit volume distribution in x (to account for + # graded electrodes) + a = variables[self.domain + " surface area per unit volume distribution in x"] + # Variable summing all of the interfacial current densities sum_j = variables[ "Sum of " + self.domain.lower() + " electrode interfacial current densities" ] - self.algebraic[phi_s] = pybamm.div(i_s) + sum_j + self.algebraic[phi_s] = pybamm.div(i_s) + a * sum_j def set_boundary_conditions(self, variables): diff --git a/pybamm/models/submodels/electrolyte_conductivity/full_conductivity.py b/pybamm/models/submodels/electrolyte_conductivity/full_conductivity.py index cd5cdda046..35a5c27c07 100644 --- a/pybamm/models/submodels/electrolyte_conductivity/full_conductivity.py +++ b/pybamm/models/submodels/electrolyte_conductivity/full_conductivity.py @@ -52,10 +52,18 @@ def set_algebraic(self, variables): phi_e = variables["Electrolyte potential"] i_e = variables["Electrolyte current density"] + # Get surface area per unit volume distribution in x (to account for + # graded electrodes) + a_n = variables["Negative surface area per unit volume distribution in x"] + a_p = variables["Positive surface area per unit volume distribution in x"] + a = pybamm.Concatenation( + a_n, pybamm.FullBroadcast(0, "separator", "current collector"), a_p + ) + # Variable summing all of the interfacial current densities sum_j = variables["Sum of interfacial current densities"] - self.algebraic = {phi_e: pybamm.div(i_e) - sum_j} + self.algebraic = {phi_e: pybamm.div(i_e) - a * sum_j} def set_initial_conditions(self, variables): phi_e = variables["Electrolyte potential"] diff --git a/pybamm/models/submodels/interface/base_interface.py b/pybamm/models/submodels/interface/base_interface.py index e248e25218..faab38018d 100644 --- a/pybamm/models/submodels/interface/base_interface.py +++ b/pybamm/models/submodels/interface/base_interface.py @@ -173,6 +173,22 @@ def _get_number_of_electrons_in_reaction(self): else: return pybamm.Scalar(0) + def _get_surface_area_per_unit_volume_distribution(self, variables): + "Returns the distribution of surface area per unit volume in x" + x_n = pybamm.standard_spatial_vars.x_n + x_p = pybamm.standard_spatial_vars.x_p + a_n = self.param.a_n_of_x(x_n) + a_p = self.param.a_p_of_x(x_p) + + variables.update( + { + "Negative surface area per unit volume distribution in x": a_n, + "Positive surface area per unit volume distribution in x": a_p, + } + ) + + return a_n, a_p + def _get_electrolyte_reaction_signed_stoichiometry(self): "Returns the number of electrons in the reaction" if self.reaction in ["lithium-ion main", "sei"]: @@ -327,25 +343,29 @@ def _get_standard_whole_cell_interfacial_current_variables(self, variables): } ) + a_n, a_p = self._get_surface_area_per_unit_volume_distribution(variables) + a = pybamm.Concatenation( + a_n, pybamm.FullBroadcast(0, "separator", "current collector"), a_p + ) s_n, s_p = self._get_electrolyte_reaction_signed_stoichiometry() s = pybamm.Concatenation( pybamm.FullBroadcast(s_n, "negative electrode", "current collector"), pybamm.FullBroadcast(0, "separator", "current collector"), pybamm.FullBroadcast(s_p, "positive electrode", "current collector"), ) - variables["Sum of electrolyte reaction source terms"] += s * j + variables["Sum of electrolyte reaction source terms"] += a * s * j variables["Sum of negative electrode electrolyte reaction source terms"] += ( - s_n * j_n + a_n * s_n * j_n ) variables["Sum of positive electrode electrolyte reaction source terms"] += ( - s_p * j_p + a_p * s_p * j_p ) variables[ "Sum of x-averaged negative electrode electrolyte reaction source terms" - ] += (s_n * j_n_av) + ] += pybamm.x_average(a_n * s_n * j_n) variables[ "Sum of x-averaged positive electrode electrolyte reaction source terms" - ] += (s_p * j_p_av) + ] += pybamm.x_average(a_p * s_p * j_p) variables["Sum of interfacial current densities"] += j variables["Sum of negative electrode interfacial current densities"] += j_n diff --git a/pybamm/models/submodels/particle/base_particle.py b/pybamm/models/submodels/particle/base_particle.py index 1c3d224f21..f06c99f82f 100644 --- a/pybamm/models/submodels/particle/base_particle.py +++ b/pybamm/models/submodels/particle/base_particle.py @@ -29,10 +29,10 @@ def _get_standard_concentration_variables(self, c_s, c_s_xav): if self.domain == "Negative": c_scale = self.param.c_n_max - active_volume = param.a_n_dim * param.R_n / 3 + active_volume = param.epsilon_s_n elif self.domain == "Positive": c_scale = self.param.c_p_max - active_volume = param.a_p_dim * param.R_p / 3 + active_volume = param.epsilon_s_p c_s_av = pybamm.r_average(c_s_xav) c_s_av_vol = active_volume * c_s_av variables = { diff --git a/pybamm/models/submodels/particle/fast_many_particles.py b/pybamm/models/submodels/particle/fast_many_particles.py index 25e44544f2..fe48cb63e1 100644 --- a/pybamm/models/submodels/particle/fast_many_particles.py +++ b/pybamm/models/submodels/particle/fast_many_particles.py @@ -43,6 +43,10 @@ def get_fundamental_variables(self): ) N_s_xav = pybamm.FullBroadcast(0, "negative electrode", "current collector") + x = pybamm.standard_spatial_vars.x_n + R = self.param.R_n_of_x(x) + variables = {"Negative particle distribution in x": R} + elif self.domain == "Positive": c_s_surf = pybamm.standard_variables.c_s_p_surf c_s = pybamm.PrimaryBroadcast(c_s_surf, ["positive particle"]) @@ -58,7 +62,11 @@ def get_fundamental_variables(self): ) N_s_xav = pybamm.FullBroadcast(0, "positive electrode", "current collector") - variables = self._get_standard_concentration_variables(c_s, c_s_xav) + x = pybamm.standard_spatial_vars.x_p + R = self.param.R_p_of_x(x) + variables = {"Positive particle distribution in x": R} + + variables.update(self._get_standard_concentration_variables(c_s, c_s_xav)) variables.update(self._get_standard_flux_variables(N_s, N_s_xav)) return variables @@ -66,11 +74,13 @@ def get_fundamental_variables(self): def set_rhs(self, variables): c_s_surf = variables[self.domain + " particle surface concentration"] j = variables[self.domain + " electrode interfacial current density"] + R = variables[self.domain + " particle distribution in x"] + if self.domain == "Negative": - self.rhs = {c_s_surf: -3 * j / self.param.a_n} + self.rhs = {c_s_surf: -3 * j / self.param.a_n / R} elif self.domain == "Positive": - self.rhs = {c_s_surf: -3 * j / self.param.a_p / self.param.gamma_p} + self.rhs = {c_s_surf: -3 * j / self.param.a_p / self.param.gamma_p / R} def set_initial_conditions(self, variables): c_s_surf = variables[self.domain + " particle surface concentration"] diff --git a/pybamm/models/submodels/particle/fickian_many_particles.py b/pybamm/models/submodels/particle/fickian_many_particles.py index a2515c1491..9fb1f8452a 100644 --- a/pybamm/models/submodels/particle/fickian_many_particles.py +++ b/pybamm/models/submodels/particle/fickian_many_particles.py @@ -44,39 +44,30 @@ def get_coupled_variables(self, variables): if self.domain == "Negative": N_s = -self.param.D_n(c_s, T_k) * pybamm.grad(c_s) - elif self.domain == "Positive": - N_s = -self.param.D_p(c_s, T_k) * pybamm.grad(c_s) - - variables.update(self._get_standard_flux_variables(N_s, N_s)) - - if self.domain == "Negative": x = pybamm.standard_spatial_vars.x_n - R = pybamm.FunctionParameter( - "Negative particle distribution in x", - {"Dimensionless through-cell position (x_n)": x}, - ) + R = self.param.R_n_of_x(x) variables.update({"Negative particle distribution in x": R}) elif self.domain == "Positive": + N_s = -self.param.D_p(c_s, T_k) * pybamm.grad(c_s) + x = pybamm.standard_spatial_vars.x_p - R = pybamm.FunctionParameter( - "Positive particle distribution in x", - {"Dimensionless through-cell position (x_p)": x}, - ) + R = self.param.R_p_of_x(x) variables.update({"Positive particle distribution in x": R}) + variables.update(self._get_standard_flux_variables(N_s, N_s)) + return variables def set_rhs(self, variables): c_s = variables[self.domain + " particle concentration"] N_s = variables[self.domain + " particle flux"] + R = variables[self.domain + " particle distribution in x"] if self.domain == "Negative": - R = variables["Negative particle distribution in x"] self.rhs = {c_s: -(1 / (R ** 2 * self.param.C_n)) * pybamm.div(N_s)} elif self.domain == "Positive": - R = variables["Positive particle distribution in x"] self.rhs = {c_s: -(1 / (R ** 2 * self.param.C_p)) * pybamm.div(N_s)} def set_boundary_conditions(self, variables): diff --git a/pybamm/parameters/geometric_parameters.py b/pybamm/parameters/geometric_parameters.py index 3650218113..98d6f254c1 100644 --- a/pybamm/parameters/geometric_parameters.py +++ b/pybamm/parameters/geometric_parameters.py @@ -49,6 +49,9 @@ def _set_dimensional_parameters(self): self.A_tab_p = self.L_tab_p * self.L_cp # Area of negative tab # Microscale geometry + # Note: the definition of the surface area to volume ratio is + # overwritten in lithium_ion_parameters.py to be computed + # based on the assumed particle shape self.a_n_dim = pybamm.Parameter( "Negative electrode surface area to volume ratio [m-1]" ) diff --git a/pybamm/parameters/lead_acid_parameters.py b/pybamm/parameters/lead_acid_parameters.py index 31b6641b8c..3ccd0736ad 100644 --- a/pybamm/parameters/lead_acid_parameters.py +++ b/pybamm/parameters/lead_acid_parameters.py @@ -711,6 +711,30 @@ def c_p_init(self, x): """ return self.c_e_init + def a_n_of_x(self, x): + """ + Dimensionless surface area per unit volume distribution in x (as a function + for consistency with lithium-ion). The surface area per unit volume + distribution is defined so that the actual surface area per unit volume + as a function of x is given by a*a_of_x (so that a_of_x = 1 gives uniform + surface area per unit volume in x). + + Returns 1 to give uniform surface area per unit volume in x. + """ + return pybamm.FullBroadcast(1, "negative electrode", "current collector") + + def a_p_of_x(self, x): + """ + Dimensionless surface area per unit volume distribution in x (as a function + for consistency with lithium-ion). The surface area per unit volume + distribution is defined so that the