Research progress in fault detection of battery systems: A review

Shang, Yuzhao; Wang, Shanshuai; Tang, Nianhang; Fu, Yaping; Wang, Kai

doi:10.1016/j.est.2024.113079

Journal of Energy Storage

2024

DOI: 10.1016/j.est.2024.113079

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Research progress in fault detection of battery systems: A review

Yuzhao Shang,

Shanshuai Wang,

Nianhang Tang

et al.

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2024

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Cited by 26 publications

References 104 publications

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Early Warning of Energy Storage Battery Fault Based on Improved Autoformer and Adaptive Threshold

Cheng,

Zhang,

Liu

2024

Energy Tech

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To enhance voltage prediction accuracy in energy storage batteries and address the limitations of fixed threshold warning methods, a fault warning approach based on an improved Autoformer model and adaptive thresholds is proposed. First, a spatiotemporal filtering layer is introduced into the autocorrelation mechanism to analyze the trend features of voltage sequences across different frequency domains. Additionally, an adaptive gating residual connection is used to link the sublayer and current layer output features, which helps to improve the model's adaptive feature selection capability. This innovation enables the development of a robust voltage prediction model based on the enhanced Autoformer. Then, a similarity‐based adaptive threshold, using interval estimation, is employed to rapidly track variations in battery voltage, enabling dynamic adjustment of voltage thresholds. Finally, the proposed method is validated with real voltage data from an operational energy storage station. The experimental results shows that the proposed model has higher accuracy and robustness compared to similar methods. The adaptive threshold can reduce the false alarm rate by ≈18% and issue alarms at three sampling points ahead of the battery management system alarm, improving fault warning accuracy and illustrating that early fault warning is effectively and practically carried out using the method.

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Early Warning of Energy Storage Battery Fault Based on Improved Autoformer and Adaptive Threshold

Cheng,

Zhang,

Liu

2024

Energy Tech

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Battery health state prediction based on lightweight neural networks: A review

Zhang,

Wang,

Wang

et al. 2024

Ionics

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Novel asymmetrical oxime ligand-based coordination compounds in conducting copolymer as a useful electrodes for electrochemical supercapacitors

Yildirim Kalyon,

Basili,

Gencten

et al. 2024

Ionics

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Research progress in fault detection of battery systems: A review

Cited by 26 publications

References 104 publications

Early Warning of Energy Storage Battery Fault Based on Improved Autoformer and Adaptive Threshold

Early Warning of Energy Storage Battery Fault Based on Improved Autoformer and Adaptive Threshold

Battery health state prediction based on lightweight neural networks: A review

Novel asymmetrical oxime ligand-based coordination compounds in conducting copolymer as a useful electrodes for electrochemical supercapacitors

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