IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v318y2025ics0360544225005237.html
   My bibliography  Save this article

High-reliability multi-fault diagnosis of lithium-ion batteries based on low-redundancy cross-measurement and affine transformation

Author

Listed:
  • Yang, Qifan
  • Yu, Zhiguo
  • Liu, Yiqing
  • Kang, Yongzhe

Abstract

Electrical faults pose significant risks to the safety of battery packs. The cross-voltage measurement circuit (CVMC) offers a common solution for diagnosing multiple types of electrical faults. However, balancing diagnosis reliability with sensor redundancy in CVMC remains a challenging problem. Motivated by this, we propose a low-sensor redundancy CVMC designed for the reliable diagnosis of the common electrical faults, including the internal short circuit, connection faults, and sensor faults. Specifically, each sensor in the proposed CVMC sequentially monitors three neighboring components (two cells and one connection plate, or one cell and two connection plates), ensuring the overlapping measurements for each cell and connection plate. Moreover, affine transformation with multiple independent elements is exploited to delicately characterize faults, greatly enhancing the pointing to specific faults. By integrating both methods, fault types and locations can be accurately distinguished and determined. Experimental results show the effectiveness and reliability of the proposed multi-fault diagnosis method.

Suggested Citation

  • Yang, Qifan & Yu, Zhiguo & Liu, Yiqing & Kang, Yongzhe, 2025. "High-reliability multi-fault diagnosis of lithium-ion batteries based on low-redundancy cross-measurement and affine transformation," Energy, Elsevier, vol. 318(C).
  • Handle: RePEc:eee:energy:v:318:y:2025:i:c:s0360544225005237
    DOI: 10.1016/j.energy.2025.134881
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225005237
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2025.134881?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. Ma, Mina & Li, Xiaoyu & Gao, Wei & Sun, Jinhua & Wang, Qingsong & Mi, Chris, 2022. "Multi-fault diagnosis for series-connected lithium-ion battery pack with reconstruction-based contribution based on parallel PCA-KPCA," Applied Energy, Elsevier, vol. 324(C).
    2. Liu, Hanxiao & Li, Liwei & Duan, Bin & Kang, Yongzhe & Zhang, Chenghui, 2024. "Multi-fault detection and diagnosis method for battery packs based on statistical analysis," Energy, Elsevier, vol. 293(C).
    3. Yang, Qifan & Sun, Jinlei & Kang, Yongzhe & Ma, Hongzhong & Duan, Dawei, 2023. "Internal short circuit detection and evaluation in battery packs based on transformation matrix and an improved state-space model," Energy, Elsevier, vol. 276(C).
    4. Qiao, Dongdong & Wei, Xuezhe & Fan, Wenjun & Jiang, Bo & Lai, Xin & Zheng, Yuejiu & Tang, Xiaolin & Dai, Haifeng, 2022. "Toward safe carbon–neutral transportation: Battery internal short circuit diagnosis based on cloud data for electric vehicles," Applied Energy, Elsevier, vol. 317(C).
    5. 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).
    6. 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).
    7. Semeraro, Concetta & Caggiano, Mariateresa & Olabi, Abdul-Ghani & Dassisti, Michele, 2022. "Battery monitoring and prognostics optimization techniques: Challenges and opportunities," Energy, Elsevier, vol. 255(C).
    8. Zhang, Ce & Dong, Huijie & Xu, Yuanlu & Nick, Lucky, 2024. "Balancing Cost and Economic Efficiency: Consider the multi-purpose optimization of green energy market's function in Green energy interactive infrastructure," Energy, Elsevier, vol. 309(C).
    9. 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).
    10. Zhang, Guangxu & Wei, Xuezhe & Tang, Xuan & Zhu, Jiangong & Chen, Siqi & Dai, Haifeng, 2021. "Internal short circuit mechanisms, experimental approaches and detection methods of lithium-ion batteries for electric vehicles: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    11. Kang, Yongzhe & Duan, Bin & Zhou, Zhongkai & Shang, Yunlong & Zhang, Chenghui, 2020. "Online multi-fault detection and diagnosis for battery packs in electric vehicles," Applied Energy, Elsevier, vol. 259(C).