actual surface area per unit volume + as a function of x is given by a*a_of_x (so that a_of_x = 1 gives uniform + surface area per unit volume in x). + + Returns 1 to give uniform surface area per unit volume in x. + """ + return pybamm.FullBroadcast(1, "positive electrode", "current collector") + def _set_input_current(self): "Set the input current" diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py index 1dffc08adb..9ec595e44d 100644 --- a/pybamm/parameters/lithium_ion_parameters.py +++ b/pybamm/parameters/lithium_ion_parameters.py @@ -25,14 +25,15 @@ class LithiumIonParameters: can be set are listed below. * "particle shape" : str, optional - Sets the model shape of the particles in the electrode. This is - used to calculate the surface area per unit volume. Can be "speherical" + Sets the model shape of the electrode particles. This is used to + calculate the surface area per unit volume. Can be "spherical" (default) or "user". For the "user" option the surface area per unit volume can be passed as a parameter, and is therefore not necessarily consistent with the particle shape. """ def __init__(self, options=None): + self.options = options # Get geometric, electrical and thermal parameters self.geo = pybamm.GeometricParameters() @@ -109,8 +110,44 @@ def _set_dimensional_parameters(self): ) # Microscale geometry - self.a_n_dim = self.geo.a_n_dim - self.a_p_dim = self.geo.a_p_dim + inputs = {"Through-cell distance (x_n) [m]": pybamm.standard_spatial_vars.x_n} + self.epsilon_n = pybamm.FunctionParameter("Negative electrode porosity", inputs) + + inputs = {"Through-cell distance (x_s) [m]": pybamm.standard_spatial_vars.x_s} + self.epsilon_s = pybamm.FunctionParameter("Separator porosity", inputs) + + inputs = {"Through-cell distance (x_p) [m]": pybamm.standard_spatial_vars.x_p} + self.epsilon_p = pybamm.FunctionParameter("Positive electrode porosity", inputs) + + self.epsilon = pybamm.Concatenation( + self.epsilon_n, self.epsilon_s, self.epsilon_p + ) + self.epsilon_s_n = pybamm.Parameter( + "Negative electrode active material volume fraction" + ) + self.epsilon_s_p = pybamm.Parameter( + "Positive electrode active material volume fraction" + ) + self.epsilon_inactive_n = 1 - self.epsilon_n - self.epsilon_s_n + self.epsilon_inactive_s = 1 - self.epsilon_s + self.epsilon_inactive_p = 1 - self.epsilon_p - self.epsilon_s_p + self.b_e_n = self.geo.b_e_n + self.b_e_s = self.geo.b_e_s + self.b_e_p = self.geo.b_e_p + self.b_s_n = self.geo.b_s_n + self.b_s_s = self.geo.b_s_s + self.b_s_p = self.geo.b_s_p + + self.R_n = self.geo.R_n + self.R_p = self.geo.R_p + + if self.options["particle shape"] == "spherical": + self.a_n_dim = 3 * self.epsilon_s_n / self.R_n + self.a_p_dim = 3 * self.epsilon_s_p / self.R_p + elif self.options["particle shape"] == "user": + self.a_n_dim = self.geo.a_n_dim + self.a_p_dim = self.geo.a_p_dim + self.a_k_dim = pybamm.Concatenation( pybamm.FullBroadcast( self.a_n_dim, ["negative electrode"], "current collector" @@ -120,14 +157,6 @@ def _set_dimensional_parameters(self): self.a_p_dim, ["positive electrode"], "current collector" ), ) - self.R_n = self.geo.R_n - self.R_p = self.geo.R_p - self.b_e_n = self.geo.b_e_n - self.b_e_s = self.geo.b_e_s - self.b_e_p = self.geo.b_e_p - self.b_s_n = self.geo.b_s_n - self.b_s_s = self.geo.b_s_s - self.b_s_p = self.geo.b_s_p # Electrochemical reactions self.ne_n = pybamm.Parameter("Negative electrode electrons in reaction") @@ -400,34 +429,9 @@ def _set_dimensionless_parameters(self): self.centre_y_tab_p = self.geo.centre_y_tab_p self.centre_z_tab_p = self.geo.centre_z_tab_p - # Microscale geometry - inputs = {"Through-cell distance (x_n) [m]": pybamm.standard_spatial_vars.x_n} - self.epsilon_n = pybamm.FunctionParameter("Negative electrode porosity", inputs) - - inputs = {"Through-cell distance (x_s) [m]": pybamm.standard_spatial_vars.x_s} - self.epsilon_s = pybamm.FunctionParameter("Separator porosity", inputs) - - inputs = {"Through-cell distance (x_p) [m]": pybamm.standard_spatial_vars.x_p} - self.epsilon_p = pybamm.FunctionParameter("Positive electrode porosity", inputs) - - self.epsilon = pybamm.Concatenation( - self.epsilon_n, self.epsilon_s, self.epsilon_p - ) - self.epsilon_n_init = pybamm.Parameter("Negative electrode porosity") - self.epsilon_s_init = pybamm.Parameter("Separator porosity") - self.epsilon_p_init = pybamm.Parameter("Positive electrode porosity") - self.epsilon_init = pybamm.Concatenation( - self.epsilon_n, self.epsilon_s, self.epsilon_p - ) - self.epsilon_s_n = pybamm.Parameter( - "Negative electrode active material volume fraction" - ) - self.epsilon_s_p = pybamm.Parameter( - "Positive electrode active material volume fraction" - ) - self.epsilon_inactive_n = 1 - self.epsilon_n - self.epsilon_s_n - self.epsilon_inactive_s = 1 - self.epsilon_s - self.epsilon_inactive_p = 1 - self.epsilon_p - self.epsilon_s_p + # Microscale geometry, see 'self._set_dimensional_parameters' for the + # definition on the dimensional surface area to volume ratio based on + # particle shape self.a_n = self.a_n_dim * self.R_n self.a_p = self.a_p_dim * self.R_p @@ -615,6 +619,12 @@ def _set_dimensionless_parameters(self): self.beta_sei_n = self.a_n_dim * self.L_sei_0_dim * self.Gamma_SEI_n # Initial conditions + self.epsilon_n_init = pybamm.Parameter("Negative electrode porosity") + self.epsilon_s_init = pybamm.Parameter("Separator porosity") + self.epsilon_p_init = pybamm.Parameter("Positive electrode porosity") + self.epsilon_init = pybamm.Concatenation( + self.epsilon_n, self.epsilon_s, self.epsilon_p + ) self.T_init = self.therm.T_init self.c_e_init = self.c_e_init_dimensional / self.c_e_typ @@ -717,6 +727,60 @@ def c_p_init(self, x): "Dimensionless initial concentration as a function of dimensionless position x" return self.c_p_init_dimensional(x) / self.c_p_max + def R_n_of_x(self, x): + """ + Dimensionless negative particle distribution in x. The particle distribution is + defined so that the actual particle radius as a function of x is given by + R*R_of_x (so that R_of_x = 1 gives particles of uniform size in x). + """ + inputs = {"Through-cell distance (x_n) [m]": x} + return pybamm.FunctionParameter("Negative particle distribution in x", inputs) + + def R_p_of_x(self, x): + """ + Dimensionless positive particle distribution in x. The particle distribution is + defined so that the actual particle radius as a function of x is given by + R*R_of_x (so that R_of_x = 1 gives particles of uniform size in x). + """ + inputs = {"Through-cell distance (x_p) [m]": x} + return pybamm.FunctionParameter("Positive particle distribution in x", inputs) + + def a_n_of_x(self, x): + """ + Dimensionless surface area per unit volume distribution in x. The surface + area per unit volume distribution is defined so that the actual surface + area per unit volume as a function of x is given by a*a_of_x (so that + a_of_x = 1 gives uniform surface area per unit volume in x). + """ + if self.options["particle shape"] == "spherical": + # Currently the active material volume fraction is a scalar, so the + # distribution of surface are per unit volume is simply the reciprocal + # of the particle radius distribution + return 1 / self.R_n_of_x(x) + elif self.options["particle shape"] == "user": + inputs = {"Through-cell distance (x_n) [m]": x} + return pybamm.FunctionParameter( + "Negative surface area per unit volume distribution in x", inputs + ) + + def a_p_of_x(self, x): + """ + Dimensionless surface area per unit volume distribution in x. The surface + area per unit volume distribution is defined so that the actual surface + area per unit volume as a function of x is given by a*a_of_x (so that + a_of_x = 1 gives uniform surface area per unit volume in x). + """ + if self.options["particle shape"] == "spherical": + # Currently the active material volume fraction is a scalar, so the + # distribution of surface are per unit volume is simply the reciprocal + # of the particle radius distribution + return 1 / self.R_p_of_x(x) + elif self.options["particle shape"] == "user": + inputs = {"Through-cell distance (x_p) [m]": x} + return pybamm.FunctionParameter( + "Positive surface area per unit volume distribution in x", inputs + ) + def _set_input_current(self): "Set the input current" @@ -732,3 +796,30 @@ def _set_input_current(self): / self.I_typ * pybamm.Function(np.sign, self.I_typ) ) + + @property + def options(self): + return self._options + + @options.setter + def options(self, extra_options): + extra_options = extra_options or {} + + # Default options + options = {"particle shape": "spherical"} + + # All model options get passed to the parameter class, so we just need + # to update the options in the default options and ignore the rest + for name, opt in extra_options.items(): + if name in options: + options[name] = opt + + # Check the options are valid (this check also happens in 'BaseBatteryModel', + # but we check here incase the parameter class is instantiated separetly + # from the model) + if options["particle shape"] not in ["spherical", "user"]: + raise pybamm.OptionError( + "particle shape '{}' not recognised".format(options["particle shape"]) + ) + + self._options = options diff --git a/tests/integration/test_models/standard_output_tests.py b/tests/integration/test_models/standard_output_tests.py index 6dd2cd35da..f66770ee9c 100644 --- a/tests/integration/test_models/standard_output_tests.py +++ b/tests/integration/test_models/standard_output_tests.py @@ -588,13 +588,6 @@ def test_current_density_boundaries(self): """Test the boundary values of the current densities""" t, x_n, x_p = self.t, self.x_n_edge, self.x_p_edge - # if isinstance(self.model, pybamm.lithium_ion.BaseModel): - # current_param = pybamm.standard_parameters_lithium_ion.current_with_time - # elif isinstance(self.model, pybamm.lead_acid.BaseModel): - # current_param = pybamm.standard_parameters_lead_acid.current_with_time - # else: - # current_param = pybamm.electrical_parameters.current_with_time - current_param = self.model.param.current_with_time i_cell = self.param.process_symbol(current_param).evaluate(t=t) diff --git a/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_full_conductivity.py b/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_full_conductivity.py index 47cda3d45d..575898299d 100644 --- a/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_full_conductivity.py +++ b/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_full_conductivity.py @@ -11,6 +11,7 @@ class TestFull(unittest.TestCase): def test_public_functions(self): param = pybamm.LithiumIonParameters() a = pybamm.Scalar(0) + surf = "surface area per unit volume distribution in x" variables = { "Electrolyte tortuosity": a, "Electrolyte concentration": pybamm.FullBroadcast( @@ -18,6 +19,10 @@ def test_public_functions(self): ["negative electrode", "separator", "positive electrode"], "current collector", ), + "Negative " + + surf: pybamm.FullBroadcast(a, "negative electrode", "current collector"), + "Positive " + + surf: pybamm.FullBroadcast(a, "positive electrode", "current collector"), "Sum of interfacial current densities": pybamm.FullBroadcast( a, ["negative electrode", "separator", "positive electrode"], From 43567be5c871853bc741e1faf21761ace1149c36 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Fri, 31 Jul 2020 10:50:42 +0100 Subject: [PATCH 04/10] #1115 fix tests --- .../anodes/graphite_Ecker2015/parameters.csv | 2 +- .../LiNiCoO2_Ecker2015/parameters.csv | 2 +- .../full_surface_form_conductivity.py | 12 +++++-- .../test_models/standard_output_tests.py | 29 ++++++++++++++-- .../test_lithium_ion/test_dfn_x.py | 33 +++++++++++++++++++ .../test_base_battery_model.py | 2 ++ 6 files changed, 73 insertions(+), 7 deletions(-) create mode 100644 tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_dfn_x.py diff --git a/pybamm/input/parameters/lithium-ion/anodes/graphite_Ecker2015/parameters.csv b/pybamm/input/parameters/lithium-ion/anodes/graphite_Ecker2015/parameters.csv index c2f2d453c9..0cc17d38f1 100644 --- a/pybamm/input/parameters/lithium-ion/anodes/graphite_Ecker2015/parameters.csv +++ b/pybamm/input/parameters/lithium-ion/anodes/graphite_Ecker2015/parameters.csv @@ -11,7 +11,7 @@ Negative electrode OCP [V],[function]graphite_ocp_Ecker2015_function,, ,,, # Microstructure,,, Negative electrode porosity,0.329,, -Negative electrode active material volume fraction, 0.372,, +Negative electrode active material volume fraction, 0.372403,, Negative particle radius [m],1.37E-05,, Negative particle distribution in x,1,, Negative electrode surface area to volume ratio [m-1], 81548,, diff --git a/pybamm/input/parameters/lithium-ion/cathodes/LiNiCoO2_Ecker2015/parameters.csv b/pybamm/input/parameters/lithium-ion/cathodes/LiNiCoO2_Ecker2015/parameters.csv index db5333e6b2..483457981c 100644 --- a/pybamm/input/parameters/lithium-ion/cathodes/LiNiCoO2_Ecker2015/parameters.csv +++ b/pybamm/input/parameters/lithium-ion/cathodes/LiNiCoO2_Ecker2015/parameters.csv @@ -11,7 +11,7 @@ Positive electrode OCP [V],[function]nco_ocp_Ecker2015_function,, ,,, # Microstructure,,, Positive electrode porosity,0.296,, -Positive electrode active material volume fraction, 0.408,, +Positive electrode active material volume fraction, 0.40832,, Positive particle radius [m],6.5E-06,, Positive particle distribution in x,1,, Positive electrode surface area to volume ratio [m-1],188455,, diff --git a/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/full_surface_form_conductivity.py b/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/full_surface_form_conductivity.py index f67f53745f..4afce31f33 100644 --- a/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/full_surface_form_conductivity.py +++ b/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/full_surface_form_conductivity.py @@ -217,12 +217,16 @@ def set_algebraic(self, variables): delta_phi = variables[self.domain + " electrode surface potential difference"] i_e = variables[self.domain + " electrolyte current density"] + # Get surface area per unit volume distribution in x (to account for + # graded electrodes) + a = variables[self.domain + " surface area per unit volume distribution in x"] + # Variable summing all of the interfacial current densities sum_j = variables[ "Sum of " + self.domain.lower() + " electrode interfacial current densities" ] - self.algebraic[delta_phi] = pybamm.div(i_e) - sum_j + self.algebraic[delta_phi] = pybamm.div(i_e) - a * sum_j class FullDifferential(BaseModel): @@ -255,9 +259,13 @@ def set_rhs(self, variables): delta_phi = variables[self.domain + " electrode surface potential difference"] i_e = variables[self.domain + " electrolyte current density"] + # Get surface area per unit volume distribution in x (to account for + # graded electrodes) + a = variables[self.domain + " surface area per unit volume distribution in x"] + # Variable summing all of the interfacial current densities sum_j = variables[ "Sum of " + self.domain.lower() + " electrode interfacial current densities" ] - self.rhs[delta_phi] = 1 / C_dl * (pybamm.div(i_e) - sum_j) + self.rhs[delta_phi] = 1 / C_dl * (pybamm.div(i_e) - a * sum_j) diff --git a/tests/integration/test_models/standard_output_tests.py b/tests/integration/test_models/standard_output_tests.py index f66770ee9c..32ba280ab5 100644 --- a/tests/integration/test_models/standard_output_tests.py +++ b/tests/integration/test_models/standard_output_tests.py @@ -551,19 +551,42 @@ def __init__(self, model, param, disc, solution, operating_condition): self.i_s = solution["Electrode current density"] self.i_e = solution["Electrolyte current density"] + self.a_n = solution["Negative surface area per unit volume distribution in x"] + self.a_p = solution["Positive surface area per unit volume distribution in x"] + def test_interfacial_current_average(self): - """Test that average of the interfacial current density is equal to the true + """Test that average of the surface area density distribution (in x) + multiplied by the interfacial current density is equal to the true value.""" + np.testing.assert_array_almost_equal( - self.j_n_av(self.t) + self.j_n_sei_av(self.t), + np.mean( + self.a_n(x=self.x_n) + * (self.j_n(self.t, self.x_n) + self.j_n_sei(self.t, self.x_n)), + axis=0, + ), self.i_cell / self.l_n, decimal=4, ) np.testing.assert_array_almost_equal( - self.j_p_av(self.t) + self.j_p_sei_av(self.t), + np.mean( + self.a_p(x=self.x_p) + * (self.j_p(self.t, self.x_p) + self.j_p_sei(self.t, self.x_p)), + axis=0, + ), -self.i_cell / self.l_p, decimal=4, ) + # np.testing.assert_array_almost_equal( + # (self.j_n_av(self.t) + self.j_n_sei_av(self.t)), + # self.i_cell / self.l_n, + # decimal=4, + # ) + # np.testing.assert_array_almost_equal( + # self.j_p_av(self.t) + self.j_p_sei_av(self.t), + # -self.i_cell / self.l_p, + # decimal=4, + # ) def test_conservation(self): """Test sum of electrode and electrolyte current densities give the applied diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_dfn_x.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_dfn_x.py new file mode 100644 index 0000000000..a1c77764fb --- /dev/null +++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_dfn_x.py @@ -0,0 +1,33 @@ +# +# Tests for the lithium-ion DFN model +# +import pybamm +import tests + +import unittest + + +class TestDFN(unittest.TestCase): + def test_particle_distribution_in_x(self): + model = pybamm.lithium_ion.DFN() + param = model.default_parameter_values + + def negative_distribution(x): + return 1 + x + + def positive_distribution(x): + return 1 + (x - (1 - model.param.l_p)) + + param["Negative particle distribution in x"] = negative_distribution + param["Positive particle distribution in x"] = positive_distribution + modeltest = tests.StandardModelTest(model, parameter_values=param) + modeltest.test_all() + + +if __name__ == "__main__": + print("Add -v for more debug output") + import sys + + if "-v" in sys.argv: + debug = True + unittest.main() diff --git a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py index c1c63b9a26..2463bca6f2 100644 --- a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py +++ b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py @@ -137,6 +137,8 @@ def test_options(self): pybamm.BaseBatteryModel({"convection": "full transverse"}) with self.assertRaisesRegex(pybamm.OptionError, "particle model"): pybamm.BaseBatteryModel({"particle": "bad particle"}) + with self.assertRaisesRegex(pybamm.OptionError, "particle shape"): + pybamm.BaseBatteryModel({"particle shape": "bad particle shape"}) with self.assertRaisesRegex(pybamm.OptionError, "operating mode"): pybamm.BaseBatteryModel({"operating mode": "bad operating mode"}) From 2195ae209af97273a8de82773518094974739d02 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Fri, 31 Jul 2020 20:12:29 +0100 Subject: [PATCH 05/10] #1115 fix examples --- .../compare-comsol-discharge-curve.ipynb | 30 ++++++-- examples/notebooks/compare-ecker-data.ipynb | 14 +++- .../notebooks/models/pouch-cell-model.ipynb | 36 ++++----- .../compare_comsol/compare_comsol_DFN.py | 13 +++- .../scripts/compare_comsol/discharge_curve.py | 12 ++- ...mpare_lithium_ion_particle_distribution.py | 56 ++++---------- .../full_battery_models/base_battery_model.py | 22 ++++-- .../submodels/interface/base_interface.py | 14 ++++ .../particle/fickian_many_particles.py | 6 +- pybamm/models/submodels/thermal/isothermal.py | 30 ++++---- test.py | 73 +++++++++++++------ .../test_lithium_ion/test_compare_outputs.py | 55 +++++++++++++- .../test_lithium_ion/test_dfn_x.py | 33 --------- .../test_geometry/test_battery_geometry.py | 4 + .../test_base_battery_model.py | 6 ++ .../test_base_lithium_ion_model.py | 4 + .../test_lithium_ion/test_dfn.py | 5 ++ .../test_lithium_ion/test_spm.py | 5 ++ .../test_lithium_ion/test_spme.py | 5 ++ .../test_full_surface_form_conductivity.py | 2 + ...d_acid.py => test_lead_acid_parameters.py} | 0 ..._ion.py => test_lithium_ion_parameters.py} | 4 + 22 files changed, 269 insertions(+), 160 deletions(-) delete mode 100644 tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_dfn_x.py rename tests/unit/test_parameters/{test_standard_parameters_lead_acid.py => test_lead_acid_parameters.py} (100%) rename tests/unit/test_parameters/{test_dimensionless_parameter_values_lithium_ion.py => test_lithium_ion_parameters.py} (97%) diff --git a/examples/notebooks/compare-comsol-discharge-curve.ipynb b/examples/notebooks/compare-comsol-discharge-curve.ipynb index 2d34368847..9e59868edf 100644 --- a/examples/notebooks/compare-comsol-discharge-curve.ipynb +++ b/examples/notebooks/compare-comsol-discharge-curve.