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Review of Abnormality Detection and Fault Diagnosis Methods for Lithium-Ion Batteries

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Abstract

Electric vehicles are develo** 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.

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Abbreviations

BMS:

Battery management system

EIS:

Electrochemical impedance spectroscopy

EV:

Electric vehicle

ISC:

Internal short-circuit

LIB:

Lithium-ion battery

RUL:

Remaining useful life

SOC:

State of charge

SOH:

State of health

SVM:

Support vector machine

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Acknowledgements

This work is supported by National Natural Science Foundation of China (No. 52102470 and No. U1864213)

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Authors and Affiliations

  1. School of Transportation Science and Engineering, Beihang University, Bei**g, 100191, China

    **nhua Liu, Mingyue Wang, Rui Cao, Meng Lyu, Bin Guo, Lisheng Zhang, Zhengjie Zhang, **nlei Gao, Hanchao Cheng, Bin Ma & Shichun Yang

  2. Institute for Clean Growth and Future Mobility, Coventry University, Coventry, UK

    Cheng Zhang

  3. Department of Mechanical Engineering, Imperial College London, London, SW7 2AZ, UK

    Shen Li

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  1. **nhua Liu
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Liu, X., Wang, M., Cao, R. et al. Review of Abnormality Detection and Fault Diagnosis Methods for Lithium-Ion Batteries. Automot. Innov. 6, 256–267 (2023). https://doi.org/10.1007/s42154-022-00215-y

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  • DOI: https://doi.org/10.1007/s42154-022-00215-y

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