IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v335y2025ics0360544225036989.html

Battery connection fault diagnosis method based on enhanced voltage entropy and real vehicle data

Author

Listed:
  • Xiao, Yanqiu
  • Jiao, Jianqiang
  • Ma, Liuke
  • Yao, Lei
  • Dai, Huilin
  • Cui, Guangzhen

Abstract

The reliable operation of an electric vehicle (EV) is largely dependent on the integrity of the battery pack connections, which, due to the large number of connectors involved, are prone to connectivity failures, posing a serious threat to the safety of the entire vehicle. To this end, this study proposes a battery connection fault diagnosis method based on enhanced voltage entropy and real vehicle data. Firstly, based on the connection fault mechanism, a fault feature enhancement filtering strategy is introduced to achieve double filtering of redundant signals in the voltage signal in both frequency and time domains that are not related to connection faults; secondly, the Shannon entropy is used as a high mapping characterization measure to comprehensively describe the disordered nature of voltage fluctuations caused by connection faults; Finally, a new voltage entropy peak residence algorithm is proposed to dynamically accumulate and ablate the abnormal behaviors of historical information and real-time signals to accurately capture transient fault pulses while suppressing pseudo-feature interference triggered by operating condition fluctuations, forming a chain of evidence of faults with temporal continuity, realizing the accurate detection of connection faults and the protection against misdiagnosis. It is validated by ablation experiments and on real vehicle fault data of five different fault types. The results show that the proposed method significantly improves the detection accuracy and anti-interference ability, and provides a fast detection and high-precision identification solution for the diagnosis of real battery connection faults in EV.

