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A multi-step parameter identification of a physico-chemical lithium-ion battery model with electrochemical impedance data

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journal contribution
posted on 2023-07-31, 11:00 authored by Buddhi Wimarshana, Izzuan Bin-Mat-ArishadIzzuan Bin-Mat-Arishad, Ashley FlyAshley Fly

Physico-chemical battery models are widely used in the design and simulation of lithium-ion batteries due to their physically descriptive modelling approach. The model accuracy highly depends on the accurate identification of model parameters, yet accurate and feasible model parameterisation with state-of-the-art experimental techniques is laborious, expensive and tied to inherent measurement and estimation errors. Multi-step voltage-based data-driven parameter identification techniques are widely adopted in the literature to tackle this challenge. However, impedance-based parameter identification schemes lack a similar level of detailed analysis. Therefore, we propose a novel multi-step electrochemical impedance spectroscopy (EIS) based data-driven parameter identification framework to identify kinetic parameters of a physico-chemical battery model utilising particle swarm optimisation featuring metaheuristic optimisation capabilities. The parameter optimisation framework is designed methodically to identify parameter clusters with distinct sensitivities to specific EIS impedance regions, significantly improving identification accuracy. The generic particle swarm optimisation is fused with nature-inspired Darwinian events cross-generating new particles using selected parents to improve the algorithm’s predictions. The proposed data-driven parameter identification framework is benchmarked under multiple cell operating conditions and achieves a voltage prediction improvement of 28% for constant current discharge and 65% for transient duty cycles compared to an experimentally derived parameter set from the literature.

Funding

UK Research and Innovation

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

Journal of Power Sources

Volume

580

Publisher

Elsevier

Version

  • VoR (Version of Record)

Rights holder

© The Author(s)

Publisher statement

This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

2023-07-07

Publication date

2023-07-22

Copyright date

2023

ISSN

0378-7753

eISSN

1873-2755

Language

  • en

Depositor

Deposit date: 26 July 2023

Article number

233400

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