Automotive Innovation ›› 2023, Vol. 6 ›› Issue (2): 256-267.doi: 10.1007/s42154-022-00215-y
Review of Abnormality Detection and Fault Diagnosis Methods for Lithium-Ion Batteries
Xinhua Liu1 · Mingyue Wang1 · Rui Cao1 · Meng Lyu1 · Cheng Zhang2 · Shen Li3 · Bin Guo1 · Lisheng Zhang1 ·
Zhengjie Zhang1 · Xinlei Gao1 · Hanchao Cheng1 · Bin Ma1 · Shichun Yang1
- 1 School of Transportation Science and Engineering , Beihang University , Beijing 100191 , China
2 Institute for Clean Growth and Future Mobility , Coventry University , Coventry , UK
3 Department of Mechanical Engineering , Imperial College London, London SW7 2AZ , UK
Review of Abnormality Detection and Fault Diagnosis Methods for Lithium-Ion Batteries
Xinhua Liu1 · Mingyue Wang1 · Rui Cao1 · Meng Lyu1 · Cheng Zhang2 · Shen Li3 · Bin Guo1 · Lisheng Zhang1 ·#br# Zhengjie Zhang1 · Xinlei Gao1 · Hanchao Cheng1 · Bin Ma1 · Shichun Yang1 #br#
- 1 School of Transportation Science and Engineering , Beihang University , Beijing 100191 , China
2 Institute for Clean Growth and Future Mobility , Coventry University , Coventry , UK
3 Department of Mechanical Engineering , Imperial College London, London SW7 2AZ , UK
摘要： Electric vehicles are developing prosperously in recent years. Lithium-ion batteries have become the dominant energy storage device in electric vehicle application because of its advantages such as high power density and long cycle life. To ensure safe and efficient battery operations and to enable timely battery system maintenance, accurate and reliable detection and diagnosis of battery faults are necessitated. In this paper, the state-of-the-art battery fault diagnosis methods are comprehensively reviewed. First, the degradation and fault mechanisms are analyzed and common abnormal behaviors are summarized. Then, the fault diagnosis methods are categorized into the statistical analysis-, model-, signal processing-, and data-driven methods. Their distinctive characteristics and applications are summarized and compared. Finally, the challenges facing the existing fault diagnosis methods are discussed and the future research directions are pointed out.