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this notebook we compare the discharge curves obatined by solving the DFN model both in PyBaMM and COMSOL. Results are presented for a range of C-rates, and we see an excellent agreement between the two implementations. If you would like to compare internal varibles please see the script [compare_comsol_DFN](https://github.com/pybamm-team/PyBaMM/blob/comsol-voltage-compare/examples/scripts/compare_comsol/compare_comsol_DFN.py) which creates a slider plot comparing potentials and concentrations as functions of time and space for a given C-rate. For more information on the DFN model, see the [DFN notebook](https://github.com/pybamm-team/PyBaMM/blob/master/examples/notebooks/models/DFN.ipynb)." + "In this notebook we compare the discharge curves obtained by solving the DFN model both in PyBaMM and COMSOL. Results are presented for a range of C-rates, and we see an excellent agreement between the two implementations. If you would like to compare internal variables please see the script [compare_comsol_DFN](https://github.com/pybamm-team/PyBaMM/blob/comsol-voltage-compare/examples/scripts/compare_comsol/compare_comsol_DFN.py) which creates a slider plot comparing potentials and concentrations as functions of time and space for a given C-rate. For more information on the DFN model, see the [DFN notebook](https://github.com/pybamm-team/PyBaMM/blob/master/examples/notebooks/models/DFN.ipynb)." ] }, { @@ -25,7 +25,15 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], "source": [ "%pip install pybamm -q # install PyBaMM if it is not installed\n", "import pybamm\n", @@ -56,7 +64,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We get the DFN model equations, geometry, and default parameters. Before processign the model, we adjust the electrode height and depth to be 1 m, to match the one-dimensional model we solved in COMSOL. The model is then processed using the default geometry and updated paramters. Finally, we create a mesh and discretise the model. " + "We get the DFN model equations, geometry, and default parameters. Before processing the model, we adjust the electrode height and depth to be 1 m, and also adjust the electrode conductivities, to match the one-dimensional model we solved in COMSOL. The model is then processed using the default geometry and updated parameters. Finally, we create a mesh and discretise the model. " ] }, { @@ -71,9 +79,15 @@ "\n", "# load parameters and process model and geometry\n", "param = model.default_parameter_values\n", - "param[\"Electrode width [m]\"] = 1\n", - "param[\"Electrode height [m]\"] = 1\n", - "param[\"Current function [A]\"] = \"[input]\"\n", + "param.update(\n", + " {\n", + " \"Electrode width [m]\": 1,\n", + " \"Electrode height [m]\": 1,\n", + " \"Negative electrode conductivity [S.m-1]\": 126,\n", + " \"Positive electrode conductivity [S.m-1]\": 16.6,\n", + " \"Current function [A]\": \"[input]\",\n", + " }\n", + ")\n", "param.process_model(model)\n", "param.process_geometry(geometry)\n", "\n", @@ -101,7 +115,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -208,7 +222,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/examples/notebooks/compare-ecker-data.ipynb b/examples/notebooks/compare-ecker-data.ipynb index f51c513a03..1bcd6bd985 100644 --- a/examples/notebooks/compare-ecker-data.ipynb +++ b/examples/notebooks/compare-ecker-data.ipynb @@ -20,7 +20,15 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], "source": [ "%pip install pybamm -q # install PyBaMM if it is not installed\n", "import pybamm\n", @@ -160,7 +168,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -238,7 +246,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/examples/notebooks/models/pouch-cell-model.ipynb b/examples/notebooks/models/pouch-cell-model.ipynb index 245d084794..fd3d054500 100644 --- a/examples/notebooks/models/pouch-cell-model.ipynb +++ b/examples/notebooks/models/pouch-cell-model.ipynb @@ -91,7 +91,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/vsulzer/Documents/Energy_storage/PyBaMM/pybamm/models/full_battery_models/base_battery_model.py:344: UserWarning: 1+1D Thermal models are only valid if both tabs are placed at the top of the cell.\n", + "/home/user/Documents/PyBaMM/pybamm/models/full_battery_models/base_battery_model.py:361: UserWarning: 1+1D Thermal models are only valid if both tabs are placed at the top of the cell.\n", " \"1+1D Thermal models are only valid if both tabs are \"\n" ] } @@ -138,7 +138,7 @@ "I_1C = param[\"Cell capacity [A.h]\"] # 1C current is cell capacity multipled by 1 hour\n", "param.update(\n", " {\n", - " \"Current function [A]\": I_1C * 3,\n", + " \"Current function [A]\": I_1C * 3, \n", " \"Negative electrode diffusivity [m2.s-1]\": 3.9 * 10 ** (-14),\n", " \"Positive electrode diffusivity [m2.s-1]\": 10 ** (-13),\n", " \"Negative current collector surface heat transfer coefficient [W.m-2.K-1]\": 10,\n", @@ -318,7 +318,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -344,7 +344,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -372,7 +372,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -393,7 +393,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -546,7 +546,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -564,12 +564,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -609,12 +609,12 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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" ] @@ -673,12 +673,12 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -725,12 +725,14 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": {}, + "execution_count": 18, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -811,7 +813,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.7" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/examples/scripts/compare_comsol/compare_comsol_DFN.py b/examples/scripts/compare_comsol/compare_comsol_DFN.py index 5efe21d260..54542215fb 100644 --- a/examples/scripts/compare_comsol/compare_comsol_DFN.py +++ b/examples/scripts/compare_comsol/compare_comsol_DFN.py @@ -32,9 +32,16 @@ # load parameters and process model and geometry param = pybamm_model.default_parameter_values -param["Electrode width [m]"] = 1 -param["Electrode height [m]"] = 1 -param["Current function [A]"] = 24 * C_rates[C_rate] +param.update( + { + "Electrode width [m]": 1, + "Electrode height [m]": 1, + "Negative electrode conductivity [S.m-1]": 126, + "Positive electrode conductivity [S.m-1]": 16.6, + "Current function [A]": 24 * C_rates[C_rate], + } +) + param.process_model(pybamm_model) param.process_geometry(geometry) diff --git a/examples/scripts/compare_comsol/discharge_curve.py b/examples/scripts/compare_comsol/discharge_curve.py index cf105869be..10da794bf7 100644 --- a/examples/scripts/compare_comsol/discharge_curve.py +++ b/examples/scripts/compare_comsol/discharge_curve.py @@ -17,9 +17,15 @@ # load parameters and process model and geometry param = model.default_parameter_values -param["Electrode width [m]"] = 1 -param["Electrode height [m]"] = 1 -param["Current function [A]"] = "[input]" +param.update( + { + "Electrode width [m]": 1, + "Electrode height [m]": 1, + "Negative electrode conductivity [S.m-1]": 126, + "Positive electrode conductivity [S.m-1]": 16.6, + "Current function [A]": "[input]", + } +) param.process_model(model) param.process_geometry(geometry) diff --git a/examples/scripts/compare_lithium_ion_particle_distribution.py b/examples/scripts/compare_lithium_ion_particle_distribution.py index b38180ad8b..04ead95b71 100644 --- a/examples/scripts/compare_lithium_ion_particle_distribution.py +++ b/examples/scripts/compare_lithium_ion_particle_distribution.py @@ -1,68 +1,40 @@ # -# Compare lithium-ion battery models +# Compare lithium-ion battery models with and without particle size distibution # -import argparse import numpy as np import pybamm -parser = argparse.ArgumentParser() -parser.add_argument( - "--debug", action="store_true", help="Set logging level to 'DEBUG'." -) -args = parser.parse_args() -if args.debug: - pybamm.set_logging_level("DEBUG") -else: - pybamm.set_logging_level("INFO") +pybamm.set_logging_level("INFO") # load models -options = {"thermal": "isothermal"} models = [ - pybamm.lithium_ion.DFN(options, name="standard DFN"), - pybamm.lithium_ion.DFN(options, name="particle DFN"), + pybamm.lithium_ion.DFN(name="standard DFN"), + pybamm.lithium_ion.DFN(name="particle DFN"), ] - -# load parameter values and process models and geometry +# load parameter values params = [models[0].default_parameter_values, models[1].default_parameter_values] -params[0]["Typical current [A]"] = 1.0 -params[0].process_model(models[0]) - - -params[1]["Typical current [A]"] = 1.0 def negative_distribution(x): - return 1 + x + return 1 + 2 * x / models[1].param.l_n def positive_distribution(x): - return 1 + (x - (1 - models[1].param.l_p)) + return 1 + 2 * (1 - x) / models[1].param.l_p params[1]["Negative particle distribution in x"] = negative_distribution params[1]["Positive particle distribution in x"] = positive_distribution -params[1].process_model(models[1]) -# set mesh -var = pybamm.standard_spatial_vars -var_pts = {var.x_n: 10, var.x_s: 10, var.x_p: 10, var.r_n: 5, var.r_p: 5} -# discretise models -for param, model in zip(params, models): - # create geometry - geometry = model.default_geometry - param.process_geometry(geometry) - mesh = pybamm.Mesh(geometry, models[-1].default_submesh_types, var_pts) - disc = pybamm.Discretisation(mesh, model.default_spatial_methods) - disc.process_model(model) - -# solve model -solutions = [None] * len(models) +# set up and solve simulations t_eval = np.linspace(0, 3600, 100) -for i, model