Detecting faulty lithium-ion cells in large-scale parallel battery packs using current distributions

Abstract

One of the main concerns affecting the uptake of battery packs is safety, particularly with respect to fires caused by cell faults. Mitigating possible risks from faults requires advances in battery management systems and an understanding of the dynamics of large packs. To address this, a machine learning classifier based upon a support vector machine was developed that detects cell faults within large packs using a limited number of current sensors. To train the classifier, a modelling framework for parallel-connected packs is introduced and shown to generalise to Doyle-FullerNewman electrochemical models. The fault classification performance was found to be satisfactory, with an accuracy of 83% using current information from only 27% of the cells. Validation on experimental pack data is also shown. These results highlight the potential to combine mathematical modelling and machine learning to improve battery management systems and deal with the complexities of large packs.

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Item Type:	Article
Authors/Creators:	Lambert, P. https://orcid.org/0009-0001-1433-0524 Drummond, R. https://orcid.org/0000-0002-2586-1718 Ross, J.P. Tredenick, E.C. https://orcid.org/0000-0001-9105-2858 Howey, D.A. https://orcid.org/0000-0002-0620-3955 Duncan, S.R.
Copyright, Publisher and Additional Information:	© The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Keywords:	Batteries; Electrical and electronic engineering
Dates:	Accepted: 29 October 2025 Published (online): 21 January 2026 Published: 21 January 2026
Institution:	The University of Sheffield
Academic Units:	The University of Sheffield > Faculty of Engineering (Sheffield) > School of Electrical and Electronic Engineering
Date Deposited:	23 Jan 2026 12:11
Last Modified:	23 Jan 2026 12:11
Status:	Published
Publisher:	Nature Research
Refereed:	Yes
Identification Number:	10.1038/s44172-025-00543-x
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:236832

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Detecting faulty lithium-ion cells in large-scale parallel battery packs using current distributions

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