Batteries, Vol. 11, Pages 414: Review of Fault Detection Approaches for Large-Scale Lithium-Ion Battery Systems: A Spatio-Temporal Perspective

Batteries doi: 10.3390/batteries11110414

Authors:
Qingbin Wang
Hangang Yan
Yun Yang
Xianzhong Zhao
Hui Huang
Zudi Huang
Zhuoqi Zhu
Shi Liu
Bin Yi
Gancai Huang
Jianfeng Yang

Battery fault detection is crucial for maintaining the safety and reliability of large-scale lithium-ion battery systems, especially in demanding applications like electric vehicles and energy storage power stations. However, existing research primarily addresses either temporal patterns or spatial variations in isolation. This paper presents a comprehensive review of fault detection from a spatio-temporal perspective, with a specific focus on AI-driven methods that integrate temporal dynamics with spatial sensor data. The contributions of this review include an in-depth analysis of advanced techniques such as transfer learning, foundation models, and physics-informed neural networks, emphasizing their potential for modeling complex spatio-temporal dependencies. On the engineering side, this review surveys the practical application of these methods for early fault detection and diagnostics in large-scale battery systems, supported by case studies and real-world deployment examples. The findings of this review provide a unified perspective to guide the development of robust and scalable spatio-temporal fault detection methods for EV batteries, highlighting key challenges, promising solutions, and future research directions.

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Qingbin Wang www.mdpi.com