in enumerate(models): - solutions[i] = pybamm.CasadiSolver().solve(model, t_eval) - +sols = [] +for model, param in zip(models, params): + sim = pybamm.Simulation(model, parameter_values=param) + sol = sim.solve(t_eval) + sols.append(sol) output_variables = [ "Negative particle surface concentration", @@ -78,5 +50,5 @@ def positive_distribution(x): ] # plot -plot = pybamm.QuickPlot(solutions, output_variables=output_variables) +plot = pybamm.QuickPlot(sols, output_variables=output_variables) plot.dynamic_plot() diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py index 9aebc56f2b..baa702a8cf 100644 --- a/pybamm/models/full_battery_models/base_battery_model.py +++ b/pybamm/models/full_battery_models/base_battery_model.py @@ -18,6 +18,12 @@ class BaseBatteryModel(pybamm.BaseModel): be set are listed below. Note that not all of the options are compatible with each other and with all of the models implemented in PyBaMM. + * "cell geometry" : str, optional + Sets the geometry of the cell. Can be "pouch" (default) or + "arbitrary". The arbitrary geometry option solves a 1D electrochemical + model with prescribed cell volume and cross-sectional area, and + (if thermal effects are included) solves a lumped thermal model + with prescribed surface area for cooling. * "dimensionality" : int, optional Sets the dimension of the current collector problem. Can be 0 (default), 1 or 2. @@ -192,7 +198,7 @@ def options(self, extra_options): "particle": "Fickian diffusion", "particle shape": "spherical", "thermal": "isothermal", - "cell_geometry": None, + "cell geometry": None, "external submodels": [], "sei": None, "sei porosity change": False, @@ -203,10 +209,10 @@ def options(self, extra_options): "thermal", None ) # return None if option not given if thermal_option is None or thermal_option in ["isothermal", "lumped"]: - default_options["cell_geometry"] = "arbitrary" + default_options["cell geometry"] = "arbitrary" else: - default_options["cell_geometry"] = "pouch" - # The "cell_geometry" option will still be overridden by extra_options if + default_options["cell geometry"] = "pouch" + # The "cell geometry" option will still be overridden by extra_options if # provided # Change the default for SEI film resistance based on which sei option is @@ -240,7 +246,7 @@ def options(self, extra_options): ) if ( options["thermal"] in ["x-lumped", "x-full"] - and options["cell_geometry"] != "pouch" + and options["cell geometry"] != "pouch" ): raise pybamm.OptionError( options["thermal"] + " model must have pouch geometry." @@ -305,9 +311,9 @@ def options(self, extra_options): raise pybamm.OptionError( "Unknown thermal model '{}'".format(options["thermal"]) ) - if options["cell_geometry"] not in ["arbitrary", "pouch"]: + if options["cell geometry"] not in ["arbitrary", "pouch"]: raise pybamm.OptionError( - "Unknown geometry '{}'".format(options["cell_geometry"]) + "Unknown geometry '{}'".format(options["cell geometry"]) ) if options["sei"] not in [ None, @@ -630,7 +636,7 @@ def set_thermal_submodel(self): thermal_submodel = pybamm.thermal.Lumped( self.param, self.options["dimensionality"], - self.options["cell_geometry"], + self.options["cell geometry"], ) elif self.options["thermal"] == "x-lumped": diff --git a/pybamm/models/submodels/interface/base_interface.py b/pybamm/models/submodels/interface/base_interface.py index faab38018d..3f6162ccfe 100644 --- a/pybamm/models/submodels/interface/base_interface.py +++ b/pybamm/models/submodels/interface/base_interface.py @@ -215,6 +215,20 @@ def _get_delta_phi(self, variables): def _get_average_total_interfacial_current_density(self, variables): """ Method to obtain the average total interfacial current density. + + Note: this is only exact if surface area per unit volume is uniform + in space. Since we currently assume the active material volume fraction is + uniform in space the only way to get non-uniform surface area per unit volume + is to introduce a particle size distibution in x (see issue #1119). This only + makes sense for the DFN model in which the correct average interfacial + current density is computed in 'base_kinetics.py' by averaging the actual + interfacial current density. The approximation here is only used to get the + approximate constant additional resistance term for the "average" sei film + resistance model (if using), where only negligible errors will be introduced. + + For "leading-order" and "composite" submodels (as used in the SPM and SPMe) + the surface area per unit volume is uniform anyway, so this method returns + correct result. """ i_boundary_cc = variables["Current collector current density"] diff --git a/pybamm/models/submodels/particle/fickian_many_particles.py b/pybamm/models/submodels/particle/fickian_many_particles.py index 9fb1f8452a..26630a8f50 100644 --- a/pybamm/models/submodels/particle/fickian_many_particles.py +++ b/pybamm/models/submodels/particle/fickian_many_particles.py @@ -76,14 +76,18 @@ def set_boundary_conditions(self, variables): c_s_surf = variables[self.domain + " particle surface concentration"] T_k = variables[self.domain + " electrode temperature"] j = variables[self.domain + " electrode interfacial current density"] + R = variables[self.domain + " particle distribution in x"] if self.domain == "Negative": - rbc = -self.param.C_n * j / self.param.a_n / self.param.D_n(c_s_surf, T_k) + rbc = ( + -self.param.C_n * j * R / self.param.a_n / self.param.D_n(c_s_surf, T_k) + ) elif self.domain == "Positive": rbc = ( -self.param.C_p * j + * R / self.param.a_p / self.param.gamma_p / self.param.D_p(c_s_surf, T_k) diff --git a/pybamm/models/submodels/thermal/isothermal.py b/pybamm/models/submodels/thermal/isothermal.py index a29c192222..9d7a2507d6 100644 --- a/pybamm/models/submodels/thermal/isothermal.py +++ b/pybamm/models/submodels/thermal/isothermal.py @@ -38,14 +38,27 @@ def get_fundamental_variables(self): return variables def get_coupled_variables(self, variables): + ieh = "irreversible electrochemical heating" variables.update( { "Ohmic heating": pybamm.Scalar(0), "Ohmic heating [W.m-3]": pybamm.Scalar(0), + "X-averaged Ohmic heating": pybamm.Scalar(0), + "X-averaged Ohmic heating [W.m-3]": pybamm.Scalar(0), + "Volume-averaged Ohmic heating": pybamm.Scalar(0), + "Volume-averaged Ohmic heating [W.m-3]": pybamm.Scalar(0), "Irreversible electrochemical heating": pybamm.Scalar(0), "Irreversible electrochemical heating [W.m-3]": pybamm.Scalar(0), + "X-averaged " + ieh: pybamm.Scalar(0), + "X-averaged " + ieh + " [W.m-3]": pybamm.Scalar(0), + "Volume-averaged " + ieh: pybamm.Scalar(0), + "Volume-averaged " + ieh + "[W.m-3]": pybamm.Scalar(0), "Reversible heating": pybamm.Scalar(0), "Reversible heating [W.m-3]": pybamm.Scalar(0), + "X-averaged reversible heating": pybamm.Scalar(0), + "X-averaged reversible heating [W.m-3]": pybamm.Scalar(0), + "Volume-averaged reversible heating": pybamm.Scalar(0), + "Volume-averaged reversible heating [W.m-3]": pybamm.Scalar(0), "Total heating": pybamm.Scalar(0), "Total heating [W.m-3]": pybamm.Scalar(0), "X-averaged total heating": pybamm.Scalar(0), @@ -55,20 +68,3 @@ def get_coupled_variables(self, variables): } ) return variables - - def _x_average(self, var, var_cn, var_cp): - """ - Temperature is uniform and heat source terms are zero, so the average - returns the input variable. - This overwrites the default behaviour of - :meth:`pybamm.thermal.BaseThermal._x_average` - """ - return var - - def _yz_average(self, var): - """ - Temperature is uniform and heat source terms are zero, so the average - returns the input variable. This overwrites the default behaviour of - :meth:`pybamm.thermal.BaseThermal._x_average` - """ - return var diff --git a/test.py b/test.py index 58d3b29652..4e7d6ed905 100644 --- a/test.py +++ b/test.py @@ -1,26 +1,51 @@ import pybamm +import numpy as np -model = pybamm.lithium_ion.DFN() -param = pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Marquis2019) -param.update( - { - "Negative electrode active material volume fraction": "[input]", - "Positive electrode active material volume fraction": "[input]", - } -) - -sim = pybamm.Simulation(model, parameter_values=param) - -inputs = { - "Negative electrode active material volume fraction": 0.7, - "Positive electrode active material volume fraction": 0.7, -} -sol_old = sim.solve(t_eval=[0, 3600], inputs=inputs) - -inputs = { - "Negative electrode active material volume fraction": 0.6, - "Positive electrode active material volume fraction": 0.5, -} -sol_new = sim.solve(t_eval=[0, 3600], inputs=inputs) - -pybamm.dynamic_plot([sol_old, sol_new]) +models = [ + pybamm.lithium_ion.SPM({"particle shape": "spherical"}, name="spherical"), + pybamm.lithium_ion.SPM({"particle shape": "user"}, name="user"), +] +params = [models[0].default_parameter_values, models[0].default_parameter_values] + +# set same mesh for all models +var = pybamm.standard_spatial_vars +var_pts = {var.x_n: 5, var.x_s: 5, var.x_p: 5, var.r_n: 5, var.r_p: 5} + + +for model, param in zip(models, params): + if model.name == "user": + R_n = param["Negative particle radius [m]"] + R_p = param["Positive particle radius [m]"] + eps_s_n = param["Negative electrode active material volume fraction"] + eps_s_p = param["Positive electrode active material volume fraction"] + + param.update( + { + "Negative electrode surface area to volume ratio [m-1]": 3 + * eps_s_n + / R_n, + "Positive electrode surface area to volume ratio [m-1]": 3 + * eps_s_p + / R_p, + "Negative surface area per unit volume distribution in x": 1, + "Positive surface area per unit volume distribution in x": 1, + }, + check_already_exists=False, + ) + + param.process_model(model) + geometry = model.default_geometry + param.process_geometry(geometry) + mesh = pybamm.Mesh(geometry, model.default_submesh_types, var_pts) + disc = pybamm.Discretisation(mesh, model.default_spatial_methods) + disc.process_model(model) + + +# solve model +solutions = [] +t_eval = np.linspace(0, 3600, 100) +for model in models: + solution = pybamm.CasadiSolver().solve(model, t_eval) + solutions.append(solution) + +pybamm.dynamic_plot(solutions) diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_compare_outputs.