EIS fit

Fit an exchange-current density from EIS data with ionworks.pipeline.

Build the fit with ionworks.pipeline, submit it through the API client, and read the typed result.

This example runs on synthetic sample data bundled with ionworks.pipeline, so you can copy it and run it as-is. Swap iw.pipeline.example_data(...) for your own export, or read a measurement from the platform with client.cell_measurement.

import ionworks as iw
import matplotlib.pyplot as plt
import pandas as pd
import pybamm

# Synthetic example spectrum: Frequency [Hz], Z_Re [Ohm], Z_Im [Ohm]. Swap
# in your own export, or read a measurement with `client.cell_measurement`.
data = pd.read_csv(iw.pipeline.example_data("eis_synthetic"))

# The Arrhenius form below needs an activation energy, which Chen2020
# does not define; zero gives j0 no temperature dependence.
baseline = pybamm.ParameterValues("Chen2020")
baseline["Negative electrode reaction activation energy [J.mol-1]"] = 0.0
known = iw.pipeline.direct_entries.DirectEntry(parameters=baseline)

# j0 = j0_ref (c_e/c_e0)^0.5 (c_s/c_smax)^0.5 (1 - c_s/c_smax)^0.5
#      exp(E_r/R (1/T_ref - 1/T)), replacing Chen2020's own j0 function.
j0_function = (
    iw.pipeline.direct_entries.arrhenius_butler_volmer_exchange_current_density(
        electrode="negative"
    )
)

# We fit j0_ref.
parameters = {
    "Negative electrode reference exchange-current density [A.m-2]": iw.pipeline.Parameter(
        "Negative electrode reference exchange-current density [A.m-2]",
        initial_value=1.0,
        bounds=(0.1, 10.0),
    ),
}

objectives = {
    "impedance": iw.pipeline.objectives.EIS(
        data=data,
        options=iw.pipeline.objectives.EISOptions(
            model=pybamm.lithium_ion.DFN(options={"surface form": "differential"})
        ),
    )
}
fit = iw.pipeline.DataFit(
    objectives=objectives,
    parameters=parameters,
    cost=iw.pipeline.costs.RMSE(),
    # Capped to keep the example quick; a harder fit needs more.
    optimizer=iw.pipeline.optimizers.ScipyMinimize(
        method="Nelder-Mead", max_iterations=20
    ),
)

client = iw.api.Client()
submission = client.pipeline.create(
    iw.pipeline.Pipeline({"known": known, "j0_function": j0_function, "eis": fit})
)
client.pipeline.wait_for_completion(submission.id)
result = client.pipeline.result(submission.id)
fit_result = result.element("eis")
figs = fit_result.plot_fit_results()
plt.show()