    12. Zhang, Shuzhi & Jiang, Shiyong & Wang, Hongxia & Zhang, Xiongwen, 2022. "A novel dual time-scale voltage sensor fault detection and isolation method for series-connected lithium-ion battery pack," Applied Energy, Elsevier, vol. 322(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. Xu, Yiming & Ge, Xiaohua & Shen, Weixiang, 2024. "Multi-objective nonlinear observer design for multi-fault detection of lithium-ion battery in electric vehicles," Applied Energy, Elsevier, vol. 362(C).
    2. Liu, Qiquan & Ma, Jian & Zhao, Xuan & Zhang, Kai & Meng, Dean & Jiao, Zhipeng, 2024. "Fault diagnosis of early internal short circuit for power battery systems based on the evolution of the cell charging voltage slope in variable voltage window," Applied Energy, Elsevier, vol. 376(PB).
    3. Zhao, Hongyu & Zhang, Chengzhong & Xu, Liang & Liao, Chenglin & Wang, Liye & Wang, Lifang, 2025. "A deep neural network for multi-fault diagnosis of battery packs based on an incremental voltage measurement topology," Energy, Elsevier, vol. 316(C).
    4. 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).
    5. Song, Youngbin & Park, Shina & Kim, Sang Woo, 2023. "Model-free quantitative diagnosis of internal short circuit for lithium-ion battery packs under diverse operating conditions," Applied Energy, Elsevier, vol. 352(C).
    6. Cai, Hongchang & Tang, Xiaopeng & Lai, Xin & Wang, Yanan & Han, Xuebing & Ouyang, Minggao & Zheng, Yuejiu, 2024. "How battery capacities are correctly estimated considering latent short-circuit faults," Applied Energy, Elsevier, vol. 375(C).
    7. 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).
    8. Yang, Qifan & Sun, Jinlei & Kang, Yongzhe & Ma, Hongzhong & Duan, Dawei, 2023. "Internal short circuit detection and evaluation in battery packs based on transformation matrix and an improved state-space model," Energy, Elsevier, vol. 276(C).
    9. Ren, Song & Sun, Jing, 2024. "Multi-fault diagnosis strategy based on a non-redundant interleaved measurement circuit and improved fuzzy entropy for the battery system," Energy, Elsevier, vol. 292(C).
    10. Zhao, Hongyu & Zhang, Chengzhong & Liao, Chenglin & Wang, Liye & Liu, Weilong & Wang, Lifang, 2025. "Data-driven strategy: A robust battery anomaly detection method for short circuit fault based on mixed features and autoencoder," Applied Energy, Elsevier, vol. 382(C).
    11. Ma, Lubin & Duan, Bin & Zhang, Chenghui & Kang, Yongzhe & Li, Changlong & Liu, Kailong, 2025. "Detection and differentiation of multiple types of minor anomalies in battery packs," Energy, Elsevier, vol. 322(C).
    12. 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).
    13. Yu, Quanqing & Dai, Lei & Xiong, Rui & Chen, Zeyu & Zhang, Xin & Shen, Weixiang, 2022. "Current sensor fault diagnosis method based on an improved equivalent circuit battery model," Applied Energy, Elsevier, vol. 310(C).
    14. Gao, Renjing & Liang, Hong & Zhang, Yunfei & Zhao, Haihe & Chen, Zeyu, 2024. "Characterization of lithium-ion batteries after suffering micro short circuit induced by mechanical stress abuse," Applied Energy, Elsevier, vol. 374(C).
    15. Wei, Meng & Balaya, Palani & Ye, Min & Song, Ziyou, 2022. "Remaining useful life prediction for 18650 sodium-ion batteries based on incremental capacity analysis," Energy, Elsevier, vol. 261(PA).
    16. Xin Liu & Haihong Huang & Wenjing Chang & Yongqi Cao & Yuhang Wang, 2024. "Enhanced Wavelet Transform Dynamic Attention Transformer Model for Recycled Lithium-Ion Battery Anomaly Detection," Energies, MDPI, vol. 17(20), pages 1-15, October.
    17. Qiao, Dongdong & Wei, Xuezhe & Fan, Wenjun & Jiang, Bo & Lai, Xin & Zheng, Yuejiu & Tang, Xiaolin & Dai, Haifeng, 2022. "Toward safe carbon–neutral transportation: Battery internal short circuit diagnosis based on cloud data for electric vehicles," Applied Energy, Elsevier, vol. 317(C).
    18. 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).
    19. Zhao, Yiwen & Deng, Junjun & Liu, Peng & Zhang, Lei & Cui, Dingsong & Wang, Qiushi & Sun, Zhenyu & Wang, Zhenpo, 2025. "Enhancing battery durable operation: Multi-fault diagnosis and safety evaluation in series-connected lithium-ion battery systems," Applied Energy, Elsevier, vol. 377(PC).
    20. Wei, Meng & Ye, Min & Zhang, Chuanwei & Li, Yan & Zhang, Jiale & Wang, Qiao, 2023. "A multi-scale learning approach for remaining useful life prediction of lithium-ion batteries based on variational mode decomposition and Monte Carlo sampling," Energy, Elsevier, vol. 283(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:318:y:2025:i:c:s0360544225005237. 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.