IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i14p6322-d1698525.html

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

Author

Listed:
  • Heng Li

    (School of Electronic Information, Central South University, Changsha 410075, China
    These authors contributed equally to this work.)

  • Hamza Shaukat

    (School of Electronic Information, Central South University, Changsha 410075, China
    These authors contributed equally to this work.)

  • 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)

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.

Suggested Citation

  • Heng Li & Hamza Shaukat & Ren Zhu & Muaaz Bin Kaleem & Yue Wu, 2025. "Fault Detection of Li–Ion Batteries in Electric Vehicles: A Comprehensive Review," Sustainability, MDPI, vol. 17(14), pages 1-27, July.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:14:p:6322-:d:1698525
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/14/6322/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/14/6322/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhifu Wang & Wei Luo & Song Xu & Yuan Yan & Limin Huang & Jingkai Wang & Wenmei Hao & Zhongyi Yang, 2023. "Electric Vehicle Lithium-Ion Battery Fault Diagnosis Based on Multi-Method Fusion of Big Data," Sustainability, MDPI, vol. 15(2), pages 1-18, January.
    2. Hong, Ying-Yi & Pula, Rolando A., 2022. "Detection and classification of faults in photovoltaic arrays using a 3D convolutional neural network," Energy, Elsevier, vol. 246(C).
    3. Xiao Wang & Jun Xu & Yunfei Zhao, 2018. "Wavelet Based Denoising for the Estimation of the State of Charge for Lithium-Ion Batteries," Energies, MDPI, vol. 11(5), pages 1-13, May.
    4. Gharehghani, Ayat & Rabiei, Moeed & Mehranfar, Sadegh & Saeedipour, Soheil & Mahmoudzadeh Andwari, Amin & García, Antonio & Reche, Carlos Mico, 2024. "Progress in battery thermal management systems technologies for electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 202(C).
    5. Wang, Yujie & Zhang, Xingchen & Chen, Zonghai, 2022. "Low temperature preheating techniques for Lithium-ion batteries: Recent advances and future challenges," Applied Energy, Elsevier, vol. 313(C).
    6. Jafari, Sadiqa & Byun, Yung-Cheol, 2025. "AI-driven state of power prediction in battery systems: A PSO-optimized deep learning approach with XAI," Energy, Elsevier, vol. 331(C).
    7. Zhao, Jingyuan & Feng, Xuning & Wang, Junbin & Lian, Yubo & Ouyang, Minggao & Burke, Andrew F., 2023. "Battery fault diagnosis and failure prognosis for electric vehicles using spatio-temporal transformer networks," Applied Energy, Elsevier, vol. 352(C).
    8. Agbehadji, I. E. & Mabhaudhi, Tafadzwanashe & Botai, J. & Masinde, M., 2023. "A systematic review of existing early warning systems’ challenges and opportunities in cloud computing early warning systems," Papers published in Journals (Open Access), International Water Management Institute, pages 1-11(9):188.
    9. Daniels, Rojo Kurian & Kumar, Vikas & Chouhan, Satyendra Singh & Prabhakar, Aneesh, 2024. "Thermal runaway fault prediction in air-cooled lithium-ion battery modules using machine learning through temperature sensors placement optimization," Applied Energy, Elsevier, vol. 355(C).
    10. E, Jiaqiang & Xiao, Hanxu & Tian, Sicheng & Huang, Yuxin, 2024. "A comprehensive review on thermal runaway model of a lithium-ion battery: Mechanism, thermal, mechanical, propagation, gas venting and combustion," Renewable Energy, Elsevier, vol. 229(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Daniels, Rojo Kurian & Kumar, Vikas & Prabhakar, Aneesh, 2025. "A comparative study of data-driven thermal fault prediction using machine learning algorithms in air-cooled cylindrical Li-ion battery modules," Renewable and Sustainable Energy Reviews, Elsevier, vol. 207(C).
    2. Yao, Lei & Deng, Juzheng & Dai, Huilin & Dai, Fuyou & Chen, Shiwei & Xiao, Yanqiu & Cui, Guangzhen & Fei, Zhigen, 2026. "Multi-level collaborative fault diagnosis and self-healing framework for proton exchange membrane fuel cell," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PE).
    3. Shi, Haotian & Wang, Shunli & Huang, Qi & Fernandez, Carlos & Liang, Jianhong & Zhang, Mengyun & Qi, Chuangshi & Wang, Liping, 2024. "Improved electric-thermal-aging multi-physics domain coupling modeling and identification decoupling of complex kinetic processes based on timescale quantification in lithium-ion batteries," Applied Energy, Elsevier, vol. 353(PB).