Suggested Citation

  • Xiao, Yanqiu & Jiao, Jianqiang & Ma, Liuke & Yao, Lei & Dai, Huilin & Cui, Guangzhen, 2025. "Battery connection fault diagnosis method based on enhanced voltage entropy and real vehicle data," Energy, Elsevier, vol. 335(C).
    • Handle: RePEc:eee:energy:v:335:y:2025:i:c:s0360544225036989
    DOI: 10.1016/j.energy.2025.138056
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225036989
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.138056?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Feng, Fei & Hu, Xiaosong & Hu, Lin & Hu, Fengling & Li, Yang & Zhang, Lei, 2019. "Propagation mechanisms and diagnosis of parameter inconsistency within Li-Ion battery packs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 102-113.
    2. Pang, Hui & Chen, Kaiqiang & Geng, Yuanfei & Wu, Longxing & Wang, Fengbin & Liu, Jiahao, 2024. "Accurate capacity and remaining useful life prediction of lithium-ion batteries based on improved particle swarm optimization and particle filter," Energy, Elsevier, vol. 293(C).
    3. Shen, Dongxu & Lyu, Chao & Yang, Dazhi & Hinds, Gareth & Wang, Lixin, 2023. "Connection fault diagnosis for lithium-ion battery packs in electric vehicles based on mechanical vibration signals and broad belief network," Energy, Elsevier, vol. 274(C).
    4. Wang, Zhenpo & Hong, Jichao & Liu, Peng & Zhang, Lei, 2017. "Voltage fault diagnosis and prognosis of battery systems based on entropy and Z-score for electric vehicles," Applied Energy, Elsevier, vol. 196(C), pages 289-302.
    5. Yi, Yahui & Xia, Chengyu & Shi, Lei & Meng, Leifeng & Chi, Qifu & Qian, Liqin & Ma, Tiancai & Chen, Siqi, 2024. "Lithium-ion battery expansion mechanism and Gaussian process regression based state of charge estimation with expansion characteristics," Energy, Elsevier, vol. 292(C).
    6. Zhang, Xiang & Liu, Peng & Lin, Ni & Zhang, Zhaosheng & Wang, Zhenpo, 2023. "A novel battery abnormality detection method using interpretable Autoencoder," Applied Energy, Elsevier, vol. 330(PB).
    7. Ma, Mina & Wang, Yu & Duan, Qiangling & Wu, Tangqin & Sun, Jinhua & Wang, Qingsong, 2018. "Fault detection of the connection of lithium-ion power batteries in series for electric vehicles based on statistical analysis," Energy, Elsevier, vol. 164(C), pages 745-756.
    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. Xiong, Rui & Sun, Wanzhou & Yu, Quanqing & Sun, Fengchun, 2020. "Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles," Applied Energy, Elsevier, vol. 279(C).
    2. Sun, Zhenyu & Han, Yang & Wang, Zhenpo & Chen, Yong & Liu, Peng & Qin, Zian & Zhang, Zhaosheng & Wu, Zhiqiang & Song, Chunbao, 2022. "Detection of voltage fault in the battery system of electric vehicles using statistical analysis," Applied Energy, Elsevier, vol. 307(C).
    3. Jeon, Jihun & Cheon, Hojin & Jung, Byungil & Kim, Hongseok, 2024. "ProADD: Proactive battery anomaly dual detection leveraging denoising convolutional autoencoder and incremental voltage analysis," Applied Energy, Elsevier, vol. 373(C).
    4. Yao, Lei & Dai, Huilin & Xiao, Yanqiu & Zhao, Changsheng & Fei, Zhigen & Cui, Guangzhen & Zhang, Longhai, 2024. "An intelligent diagnosis method for battery pack connection faults based on multiple correlation analysis and adaptive fusion decision-making," Energy, Elsevier, vol. 306(C).
    5. Yao, Lei & Fang, Zhanpeng & Xiao, Yanqiu & Hou, Junjian & Fu, Zhijun, 2021. "An Intelligent Fault Diagnosis Method for Lithium Battery Systems Based on Grid Search Support Vector Machine," Energy, Elsevier, vol. 214(C).
    6. Liu, Qiquan & Ma, Jian & Zhao, Xuan & Zhang, Kai & Meng, Dean, 2023. "Online diagnosis and prediction of power battery voltage comprehensive faults for electric vehicles based on multi-parameter characterization and improved K-means method," Energy, Elsevier, vol. 283(C).
    7. Fan Zhang & Xiao Zheng & Zixuan Xing & Minghu Wu, 2024. "Fault Diagnosis Method for Lithium-Ion Power Battery Incorporating Multidimensional Fault Features," Energies, MDPI, vol. 17(7), pages 1-21, March.
    8. Shen, Dongxu & Lyu, Chao & Yang, Dazhi & Hinds, Gareth & Wang, Lixin, 2023. "Connection fault diagnosis for lithium-ion battery packs in electric vehicles based on mechanical vibration signals and broad belief network," Energy, Elsevier, vol. 274(C).
    9. Xu, Yiming & Ge, Xiaohua & Guo, Ruohan & Shen, Weixiang, 2025. "Recent advances in model-based fault diagnosis for lithium-ion batteries: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 207(C).
    10. Shen, Dongxu & Lyu, Chao & Yang, Dazhi & Hinds, Gareth & Xu, Shaochun & Bai, Miao, 2025. "Detection of connection faults and estimation of contact resistance in lithium-ion battery packs with canonical variable analysis and local Mahalanobis distance," Energy, Elsevier, vol. 318(C).
    11. Chen, Zhen & Liu, Weijie & Zhou, Di & Xia, Tangbin & Pan, Ershun, 2025. "Inconsistency identification for Lithium-ion battery energy storage systems using deep embedded clustering," Applied Energy, Elsevier, vol. 388(C).
    12. Shen, Dongxu & Lyu, Chao & Yang, Dazhi & Hinds, Gareth & Ma, Kai & Xu, Shaochun & Bai, Miao, 2024. "Concurrent multi-fault diagnosis of lithium-ion battery packs using random convolution kernel transformation and Gaussian process classifier," Energy, Elsevier, vol. 306(C).
    13. Li, Da & Deng, Junjun & Zhang, Zhaosheng & Liu, Peng & Wang, Zhenpo, 2023. "Multi-dimension statistical analysis and selection of safety-representing features for battery pack in real-world electric vehicles," Applied Energy, Elsevier, vol. 343(C).
    14. Bosong Zou & Lisheng Zhang & Xiaoqing Xue & Rui Tan & Pengchang Jiang & Bin Ma & Zehua Song & Wei Hua, 2023. "A Review on the Fault and Defect Diagnosis of Lithium-Ion Battery for Electric Vehicles," Energies, MDPI, vol. 16(14), pages 1-19, July.
    15. Shen, Dongxu & Yang, Dazhi & Lyu, Chao & Ma, Jingyan & Hinds, Gareth & Sun, Qingmin & Du, Limei & Wang, Lixin, 2024. "Multi-sensor multi-mode fault diagnosis for lithium-ion battery packs with time series and discriminative features," Energy, Elsevier, vol. 290(C).
    16. Mohammed, Abubakar Gambo & Elfeky, Karem Elsayed & Wang, Qiuwang, 2022. "Recent advancement and enhanced battery performance using phase change materials based hybrid battery thermal management for electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    17. Jinrui Nan & Bo Deng & Wanke Cao & Jianjun Hu & Yuhua Chang & Yili Cai & Zhiwei Zhong, 2022. "Big Data-Based Early Fault Warning of Batteries Combining Short-Text Mining and Grey Correlation," Energies, MDPI, vol. 15(15), pages 1-19, July.
    18. Tian, Yong & Huang, Zhijia & Tian, Jindong & Li, Xiaoyu, 2022. "State of charge estimation of lithium-ion batteries based on cubature Kalman filters with different matrix decomposition strategies," Energy, Elsevier, vol. 238(PC).
    19. 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).
    20. Wang, Zhenpo & Zhang, Dayu & Liu, Peng & Lin, Ni & Zhang, Zhaosheng & She, Chengqi, 2024. "An online inconsistency evaluation and abnormal cell identification method for real-world electric vehicles," Energy, Elsevier, vol. 307(C).

    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:eee:energy:v:335:y:2025:i:c:s0360544225036989. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.