Fault detection for lithium-ion batteries of electric vehicles with spatio-temporal autoencoder
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 reconstructio...
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| Published in: | Applied energy Vol. 392; p. 125933 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
15.08.2025
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| ISSN: | 0306-2619 |
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| 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.
•A spatio-temporal autoencoder for battery fault detection is proposed.•A multi-attention mechanism is introduced to integrate spatio-temporal information.•The two-stage fault detection method can significantly reduce the false positive rate compared to traditional method. |
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| AbstractList | 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. 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. •A spatio-temporal autoencoder for battery fault detection is proposed.•A multi-attention mechanism is introduced to integrate spatio-temporal information.•The two-stage fault detection method can significantly reduce the false positive rate compared to traditional method. |
| ArticleNumber | 125933 |
| Author | Liu, Zhijun Duan, Lijun Ruan, Siqi Li, Heng Liu, Weirong Bin Kaleem, Muaaz |
| Author_xml | – sequence: 1 givenname: Heng orcidid: 0000-0001-5592-7004 surname: Li fullname: Li, Heng organization: School of Electronic Information, Central South University, Changsha 410083, China – sequence: 2 givenname: Zhijun orcidid: 0009-0002-5326-6243 surname: Liu fullname: Liu, Zhijun organization: School of Electronic Information, Central South University, Changsha 410083, China – sequence: 3 givenname: Muaaz orcidid: 0000-0002-0277-7214 surname: Bin Kaleem fullname: Bin Kaleem, Muaaz organization: School of Electronic Information, Central South University, Changsha 410083, China – sequence: 4 givenname: Lijun surname: Duan fullname: Duan, Lijun organization: School of Computer Science and Engineering, Central South University, Changsha 410083, China – sequence: 5 givenname: Siqi orcidid: 0000-0002-8543-7521 surname: Ruan fullname: Ruan, Siqi organization: School of Electronic Information, Central South University, Changsha 410083, China – sequence: 6 givenname: Weirong surname: Liu fullname: Liu, Weirong email: frat@csu.edu.cn organization: School of Computer Science and Engineering, Central South University, Changsha 410083, China |
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| Cites_doi | 10.1002/adma.202101474 10.1016/j.est.2023.110381 10.1016/j.apenergy.2022.120312 10.1016/j.rser.2021.110790 10.1016/j.apenergy.2023.121650 10.1109/TSG.2016.2621135 10.1016/j.energy.2022.126496 10.1016/j.est.2022.105909 10.1016/j.jpowsour.2023.233474 10.1109/TPEL.2022.3150026 10.1016/j.asoc.2022.109903 10.1016/j.energy.2022.126027 10.1016/j.etran.2023.100254 10.1016/j.rser.2022.112282 10.1016/j.apenergy.2022.118588 10.1002/aenm.202300368 10.1016/j.rser.2023.114224 10.1016/j.apenergy.2013.05.048 10.1016/j.ins.2022.11.139 10.1016/j.est.2022.106196 10.1109/TII.2020.3011441 10.1016/j.energy.2021.121652 10.1016/j.apenergy.2023.121949 10.1016/j.rser.2021.111240 10.1016/j.rser.2021.111903 10.1109/TPEL.2020.3008194 10.1016/j.apenergy.2023.120841 10.1016/j.pecs.2023.101142 10.1109/TMECH.2023.3234770 10.1109/TPEL.2022.3211568 |
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| Keywords | Battery pack Inconsistency Two-stage fault detection method Spatio-temporal autoencoder |
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| SubjectTerms | Battery pack energy Inconsistency lithium batteries spatial data Spatio-temporal autoencoder time series analysis Two-stage fault detection method |
| Title | Fault detection for lithium-ion batteries of electric vehicles with spatio-temporal autoencoder |
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