Model-constrained deep learning for online fault diagnosis in Li-ion batteries over stochastic conditions

Rui Cao, Zhengjie Zhang, Runwu Shi, Jiayi Lu, Yifan Zheng, Yefan Sun, Xinhua Liu and Shichun Yang ()
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Rui Cao: Beihang University
Zhengjie Zhang: Beihang University
Runwu Shi: Beihang University
Jiayi Lu: Beihang University
Yifan Zheng: Beihang University
Yefan Sun: Beihang University
Xinhua Liu: Beihang University
Shichun Yang: Beihang University

Nature Communications, 2025, vol. 16, issue 1, 1-11

Abstract: Abstract For the intricate and infrequent safety issues of batteries, online safety fault diagnosis over stochastic working conditions is indispensable. In this work, we employ deep learning methods to develop an online fault diagnosis network for lithium-ion batteries operating under unpredictable conditions. The network integrates battery model constraints and employs a framework designed to manage the evolution of stochastic systems, thereby enabling fault real-time determination. We evaluate the performance using a dataset of 18.2 million valid entries from 515 vehicles. The results demonstrate our proposed algorithm outperforms other relevant approaches, enhancing the true positive rate by over 46.5% within a false positive rate range of 0 to 0.2. Meanwhile, we identify the trigger probability for four safety fault samples, namely, electrolyte leakage, thermal runaway, internal short circuit, and excessive aging. The proposed network is adaptable to packs of varying structures, thereby reducing the cost of implementation. Our work explores the application of deep learning for real-state prediction and diagnosis of batteries, demonstrating potential improvements in battery safety and economic benefits.

Date: 2025
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DOI: 10.1038/s41467-025-56832-8

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