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_compare_outputs.py index 868903c24c..fb338965c3 100644 --- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_compare_outputs.py +++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_compare_outputs.py @@ -55,7 +55,7 @@ def test_compare_outputs_thermal(self): # load models - for the default params we expect x-full and lumped to # agree as the temperature is practically independent of x options = [{"thermal": opt} for opt in ["lumped", "x-full"]] - options.append({"thermal": "lumped", "cell_geometry": "pouch"}) + options.append({"thermal": "lumped", "cell geometry": "pouch"}) model_combos = [ ([pybamm.lithium_ion.SPM(opt) for opt in options]), @@ -108,6 +108,59 @@ def test_compare_outputs_thermal(self): comparison = StandardOutputComparison(solutions) comparison.test_all(skip_first_timestep=True) + def test_compare_particle_shape(self): + models = [ + pybamm.lithium_ion.SPM({"particle shape": "spherical"}, name="spherical"), + pybamm.lithium_ion.SPM({"particle shape": "user"}, name="user"), + ] + params = [ + models[0].default_parameter_values, + models[0].default_parameter_values, + ] + + # set same mesh for all models + var = pybamm.standard_spatial_vars + var_pts = {var.x_n: 5, var.x_s: 5, var.x_p: 5, var.r_n: 5, var.r_p: 5} + + for model, param in zip(models, params): + if model.name == "user": + R_n = param["Negative particle radius [m]"] + R_p = param["Positive particle radius [m]"] + eps_s_n = param["Negative electrode active material volume fraction"] + eps_s_p = param["Positive electrode active material volume fraction"] + + param.update( + { + "Negative electrode surface area to volume ratio [m-1]": 3 + * eps_s_n + / R_n, + "Positive electrode surface area to volume ratio [m-1]": 3 + * eps_s_p + / R_p, + "Negative surface area per unit volume distribution in x": 1, + "Positive surface area per unit volume distribution in x": 1, + }, + check_already_exists=False, + ) + + param.process_model(model) + geometry = model.default_geometry + param.process_geometry(geometry) + mesh = pybamm.Mesh(geometry, model.default_submesh_types, var_pts) + disc = pybamm.Discretisation(mesh, model.default_spatial_methods) + disc.process_model(model) + + # solve model + solutions = [] + t_eval = np.linspace(0, 3600, 100) + for model in models: + solution = pybamm.CasadiSolver().solve(model, t_eval) + solutions.append(solution) + + # compare outputs + comparison = StandardOutputComparison(solutions) + comparison.test_all(skip_first_timestep=True) + if __name__ == "__main__": print("Add -v for more debug output") diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_dfn_x.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_dfn_x.py deleted file mode 100644 index a1c77764fb..0000000000 --- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_dfn_x.py +++ /dev/null @@ -1,33 +0,0 @@ -# -# Tests for the lithium-ion DFN model -# -import pybamm -import tests - -import unittest - - -class TestDFN(unittest.TestCase): - def test_particle_distribution_in_x(self): - model = pybamm.lithium_ion.DFN() - param = model.default_parameter_values - - def negative_distribution(x): - return 1 + x - - def positive_distribution(x): - return 1 + (x - (1 - model.param.l_p)) - - param["Negative particle distribution in x"] = negative_distribution - param["Positive particle distribution in x"] = positive_distribution - modeltest = tests.StandardModelTest(model, parameter_values=param) - modeltest.test_all() - - -if __name__ == "__main__": - print("Add -v for more debug output") - import sys - - if "-v" in sys.argv: - debug = True - unittest.main() diff --git a/tests/unit/test_geometry/test_battery_geometry.py b/tests/unit/test_geometry/test_battery_geometry.py index cf607447fc..668a4b2234 100644 --- a/tests/unit/test_geometry/test_battery_geometry.py +++ b/tests/unit/test_geometry/test_battery_geometry.py @@ -33,6 +33,10 @@ def test_geometry(self): geometry = pybamm.battery_geometry(include_particles=False) self.assertNotIn("negative particle", geometry) + def test_geometry_error(self): + with self.assertRaisesRegex(pybamm.GeometryError, "Invalid current"): + pybamm.battery_geometry(current_collector_dimension=4) + class TestReadParameters(unittest.TestCase): # This is the most complicated geometry and should test the parameters are diff --git a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py index 2463bca6f2..eb92e1fc8b 100644 --- a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py +++ b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py @@ -123,10 +123,16 @@ def test_options(self): pybamm.BaseBatteryModel({"current collector": "bad current collector"}) with self.assertRaisesRegex(pybamm.OptionError, "thermal model"): pybamm.BaseBatteryModel({"thermal": "bad thermal"}) + with self.assertRaisesRegex(pybamm.OptionError, "Unknown geometry"): + pybamm.BaseBatteryModel({"cell geometry": "bad geometry"}) with self.assertRaisesRegex( pybamm.OptionError, "Dimension of current collectors" ): pybamm.BaseBatteryModel({"dimensionality": 5}) + with self.assertRaisesRegex(pybamm.OptionError, "current collector"): + pybamm.BaseBatteryModel( + {"dimensionality": 1, "current collector": "bad option"} + ) with self.assertRaisesRegex(pybamm.OptionError, "surface form"): pybamm.BaseBatteryModel({"surface form": "bad surface form"}) with self.assertRaisesRegex(pybamm.OptionError, "convection option"): diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_base_lithium_ion_model.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_base_lithium_ion_model.py index a9654e3fe6..05860b0822 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_base_lithium_ion_model.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_base_lithium_ion_model.py @@ -9,6 +9,10 @@ class TestBaseLithiumIonModel(unittest.TestCase): def test_incompatible_options(self): with self.assertRaisesRegex(pybamm.OptionError, "convection not implemented"): pybamm.lithium_ion.BaseModel({"convection": "uniform transverse"}) + with self.assertRaisesRegex(pybamm.OptionError, "x-lumped"): + pybamm.lithium_ion.BaseModel( + {"cell geometry": "arbitrary", "thermal": "x-lumped"} + ) if __name__ == "__main__": diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py index 1c49f0f3bf..cffff2e2f3 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py @@ -93,6 +93,11 @@ def test_particle_fast_diffusion(self): model = pybamm.lithium_ion.DFN(options) model.check_well_posedness() + def test_particle_shape_user(self): + options = {"particle shape": "user"} + model = pybamm.lithium_ion.DFN(options) + model.check_well_posedness() + def test_surface_form_differential(self): options = {"surface form": "differential"} model = pybamm.lithium_ion.DFN(options) diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_spm.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_spm.py index 7f581711c1..1e3c2f16da 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_spm.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_spm.py @@ -94,6 +94,11 @@ def test_particle_fast_diffusion(self): model = pybamm.lithium_ion.SPM(options) model.check_well_posedness() + def test_particle_shape_user(self): + options = {"particle shape": "user"} + model = pybamm.lithium_ion.SPM(options) + model.check_well_posedness() + def test_surface_form_differential(self): options = {"surface form": "differential"} model = pybamm.lithium_ion.SPM(options) diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_spme.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_spme.py index f85543ae54..aeb7145a57 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_spme.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_spme.py @@ -93,6 +93,11 @@ def test_particle_fast_diffusion(self): model = pybamm.lithium_ion.SPMe(options) model.check_well_posedness() + def test_particle_shape_user(self): + options = {"particle shape": "user"} + model = pybamm.lithium_ion.SPMe(options) + model.check_well_posedness() + def test_surface_form_differential(self): options = {"surface form": "differential"} model = pybamm.lithium_ion.SPMe(options) diff --git a/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_surface_form/test_full_surface_form_conductivity.py b/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_surface_form/test_full_surface_form_conductivity.py index 20a5ca4811..327f8e85b0 100644 --- a/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_surface_form/test_full_surface_form_conductivity.py +++ b/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_surface_form/test_full_surface_form_conductivity.py @@ -25,6 +25,7 @@ def test_public_functions(self): "Negative electrode porosity": a_n, "Negative electrolyte tortuosity": a_n, "Negative electrode tortuosity": a_n, + "Negative surface area per unit volume distribution in x": a_n, "Negative electrolyte concentration": a_n, "Sum of negative electrode interfacial current densities": a_n, "Electrolyte potential": pybamm.Concatenation(a_n, a_s, a_p), @@ -54,6 +55,7 @@ def test_public_functions(self): "Positive electrode porosity": a_p, "Positive electrolyte tortuosity": a_p, "Positive electrode tortuosity": a_p, + "Positive surface area per unit volume distribution in x": a_p, "Positive electrolyte concentration": a_p, "Sum of positive electrode interfacial current densities": a_p, "Positive electrode temperature": a_p, diff --git a/tests/unit/test_parameters/test_standard_parameters_lead_acid.py b/tests/unit/test_parameters/test_lead_acid_parameters.py similarity index 100% rename from tests/unit/test_parameters/test_standard_parameters_lead_acid.py rename to tests/unit/test_parameters/test_lead_acid_parameters.py diff --git a/tests/unit/test_parameters/test_dimensionless_parameter_values_lithium_ion.py b/tests/unit/test_parameters/test_lithium_ion_parameters.py similarity index 97% rename from tests/unit/test_parameters/test_dimensionless_parameter_values_lithium_ion.py rename to tests/unit/test_parameters/test_lithium_ion_parameters.py index 84516b0109..8d48a84b10 100644 --- a/tests/unit/test_parameters/test_dimensionless_parameter_values_lithium_ion.py +++ b/tests/unit/test_parameters/test_lithium_ion_parameters.py @@ -8,6 +8,10 @@ class TestDimensionlessParameterValues(unittest.TestCase): + def test_options(self): + with self.assertRaisesRegex(pybamm.OptionError, "particle shape"): + pybamm.LithiumIonParameters({"particle shape": "bad shape"}) + def test_lithium_ion(self): """This test checks that all the dimensionless parameters are being calculated correctly for the specific set of parameters for LCO from dualfoil. The values From b1c97a0c15fcb2aa1600272e6d927c5c216b4b89 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Fri, 31 Jul 2020 20:31:48 +0100 Subject: [PATCH 06/10] #1115 remove old parameters --- .../lithium-ion/anodes/graphite_Chen2020/parameters.csv | 3 +-- .../lithium-ion/anodes/graphite_Ecker2015/parameters.csv | 1 - .../lithium-ion/anodes/graphite_Kim2011/parameters.csv | 1 - .../anodes/graphite_Ramadass2004/parameters.csv | 1 - .../anodes/graphite_UMBL_Mohtat2020/parameters.csv | 7 +++---- .../anodes/graphite_mcmb2528_Marquis2019/parameters.csv | 1 - .../lithium-ion/cathodes/LiNiCoO2_Ecker2015/parameters.csv | 1 - .../cathodes/NMC_UMBL_Mohtat2020/parameters.csv | 3 +-- .../lithium-ion/cathodes/lico2_Marquis2019/parameters.csv | 1 - .../lithium-ion/cathodes/lico2_Ramadass2004/parameters.csv | 3 +-- .../lithium-ion/cathodes/nca_Kim2011/parameters.csv | 1 - .../lithium-ion/cathodes/nmc_Chen2020/parameters.csv | 3 +-- 12 files changed, 7 insertions(+), 19 deletions(-) diff --git a/pybamm/input/parameters/lithium-ion/anodes/graphite_Chen2020/parameters.csv b/pybamm/input/parameters/lithium-ion/anodes/graphite_Chen2020/parameters.csv index 2c6f7a47c1..36af231e09 100644 --- a/pybamm/input/parameters/lithium-ion/anodes/graphite_Chen2020/parameters.csv +++ b/pybamm/input/parameters/lithium-ion/anodes/graphite_Chen2020/parameters.csv @@ -12,7 +12,6 @@ Negative electrode porosity,0.25,Chen 2020, Negative electrode active material volume fraction,0.75,Chen 2020, Negative particle radius [m],5.86E-6,Chen 2020, Negative particle distribution in x,1,default, -Negative electrode surface area to volume ratio [m-1],383959,Chen 2020, Negative electrode Bruggeman coefficient (electrolyte),1.5,Chen 2020,theoretical Negative electrode Bruggeman coefficient (electrode),1.5,default, ,,, @@ -30,4 +29,4 @@ Negative electrode density [kg.m-3],1657,default, # Thermal parameters,,, Negative electrode specific heat capacity [J.kg-1.K-1],700,default, Negative electrode thermal conductivity [W.m-1.K-1],1.7,default, -Negative electrode OCP entropic change [V.K-1],0,, \ No newline at end of file +Negative electrode OCP entropic change [V.K-1],0,, diff --git a/pybamm/input/parameters/lithium-ion/anodes/graphite_Ecker2015/parameters.csv b/pybamm/input/parameters/lithium-ion/anodes/graphite_Ecker2015/parameters.csv index 0cc17d38f1..f59ba699ae 100644 --- a/pybamm/input/parameters/lithium-ion/anodes/graphite_Ecker2015/parameters.csv +++ b/pybamm/input/parameters/lithium-ion/anodes/graphite_Ecker2015/parameters.csv @@ -14,7 +14,6 @@ Negative electrode porosity,0.329,, Negative electrode active material volume fraction, 0.372403,, Negative particle radius [m],1.37E-05,, Negative particle distribution in x,1,, -Negative electrode surface area to volume ratio [m-1], 81548,, Negative electrode Bruggeman coefficient (electrolyte),1.6372789338386007,Solve for permeability factor B=0.162=eps^b, Negative electrode Bruggeman coefficient (electrode),0,No Bruggeman correction to the solid conductivity, ,,, diff --git a/pybamm/input/parameters/lithium-ion/anodes/graphite_Kim2011/parameters.csv b/pybamm/input/parameters/lithium-ion/anodes/graphite_Kim2011/parameters.csv index cdd353502b..7322e75402 100644 --- a/pybamm/input/parameters/lithium-ion/anodes/graphite_Kim2011/parameters.csv +++ b/pybamm/input/parameters/lithium-ion/anodes/graphite_Kim2011/parameters.csv @@ -12,7 +12,6 @@ Negative electrode porosity,0.4,, Negative electrode active material volume fraction,0.51,, Negative particle radius [m],5.083E-7,, Negative particle distribution in x,1,, -Negative electrode surface area to volume ratio [m-1],3.01E6,, Negative electrode Bruggeman coefficient (electrolyte),2,, Negative electrode Bruggeman coefficient (electrode),2,, ,,, diff --git a/pybamm/input/parameters/lithium-ion/anodes/graphite_Ramadass2004/parameters.csv b/pybamm/input/parameters/lithium-ion/anodes/graphite_Ramadass2004/parameters.csv index 9661945ee8..12fba05b21 100644 --- a/pybamm/input/parameters/lithium-ion/anodes/graphite_Ramadass2004/parameters.csv +++ b/pybamm/input/parameters/lithium-ion/anodes/graphite_Ramadass2004/parameters.csv @@ -12,7 +12,6 @@ Negative electrode porosity,0.485,Ramadass,electrolyte volume fraction Negative electrode active material volume fraction,0.49,Ramadass, Negative particle radius [m],2e-06,Ramadass, Negative particle distribution in x,1,, -Negative electrode surface area to volume ratio [m-1],735000, 3eps.radi-1, Negative electrode Bruggeman coefficient (electrolyte),4,Guess, Negative electrode Bruggeman coefficient (electrode),4,Ramadass, ,,, diff --git a/pybamm/input/parameters/lithium-ion/anodes/graphite_UMBL_Mohtat2020/parameters.csv b/pybamm/input/parameters/lithium-ion/anodes/graphite_UMBL_Mohtat2020/parameters.csv index 0a8fa09328..7f4b9188fb 100644 --- a/pybamm/input/parameters/lithium-ion/anodes/graphite_UMBL_Mohtat2020/parameters.csv +++ b/pybamm/input/parameters/lithium-ion/anodes/graphite_UMBL_Mohtat2020/parameters.csv @@ -2,18 +2,17 @@ Name [units],Value,Reference,Notes # Empty rows and rows starting with ‘#’ will be ignored,,, ,,, # Electrode properties,,, -Negative electrode conductivity [S.m-1],100,Scott Moura FastDFN,no info from Peyman MPM +Negative electrode conductivity [S.m-1],100,Scott Moura FastDFN,no info from Peyman MPM Maximum concentration in negative electrode [mol.m-3],28746,Peyman MPM, Negative electrode diffusion coefficient [m2.s-1],5.0E-15,Peyman MPM, Negative electrode diffusivity [m2.s-1],[function]graphite_diffusivity_PeymanMPM,, Negative electrode OCP [V],[function]graphite_ocp_PeymanMPM,Peyman MPM, ,,, # Microstructure,,, -Negative electrode porosity,0.3,Peyman MPM, +Negative electrode porosity,0.3,Peyman MPM, Negative electrode active material volume fraction,0.61,Peyman MPM,rest is binder Negative particle radius [m],2.5E-06,Peyman MPM, Negative particle distribution in x,1,, -Negative electrode surface area to volume ratio [m-1],732000,Peyman MPM,Eq. (48) Negative electrode Bruggeman coefficient (electrode),1.5,Peyman MPM, Negative electrode Bruggeman coefficient (electrolyte),1.5,Peyman MPM, Negative electrode tortuosity, 0.16, @@ -28,7 +27,7 @@ Negative electrode double-layer capacity [F.m-2],0.2,,no info from Peyman MPM Negative electrode exchange-current density [A.m-2],[function]graphite_electrolyte_exchange_current_density_PeymanMPM,, ,,, # Density,,, -Negative electrode density [kg.m-3],3100,Peyman MPM, cell lumped value +Negative electrode density [kg.m-3],3100,Peyman MPM, cell lumped value ,,, # Thermal parameters,,, Negative electrode specific heat capacity [J.kg-1.K-1],1100,Peyman MPM,cell lumped value diff --git a/pybamm/input/parameters/lithium-ion/anodes/graphite_mcmb2528_Marquis2019/parameters.csv b/pybamm/input/parameters/lithium-ion/anodes/graphite_mcmb2528_Marquis2019/parameters.csv index 49cb9e5c62..02f5ad6747 100644 --- a/pybamm/input/parameters/lithium-ion/anodes/graphite_mcmb2528_Marquis2019/parameters.csv +++ b/pybamm/input/parameters/lithium-ion/anodes/graphite_mcmb2528_Marquis2019/parameters.csv @@ -12,7 +12,6 @@ Negative electrode porosity,0.3,Scott Moura FastDFN,electrolyte volume fraction Negative electrode active material volume fraction,0.6,, Negative particle radius [m],1E-05,Scott Moura FastDFN, Negative particle distribution in x,1,, -Negative electrode surface area to volume ratio [m-1],180000,Scott Moura FastDFN, Negative electrode Bruggeman coefficient (electrolyte),1.5,Scott Moura FastDFN, Negative electrode Bruggeman coefficient (electrode),1.5,Scott Moura FastDFN, ,,, diff --git a/pybamm/input/parameters/lithium-ion/cathodes/LiNiCoO2_Ecker2015/parameters.csv b/pybamm/input/parameters/lithium-ion/cathodes/LiNiCoO2_Ecker2015/parameters.csv index 483457981c..3e13409322 100644 --- a/pybamm/input/parameters/lithium-ion/cathodes/LiNiCoO2_Ecker2015/parameters.csv +++ b/pybamm/input/parameters/lithium-ion/cathodes/LiNiCoO2_Ecker2015/parameters.csv @@ -14,7 +14,6 @@ Positive electrode porosity,0.296,, Positive electrode active material volume fraction, 0.40832,, Positive particle radius [m],6.5E-06,, Positive particle distribution in x,1,, -Positive electrode surface area to volume ratio [m-1],188455,, Positive electrode Bruggeman coefficient (electrolyte),1.5442267190786427,Solve for permeability factor B=0.1526=eps^b, Positive electrode Bruggeman coefficient (electrode),0,No Bruggeman correction to solid conductivity, Positive electrode exchange-current density [A.m-2],[function]nco_electrolyte_exchange_current_density_Ecker2015,, diff --git a/pybamm/input/parameters/lithium-ion/cathodes/NMC_UMBL_Mohtat2020/parameters.csv b/pybamm/input/parameters/lithium-ion/cathodes/NMC_UMBL_Mohtat2020/parameters.csv index 8fb117d6b3..94d301c422 100644 --- a/pybamm/input/parameters/lithium-ion/cathodes/NMC_UMBL_Mohtat2020/parameters.csv +++ b/pybamm/input/parameters/lithium-ion/cathodes/NMC_UMBL_Mohtat2020/parameters.csv @@ -12,7 +12,6 @@ Positive electrode porosity,0.3,Peyman MPM, Positive electrode active material volume fraction,0.445,Peyman MPM,rest is binder Positive particle radius [m],3.5E-06,Peyman MPM, Positive particle distribution in x,1,, -Positive electrode surface area to volume ratio [m-1],3.8143E+5,Peyman MPM,Eq. (48) Positive electrode Bruggeman coefficient (electrode),1.5,Peyman MPM, Positive electrode Bruggeman coefficient (electrolyte),1.5,Peyman MPM, Positive electrode tortuosity,0.16, @@ -27,7 +26,7 @@ Positive electrode double-layer capacity [F.m-2],0.2,,no info from Peyman MPM Positive electrode exchange-current density [A.m-2],[function]NMC_electrolyte_exchange_current_density_PeymanMPM,, ,,, # Density,,, -Positive electrode density [kg.m-3],3100,Peyman MPM, cell lumped value +Positive electrode density [kg.m-3],3100,Peyman MPM, cell lumped value ,,, # Thermal parameters,,, Positive electrode specific heat capacity [J.kg-1.K-1],1100,Peyman MPM, cell lumped value diff --git a/pybamm/input/parameters/lithium-ion/cathodes/lico2_Marquis2019/parameters.csv b/pybamm/input/parameters/lithium-ion/cathodes/lico2_Marquis2019/parameters.csv index c20d490dea..93d0b43ed0 100644 --- a/pybamm/input/parameters/lithium-ion/cathodes/lico2_Marquis2019/parameters.csv +++ b/pybamm/input/parameters/lithium-ion/cathodes/lico2_Marquis2019/parameters.csv @@ -12,7 +12,6 @@ Positive electrode porosity,0.3,Scott Moura FastDFN,electrolyte volume fraction Positive electrode active material volume fraction,0.5,, Positive particle radius [m],1E-05,Scott Moura FastDFN, Positive particle distribution in x,1,, -Positive electrode surface area to volume ratio [m-1],150000,Scott Moura FastDFN, Positive electrode Bruggeman coefficient (electrolyte),1.5,Scott Moura FastDFN, Positive electrode Bruggeman coefficient (electrode),1.5,Scott Moura FastDFN, ,,, diff --git a/pybamm/input/parameters/lithium-ion/cathodes/lico2_Ramadass2004/parameters.csv b/pybamm/input/parameters/lithium-ion/cathodes/lico2_Ramadass2004/parameters.csv index 5691350e9f..8d26275f26 100644 --- a/pybamm/input/parameters/lithium-ion/cathodes/lico2_Ramadass2004/parameters.csv +++ b/pybamm/input/parameters/lithium-ion/cathodes/lico2_Ramadass2004/parameters.csv @@ -12,7 +12,6 @@ Positive electrode porosity,0.385,Ramadass,electrolyte volume fraction Positive electrode active material volume fraction,0.59,Ramadass, Positive particle radius [m],2e-06,Ramadass, Positive particle distribution in x,1,, -Positive electrode surface area to volume ratio [m-1],885000,3eps.radi-1, Positive electrode Bruggeman coefficient (electrolyte),4,Ramadass, Positive electrode Bruggeman coefficient (electrode),4,Ramadass, ,,, @@ -30,4 +29,4 @@ Positive electrode density [kg.m-3],3262,, # Thermal parameters,,, Positive electrode specific heat capacity [J.kg-1.K-1],700,, Positive electrode thermal conductivity [W.m-1.K-1],2.1,, -Positive electrode OCP entropic change [V.K-1],[function]lico2_entropic_change_Moura2016,, \ No newline at end of file +Positive electrode OCP entropic change [V.K-1],[function]lico2_entropic_change_Moura2016,, diff --git a/pybamm/input/parameters/lithium-ion/cathodes/nca_Kim2011/parameters.csv b/pybamm/input/parameters/lithium-ion/cathodes/nca_Kim2011/parameters.csv index 0a9e33c0e8..0a0f00fe42 100644 --- a/pybamm/input/parameters/lithium-ion/cathodes/nca_Kim2011/parameters.csv +++ b/pybamm/input/parameters/lithium-ion/cathodes/nca_Kim2011/parameters.csv @@ -12,7 +12,6 @@ Positive electrode porosity,0.4,, Positive electrode active material volume fraction,0.41,, Positive particle radius [m],1.633E-6,, Positive particle distribution in x,1,, -Positive electrode surface area to volume ratio [m-1],0.753E6,, Positive electrode Bruggeman coefficient (electrolyte),2,, Positive electrode Bruggeman coefficient (electrode),2,, ,,, diff --git a/pybamm/input/parameters/lithium-ion/cathodes/nmc_Chen2020/parameters.csv b/pybamm/input/parameters/lithium-ion/cathodes/nmc_Chen2020/parameters.csv index 58b80b334c..997f4ba416 100644 --- a/pybamm/input/parameters/lithium-ion/cathodes/nmc_Chen2020/parameters.csv +++ b/pybamm/input/parameters/lithium-ion/cathodes/nmc_Chen2020/parameters.csv @@ -12,7 +12,6 @@ Positive electrode porosity,0.335,Chen 2020, Positive electrode active material volume fraction,0.665,Chen 2020, Positive particle radius [m],5.22E-6,Chen 2020, Positive particle distribution in x,1,default, -Positive electrode surface area to volume ratio [m-1],382184,Chen 2020, Positive electrode Bruggeman coefficient (electrolyte),1.5,Chen 2020,theoretical Positive electrode Bruggeman coefficient (electrode),1.5,default, ,,, @@ -30,4 +29,4 @@ Positive electrode density [kg.m-3],3262,default, # Thermal parameters,,, Positive electrode specific heat capacity [J.kg-1.K-1],700,default, Positive electrode thermal conductivity [W.m-1.K-1],2.1,default, -Positive electrode OCP entropic change [V.K-1],0,, \ No newline at end of file +Positive electrode OCP entropic change [V.K-1],0,, From 47708c47a33c62deff902a682f9d1169aa038d9c Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Fri, 31 Jul 2020 20:32:35 +0100 Subject: [PATCH 07/10] #1115 remove local files --- basic_test.py | 52 --------------------------------------------------- test.py | 51 -------------------------------------------------- 2 files changed, 103 deletions(-) delete mode 100644 basic_test.py delete mode 100644 test.py diff --git a/basic_test.py b/basic_test.py deleted file mode 100644 index 589b386d24..0000000000 --- a/basic_test.py +++ /dev/null @@ -1,52 +0,0 @@ -import pybamm - -models = [ - # pybamm.lithium_ion.DFN( - # options={"particle": "fast diffusion"}, name="fastDFN particle" - # ), - pybamm.lithium_ion.DFN(name="DFN particle"), - pybamm.lithium_ion.DFN(name="DFN"), -] -parameters = pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Ecker2015) - - -def negative_distribution(x): - return 1 + x - - -def positive_distribution(x): - return 1 + (x - (1 - models[1].param.l_p)) - - -parameters["Negative particle distribution in x"] = negative_distribution -parameters["Positive particle distribution in x"] = positive_distribution -parameters["Negative electrode diffusivity [m2.s-1]"] = 1e-1 -parameters["Positive electrode diffusivity [m2.s-1]"] = 1e-1 -sols = [] - -for model in models: - if model.name == "DFN": - params = pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Ecker2015) - params["Negative electrode diffusivity [m2.s-1]"] = 1e-1 - params["Positive electrode diffusivity [m2.s-1]"] = 1e-1 - else: - params = parameters - sim = pybamm.Simulation(model, parameter_values=params) - sol = sim.solve([0, 3600]) - sols.append(sol) - - -output_variables = [ - "Negative particle surface concentration", - "Electrolyte concentration", - "Positive particle surface concentration", - "Current [A]", - "Negative electrode potential [V]", - "Electrolyte potential [V]", - "Positive electrode potential [V]", - "Terminal voltage [V]", - "Negative particle distribution in x", - "Positive particle distribution in x", -] - -pybamm.dynamic_plot(sols, output_variables=output_variables) diff --git a/test.py b/test.py deleted file mode 100644 index 4e7d6ed905..0000000000 --- a/test.py +++ /dev/null @@ -1,51 +0,0 @@ -import pybamm -import numpy as np - -models = [ - pybamm.lithium_ion.SPM({"particle shape": "spherical"}, name="spherical"), - pybamm.lithium_ion.SPM({"particle shape": "user"}, name="user"), -] -params = [models[0].default_parameter_values, models[0].default_parameter_values] - -# set same mesh for all models -var = pybamm.standard_spatial_vars -var_pts = {var.x_n: 5, var.x_s: 5, var.x_p: 5, var.r_n: 5, var.r_p: 5} - - -for model, param in zip(models, params): - if model.name == "user": - R_n = param["Negative particle radius [m]"] - R_p = param["Positive particle radius [m]"] - eps_s_n = param["Negative electrode active material volume fraction"] - eps_s_p = param["Positive electrode active material volume fraction"] - - param.update( - { - "Negative electrode surface area to volume ratio [m-1]": 3 - * eps_s_n - / R_n, - "Positive electrode surface area to volume ratio [m-1]": 3 - * eps_s_p - / R_p, - "Negative surface area per unit volume distribution in x": 1, - "Positive surface area per unit volume distribution in x": 1, - }, - check_already_exists=False, - ) - - param.process_model(model) - geometry = model.default_geometry - param.process_geometry(geometry) - mesh = pybamm.Mesh(geometry, model.default_submesh_types, var_pts) - disc = pybamm.Discretisation(mesh, model.default_spatial_methods) - disc.process_model(model) - - -# solve model -solutions = [] -t_eval = np.linspace(0, 3600, 100) -for model in models: - solution = pybamm.CasadiSolver().solve(model, t_eval) - solutions.append(solution) - -pybamm.dynamic_plot(solutions) From 03f00398a287d1f2000f736128e17cb5f5648074 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Tue, 4 Aug 2020 12:54:59 +0100 Subject: [PATCH 08/10] #1115 fix examples and changelog --- CHANGELOG.md | 6 +++++- SPMe.pkl | Bin 0 -> 5422508 bytes SPMe_sol.pkl | Bin 0 -> 4304319 bytes examples/scripts/SPMe_SOC.py | 6 +----- .../submodels/interface/base_interface.py | 20 ++++++++---------- sol_data.pkl | Bin 0 -> 1826 bytes squared.py | 3 +++ 7 files changed, 18 insertions(+), 17 deletions(-) create mode 100644 SPMe.pkl create mode 100644 SPMe_sol.pkl create mode 100644 sol_data.pkl create mode 100644 squared.py diff --git a/CHANGELOG.md b/CHANGELOG.md index 0708c5de71..da87a88d6f 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,7 +2,9 @@ ## Features -- Added "R-averaged particle concentration" variables` ([#1118](https://github.com/pybamm-team/PyBaMM/pull/1118)) + +- Automatically compute surface area per unit volume based on particle shape for li-ion models ([#1120])(https://github.com/pybamm-team/PyBaMM/pull/1120) +- Added "R-averaged particle concentration" variables ([#1118](https://github.com/pybamm-team/PyBaMM/pull/1118)) - Added support for sensitivity calculations to the casadi solver ([#1109](https://github.com/pybamm-team/PyBaMM/pull/1109)) - Added support for index 1 semi-explicit dae equations and sensitivity calculations to JAX BDF solver ([#1107](https://github.com/pybamm-team/PyBaMM/pull/1107)) - Allowed keyword arguments to be passed to `Simulation.plot()` ([#1099](https://github.com/pybamm-team/PyBaMM/pull/1099)) @@ -18,6 +20,8 @@ ## Breaking changes +- The modules containing standard parameters are now classes so they can take options +(e.g. `standard_parameters_lithium_ion` is now `LithiumIonParameters`) ([#1120])(https://github.com/pybamm-team/PyBaMM/pull/1120) - Renamed `quick_plot_vars` to `output_variables` in `Simulation` to be consistent with `QuickPlot`. 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