Fault Detection of Li–Ion Batteries in Electric Vehicles: A Comprehensive Review

Heng Li, Hamza Shaukat, Ren Zhu, Muaaz Bin Kaleem () and Yue Wu ()
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Heng Li: School of Electronic Information, Central South University, Changsha 410075, China
Hamza Shaukat: School of Electronic Information, Central South University, Changsha 410075, China
Ren Zhu: School of Electronic Information, Central South University, Changsha 410075, China
Muaaz Bin Kaleem: School of Electronic Information, Central South University, Changsha 410075, China
Yue Wu: School of Electronic Information, Central South University, Changsha 410075, China

Sustainability, 2025, vol. 17, issue 14, 1-27

Abstract: Lithium–ion (Li–ion) batteries are fundamental for advancing intelligent and sustainable transportation, particularly in electric vehicles, due to their long lifespan, high energy density, and strong power efficiency. Ensuring the safety and reliability of EV batteries remains a critical challenge, as undetected faults can lead to hazardous failures or gradual performance degradation. While numerous studies have addressed battery fault detection, most existing reviews adopt isolated perspectives, often overlooking interdisciplinary and intelligent approaches. This paper presents a comprehensive review of advanced battery fault detection using modern machine learning, deep learning, and hybrid methods. It also discusses the pressing challenges in the field, including limited fault data, real-time processing constraints, model adaptability across battery types, and the need for explainable AI. Furthermore, emerging AI approaches such as transformers, graph neural networks, physics-informed models, edge computing, and large language models present new opportunities for intelligent and scalable battery fault detection. Looking ahead, these frameworks, combined with AI-driven strategies, can enhance diagnostic precision, extend battery life, and strengthen safety while enabling proactive fault prevention and building trust in EV systems.

Keywords: intelligent transportation; battery safety; electric vehicles; battery management system; lithium–ion battery; sustainable transportation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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