    4. Zheng, Bowen & Deng, Zhichao & Luo, Zhenhao & Mao, Shuoyuan & Ouyang, Minggao & Han, Xuebing & Wang, Hewu & Li, Yalun & Sun, Yukun & Wang, Depeng & Yuan, Yuebo & He, Liangxi & Yang, Zhi & Zhu, Yanlin, 2025. "A comprehensive review of lithium-ion battery modelling research and prospects: in-depth analysis of current research and future directions," Applied Energy, Elsevier, vol. 401(PB).
    5. Anne Christine Lusk & Xin Li & Qiming Liu, 2023. "If the Government Pays for Full Home-Charger Installation, Would Affordable-Housing and Middle-Income Residents Buy Electric Vehicles?," Sustainability, MDPI, vol. 15(5), pages 1-26, March.
    6. Li, Shuowei & Zhang, Caiping & Du, Jingcai & Zhang, Linjing & Jiang, Yan, 2025. "Feature engineering-driven multi-scale voltage anomaly detection for Lithium-ion batteries in real-world electric vehicles," Applied Energy, Elsevier, vol. 377(PC).
    7. Zhao, Jingyuan & Wang, Zhenghong & Wu, Yuyan & Burke, Andrew F., 2025. "Predictive pretrained transformer (PPT) for real-time battery health diagnostics," Applied Energy, Elsevier, vol. 377(PD).
    8. Fan, Tian-E & Chen, Fan & Chen, Kang & Feng, Fei, 2025. "ResNet-MFII-PN for imbalanced small-sample lithium-ion battery fault diagnosis with a physics-based model generative real-word dataset," Energy, Elsevier, vol. 335(C).
    9. Li, Heng & Liu, Zhijun & Bin Kaleem, Muaaz & Duan, Lijun & Ruan, Siqi & Liu, Weirong, 2025. "Fault detection for lithium-ion batteries of electric vehicles with spatio-temporal autoencoder," Applied Energy, Elsevier, vol. 392(C).
    10. Bingxiang Sun & Xianjie Qi & Donglin Song & Haijun Ruan, 2023. "Review of Low-Temperature Performance, Modeling and Heating for Lithium-Ion Batteries," Energies, MDPI, vol. 16(20), pages 1-37, October.
    11. Xin, Zhicheng & Tang, Weiyu & Yao, Wen & Wu, Zan, 2025. "A review of thermal management of batteries with a focus on immersion cooling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 217(C).
    12. Chang, Zhonghao & Han, Te, 2024. "Prognostics and health management of photovoltaic systems based on deep learning: A state-of-the-art review and future perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 205(C).
    13. Zhuang, Zixian & Gu, Sijie & Luan, Weiling & Li, Jun & Chen, Haofeng, 2025. "A record fast ACP self-heating for lithium-ion batteries without capacity loss based on electrochemical-thermal coupling model," Applied Energy, Elsevier, vol. 393(C).
    14. Hong Shi & Mengmeng Cheng & Yi Feng & Chenghui Qiu & Caiyue Song & Nenglin Yuan & Chuanzhi Kang & Kaijie Yang & Jie Yuan & Yonghao Li, 2023. "Thermal Management Techniques for Lithium-Ion Batteries Based on Phase Change Materials: A Systematic Review and Prospective Recommendations," Energies, MDPI, vol. 16(2), pages 1-23, January.
    15. Li, Fang & Min, Yongjun & Zhang, Yong & Zuo, Hongfu & Bai, Fang & Zhang, Ying, 2025. "Towards general and efficient fault diagnosis: A novel framework for multi-fault cross-domain diagnosis of lithium-ion batteries in real-world scenarios," Energy, Elsevier, vol. 334(C).
    16. Jia, Zirun & Chen, Xiaohui & Zhao, Zhiwei & Sun, Zhenyu & Zhang, Lei & Zhang, Jinghan & Liu, Peng & Wang, Peng & Wang, Zhenpo, 2025. "Thermal runaway propagation path and fire risk assessment in electric vehicles based on full-vehicle experiments," Energy, Elsevier, vol. 336(C).
    17. Hou, Guolian & Zhou, Yuang & Zhang, Fan, 2025. "Data-driven modeling of ultra-supercritical unit based on improved PatchTST network with multi-task balancing," Energy, Elsevier, vol. 330(C).
    18. Seydali Ferahtia & Hegazy Rezk & Rania M. Ghoniem & Ahmed Fathy & Reem Alkanhel & Mohamed M. Ghonem, 2023. "Optimal Energy Management for Hydrogen Economy in a Hybrid Electric Vehicle," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    19. Kong, Xiangdi & Cai, Baoping & Yu, Yulong & Yang, Jun & Wang, Bo & Liu, Zijie & Shao, Xiaoyan & Yang, Chao, 2025. "Intelligent diagnosis method for early faults of electric-hydraulic control system based on residual analysis," Reliability Engineering and System Safety, Elsevier, vol. 261(C).
    20. Veerapandiyan Veerasamy & Noor Izzri Abdul Wahab & Rajeswari Ramachandran & Muhammad Mansoor & Mariammal Thirumeni & Mohammad Lutfi Othman, 2018. "High Impedance Fault Detection in Medium Voltage Distribution Network Using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 11(12), pages 1-24, November.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:14:p:6322-:d:1698525. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager The email address of this maintainer does not seem to be valid anymore. Please ask MDPI Indexing Manager to update the entry or send us the correct address (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.