pybop.models.lithium_ion.cell_temperature#

Classes#

CellTemperature

A lumped thermal model for a battery cell.

Module Contents#

class pybop.models.lithium_ion.cell_temperature.CellTemperature(name='Cell Temperature Model', **model_kwargs)[source]#

Bases: pybop.models.lithium_ion.base_model.BaseGroupedModel

A lumped thermal model for a battery cell.

Parameters:

name (str, optional) – The name of the model.
**model_kwargs (optional) –
Valid PyBaMM model option keys and their values, for example: options : dict, optional

A dictionary of options to customise the behaviour of the PyBaMM model.

buildbool, optional
If True, the model is built upon creation (default: False).

U(sto, domain)[source]#: Dimensional open-circuit potential [V], calculated as U(x) = U_ref(x). Credit: PyBaMM

static create_grouped_parameters(parameter_values: pybamm.ParameterValues) → pybamm.ParameterValues[source]#

Create a parameter set for the Cell Temperature Model from a PyBaMM lithium-ion ParameterValues object.

Parameters:: parameter_values (pybamm.ParameterValues) – Parameters and their corresponding values.
Returns:: parameter_values – A new set of parameters and their values.
Return type:: pybamm.ParameterValues

property default_geometry#: Returns a dictionary of the default geometry for the model, which is empty by default.

property default_parameter_values: pybamm.ParameterValues#: Returns the default parameter values for the model (an empty set of parameters by default).

property default_quick_plot_variables#: Returns the default variables for quick plotting (None by default).

property default_spatial_methods#: Returns a dictionary of the default spatial methods for the model, which is empty by default.

property default_submesh_types#: Returns a dictionary of the default submesh types for the model, which is empty by default.

property default_var_pts#: Returns a dictionary of the default variable points for the model, which is empty by default.

variables#: Returns a dictionary mapping strings to expressions representing the model’s useful variables.