Cycle ageing objective

Objectives for fitting the data generated by a cycle ageing experiment.

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

Objective for fitting summary variables to cycling data.

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).

• experiment: pybamm.Experiment

  The experiment to use for the simulation. No default is provided, but this option is required (an experiment must be passed in).

• objective variables: list of strings

  The variables to fit. No default is provided, but this option is required (a list of variables must be passed in). The variables must be a subset of the keys in the data.

• metrics: dict of str to BaseMetric

  A dictionary mapping variable names to metric objects that extract values from the simulation solution. Each metric should be created with .by_cycle() to evaluate across cycles. The cycles will be set automatically from the data. Default metrics are provided for “LLI [%]”, “LAM_ne [%]”, and “LAM_pe [%]”.

• simulation_kwargs: dict

  Keyword arguments to pass to the simulation (iwp.Simulation). Default is None.

callbackslist of callable, optional

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.SimulationObjective

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 CycleAgeing objective.

default_validation_plot_types: list[str] | None = ['model data', 'error']

Default list of validation plot type names for this objective.

Subclasses that support validation should override. Return None to use the pipeline fallback (e.g. ["model data", "error"]); return [] for no plots.

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

Prepare validation results for CycleAgeing objective.

Parameters

resultsdict

The raw results from running the objective (keys = variable names, values = arrays per cycle).

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, and validation_result is a dictionary of validation data (Cycle number and per-variable model/data).