EIS objective

Objectives for fitting the data generated by an Electrochemical Impedance Spectroscopy (EIS) experiment.

class ionworkspipeline.objectives.EIS(data_input, options=None, callbacks=None, custom_parameters=None, constraints=None, penalties=None, parameters=None)

Objective for electrochemical impedance spectroscopy (EIS) data. Simulates the model response at the given frequencies and compares the impedance to the data.

This objective uses PyBaMM’s built-in EIS functionality to simulate the experiment in the frequency domain.

Parameters

data_inputstr or dict

The data to use for the fit, see FittingObjective.

optionsdict, optional

A dictionary of options to pass to the objective.

  • model: :class:pybamm.BaseModel

    The model to fit. No default is provided, but this option is required (a model must be passed in).

  • simulation_kwargs: dict

    Keyword arguments to pass to the simulation (pybamm.EISSimulation). Default is None.

callbacksionworkspipeline.callbacks.Callback or list of callbacks

A class with methods that get called at various points during the datafit process

custom_parametersdict, optional

A dictionary of custom parameters to use for the objective. Deprecated, use parameters instead.

constraintslist[Constraint], optional

A list of equality and inequality constraints to apply to the objective.

penaltieslist[Penalty], optional

A list of penalties to apply to the objective.

parametersdict or pybamm.ParameterValues, optional

Objective-specific parameter values merged into the global parameter values before fitting. Default is None.

Extends: ionworkspipeline.data_fits.objectives.fitting_objective.InitialStateOfChargeMixin

build(parameter_values)

Build the objective.

Parameters

parameter_valuespybamm.ParameterValues

The parameter values to use for the objective.

classmethod default_options() → dict[str, Any]

Return the default options for the EIS objective.

prepare_validation_results(results: dict, summary_stats: list) → tuple[list[dict[str, float]], dict[str, float]]

Prepare validation results for EIS objective.

Parameters

resultsdict

The raw results from running the objective.

summary_statslist

List of summary statistic cost functions to compute.

Returns

tuple[list[dict[str, float]], dict[str, float]]

A tuple of (computed_statistics, validation_result) where: - computed_statistics is a list of dictionaries with computed metric names and values - validation_result is a dictionary of validation data