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
References: Add references at CitEc
Citations:
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261925006634
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:392:y:2025:i:c:s0306261925006634
Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic
DOI: 10.1016/j.apenergy.2025.125933
Access Statistics for this article
Applied Energy is currently edited by J. Yan
More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().