pybop.applications.gitt_methods#

Classes#

GITTFit

Fit the diffusion timescale of each pulse from a galvanostatic intermittent

GITTPulseFit

Fit the diffusion timescale of one pulse from a galvanostatic intermittent

Module Contents#

class pybop.applications.gitt_methods.GITTFit(gitt_dataset: pybop.Dataset, pulse_index: list[numpy.ndarray], parameter_values: pybamm.ParameterValues, cost: pybop.ErrorMeasure | pybop.LogLikelihood | None = None, optimiser: pybop.BaseOptimiser | None = None, optimiser_options: pybop.OptimiserOptions | None = None)[source]#

Bases: pybop.BaseApplication

Fit the diffusion timescale of each pulse from a galvanostatic intermittent titration technique (GITT) measurement.

Parameters:

  • gitt_dataset (pybop.Dataset) – A dataset containing the “Time [s]”, “Current [A]” and “Voltage [V]” for a GITT measurement.

  • pulse_index (list[np.ndarray]) – A nested list of integers representing the indices of each pulse in the dataset.

  • parameter_values (pybamm.ParameterValues) – A parameter set containing values for the parameters of the SPDiffusion model.

  • cost (pybop.ErrorMeasure | pybop.LogLikelihood, optional) – The cost function to quantify the error (default: pybop.RootMeanSquaredError).

  • optimiser (pybop.BaseOptimiser, optional) – The optimisation algorithm to use (default: pybop.SciPyMinimize).

  • optimiser_options (pybop.OptimiserOptions, optional) – Options for the optimiser.

__call__() → pybop.Dataset[source]#
cost#
gitt_dataset#
inverse_ocp#
optimiser#
optimiser_options#
pulse_fit#
pulse_index#
class pybop.applications.gitt_methods.GITTPulseFit(parameter_values: pybamm.ParameterValues, cost: pybop.ErrorMeasure | pybop.LogLikelihood | None = None, optimiser: pybop.BaseOptimiser | None = None, optimiser_options: pybop.OptimiserOptions | None = None)[source]#

Bases: pybop.BaseApplication

Fit the diffusion timescale of one pulse from a galvanostatic intermittent titration technique (GITT) measurement using the diffusion model for a single, spherical particle representing one electrode.

The cost function requires a “domain”-based weighting to fit (possibly non-uniform) data consistently across the observed time period.

Parameters:

  • parameter_values (pybamm.ParameterValues) – A parameter set containing values for the parameters of the SPDiffusion model.

  • cost (pybop.ErrorMeasure | pybop.LogLikelihood, optional) – The cost function to quantify the error (default: pybop.RootMeanSquaredError).

  • optimiser (pybop.BaseOptimiser, optional) – The optimisation algorithm to use (default: pybop.SciPyMinimize).

  • optimiser_options (pybop.OptimiserOptions, optional) – Options for the optimiser.

__call__(gitt_pulse: pybop.Dataset, initial_parameter_values: dict[str, float] | None = None) → pybop.Result[source]#
cost#
model#
optimiser#
optimiser_options#
parameter_values#
parameters#
problem = None#