How to parameterise an equivalent-circuit empirical battery model from time-domain data

Empirical battery models have many uses, including pack design, charge estimation, and cell selection. An accurate model requires accurate parameters, however there are a range of subtleties in parameter fitting that are often missed in the literature. This work presents a best-practice guide for parameterisation, covering design of appropriate experiments; using numerical optimisers to fit model parameters to the experimental data; and building a suitable interpolation of the fitted model parameters, to get the required parameter-functions for a model. Key novelties include the importance of using timescales in place of capacitances for numerical stability; the design of a multi-step parameterisation process where model complexity is gradually increased to give more accurate parameter estimates; and a combined numerical and experimental design, so that the right experiments, data, and fitting methods are chosen for the target application. Our method is presented as a general recipe, so that users can easily choose the right setup for their needs. Scripts are provided which implement the method and reproduce the provided examples. Additionally, a full set of experimental data, model scripts, and fitted parameters are provided, both for testing the described method and for re-use in modelling studies.

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Publication Details

Journal
MethodsX
Published
2026-03-14
DOI
https://doi.org/10.1016/j.mex.2026.103865
Primary Topic
Advanced Battery Technologies Research
Type
article
Field-Weighted Citation Impact
0.00
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article

How to parameterise an equivalent-circuit empirical battery model from time-domain data

Mark Blyth, Amey Gupta, Alastair Hales
MethodsX
Advanced Battery Technologies Research
article

How to parameterise an equivalent-circuit empirical battery model from time-domain data

Mark Blyth, Amey Gupta, Alastair Hales
article en

Abstract

Empirical battery models have many uses, including pack design, charge estimation, and cell selection. An accurate model requires accurate parameters, however there are a range of subtleties in parameter fitting that are often missed in the literature. This work presents a best-practice guide for parameterisation, covering design of appropriate experiments; using numerical optimisers to fit model parameters to the experimental data; and building a suitable interpolation of the fitted model parameters, to get the required parameter-functions for a model. Key novelties include the importance of using timescales in place of capacitances for numerical stability; the design of a multi-step parameterisation process where model complexity is gradually increased to give more accurate parameter estimates; and a combined numerical and experimental design, so that the right experiments, data, and fitting methods are chosen for the target application. Our method is presented as a general recipe, so that users can easily choose the right setup for their needs. Scripts are provided which implement the method and reproduce the provided examples. Additionally, a full set of experimental data, model scripts, and fitted parameters are provided, both for testing the described method and for re-use in modelling studies.

MethodsXVol. 16
University of Bristol (GB), The Faraday Institution (GB)
Openalex Percentile: Top 22%
Advanced Battery Technologies Research
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