Fault detection for lithium-ion batteries of electric vehicles with spatio-temporal autoencoder

Heng Li, Zhijun Liu, Muaaz Bin Kaleem, Lijun Duan, Siqi Ruan and Weirong Liu

Applied Energy, 2025, vol. 392, issue C, No S0306261925006634

Abstract: Fault detection of lithium-ion battery packs is crucial for the safe operation of electric vehicles. Autoencoder, as an advanced machine learning method, has significant potential for improving anomaly detection accuracy. However, the autoencoders that just use temporal features in the reconstruction process are suffered from high false positive rate. In this paper, a spatio-temporal autoencoder is proposed to address this limitation by learning the complex time dependence of time series data while considering inconsistencies within the battery pack. Meanwhile, a multi-head attention mechanism is introduced to the autoencoder to fuse temporal and spatial information to better learn the dynamic features of the battery charging time-series. Finally, we propose a two-stage fault detection method that first detects the faulty battery pack and then locates the faulty cell based on a dynamic threshold. Experimental results demonstrate the effectiveness of the proposed spatio-temporal autoencoder. It achieves the value of 0.961 on F1-score and is able to warn of faults a month in advance, accurately locating the faulty cell.

Keywords: Spatio-temporal autoencoder; Inconsistency; Battery pack; Two-stage fault detection method (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.125933

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