Realistic fault detection of li-ion battery via dynamical deep learning

Jingzhao Zhang, Yanan Wang, Benben Jiang, Haowei He, Shaobo Huang, Chen Wang, Yang Zhang, Xuebing Han, Dongxu Guo, Guannan He () and Minggao Ouyang ()
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Jingzhao Zhang: IIIS Tsinghua University
Yanan Wang: Tsinghua University
Benben Jiang: Tsinghua University
Haowei He: IIIS Tsinghua University
Shaobo Huang: Beijing Circue Energy Technology Co. Ltd.
Chen Wang: Beihang University
Yang Zhang: Beijing Circue Energy Technology Co. Ltd.
Xuebing Han: Tsinghua University
Dongxu Guo: Tsinghua University
Guannan He: Peking University
Minggao Ouyang: Tsinghua University

Nature Communications, 2023, vol. 14, issue 1, 1-8

Abstract: Abstract Accurate evaluation of Li-ion battery (LiB) safety conditions can reduce unexpected cell failures, facilitate battery deployment, and promote low-carbon economies. Despite the recent progress in artificial intelligence, anomaly detection methods are not customized for or validated in realistic battery settings due to the complex failure mechanisms and the lack of real-world testing frameworks with large-scale datasets. Here, we develop a realistic deep-learning framework for electric vehicle (EV) LiB anomaly detection. It features a dynamical autoencoder tailored for dynamical systems and configured by social and financial factors. We test our detection algorithm on released datasets comprising over 690,000 LiB charging snippets from 347 EVs. Our model overcomes the limitations of state-of-the-art fault detection models, including deep learning ones. Moreover, it reduces the expected direct EV battery fault and inspection costs. Our work highlights the potential of deep learning in improving LiB safety and the significance of social and financial information in designing deep learning models.

Date: 2023
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DOI: 10.1038/s41467-023-41226-5

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