IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v316y2025ics0360544225002324.html
   My bibliography  Save this article

A deep neural network for multi-fault diagnosis of battery packs based on an incremental voltage measurement topology

Author

Listed:
  • Zhao, Hongyu
  • Zhang, Chengzhong
  • Xu, Liang
  • Liao, Chenglin
  • Wang, Liye
  • Wang, Lifang

Abstract

Fault diagnosis of batteries has been a crucial task to ensure safe operation of energy storage systems. In battery packs, a comprehensive diagnosis strategy for multi-fault is always a challenge. Especially, soft short circuit (SSC) faults with insignificant voltage drops also make the task of diagnosing battery pack faults more challenging. This paper presents a multi-fault diagnosis method for battery packs using deep neural networks and an autoencoder (AE) framework. The method employs the residual difference between the original input features and the features reconstructed by the AE as a fault indicator, determining if the residual exceeds a threshold to produce the final diagnosis. We introduce an incremental voltage measurement topology and construct a high-dimensional voltage signature matrix as the model input. Multi-fault experiments demonstrate the method's accuracy in diagnosing various faults. Comparative studies with other common methods show significant advantages in external short circuit (ESC) fault diagnosis, achieving a recall rate of 96.29 % and an F1 score of 93.74 %. The method excels particularly in the rapid and accurate diagnosis of soft short circuit (SSC) faults, with an F1 score of 99.13 %. A model ablation study confirms the superior performance of the proposed method in battery pack fault diagnosis.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:316:y:2025:i:c:s0360544225002324
    DOI: 10.1016/j.energy.2025.134590
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225002324
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.134590?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Sok, Ratnak & Jeyamoorthy, Arravind & Kusaka, Jin, 2024. "Novel virtual sensors development based on machine learning combined with convolutional neural-network image processing-translation for feedback control systems of internal combustion engines," Applied Energy, Elsevier, vol. 365(C).
    2. Xu, Jun & Wang, Haitao & Shi, Hu & Mei, Xuesong, 2020. "Multi-scale short circuit resistance estimation method for series connected battery strings," Energy, Elsevier, vol. 202(C).
    3. An, Zhoujian & Zhao, Yabing & Du, Xiaoze & Shi, Tianlu & Zhang, Dong, 2023. "Experimental research on thermal-electrical behavior and mechanism during external short circuit for LiFePO4 Li-ion battery," Applied Energy, Elsevier, vol. 332(C).
    4. Woon, Kok Sin & Phuang, Zhen Xin & Taler, Jan & Varbanov, Petar Sabev & Chong, Cheng Tung & Klemeš, Jiří Jaromír & Lee, Chew Tin, 2023. "Recent advances in urban green energy development towards carbon emissions neutrality," Energy, Elsevier, vol. 267(C).
    5. 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).
    6. 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).
    7. Murali, G. & Sravya, G.S.N. & Jaya, J. & Naga Vamsi, V., 2021. "A review on hybrid thermal management of battery packs and it's cooling performance by enhanced PCM," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    8. 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).
    9. Xiao, Hanxu & E, Jiaqiang & Tian, Sicheng & Huang, Yuxin & Song, Xinyu, 2024. "Effect of composite cooling strategy including phase change material and liquid cooling on the thermal safety performance of a lithium-ion battery pack under thermal runaway propagation," Energy, Elsevier, vol. 295(C).
    10. 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).
    11. 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).
    12. 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).
    13. Chen, Zeyu & Zhang, Bo & Xiong, Rui & Shen, Weixiang & Yu, Quanqing, 2021. "Electro-thermal coupling model of lithium-ion batteries under external short circuit," Applied Energy, Elsevier, vol. 293(C).
    14. 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).
    15. Li, Da & Zhang, Lei & Zhang, Zhaosheng & Liu, Peng & Deng, Junjun & Wang, Qiushi & Wang, Zhenpo, 2023. "Battery safety issue detection in real-world electric vehicles by integrated modeling and voltage abnormality," Energy, Elsevier, vol. 284(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. 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).
    2. 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).
    3. 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).
    4. 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).
    5. 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).
    6. Juntao Wang & Zhengye Yang & Shihao Wang & Hui Yang & Mingzhe Du & Jifeng Song, 2024. "An Early Micro Internal Short Circuit Fault Diagnosis Method Based on Accumulated Correlation Coefficient for Lithium-Ion Battery Pack," Energies, MDPI, vol. 17(23), pages 1-19, December.
    7. 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).
    8. Jin, Chengwei & Xu, Jun & Jia, Zhenyu & Xie, Yanmin & Zhang, Xianggong & Mei, Xuesong, 2024. "Expansion force signal based rapid detection of early thermal runaway for pouch batteries," Energy, Elsevier, vol. 312(C).
    9. Lin, Xiang-Wei & Li, Yu-Bai & Wu, Wei-Tao & Zhou, Zhi-Fu & Chen, Bin, 2024. "Advances on two-phase heat transfer for lithium-ion battery thermal management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    10. Zhang, Yi & Zhang, E & Guo, Zhenlin & He, Xin & He, Yaling & Li, Haomiao & Jiang, Kai & Zhou, Min, 2023. "Numerical study on thermal characteristics under external short circuit for Li||Bi liquid metal batteries," Applied Energy, Elsevier, vol. 348(C).
    11. Chang, Chun & Wang, Qiyue & Jiang, Jiuchun & Jiang, Yan & Wu, Tiezhou, 2023. "Voltage fault diagnosis of a power battery based on wavelet time-frequency diagram," Energy, Elsevier, vol. 278(PB).
    12. 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).
    13. 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).
    14. 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).
    15. 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).
    16. 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).
    17. Du, Jingcai & Zhang, Caiping & Li, Shuowei & Zhang, Linjing & Zhang, Weige, 2024. "Aging abnormality detection of lithium-ion batteries combining feature engineering and deep learning," Energy, Elsevier, vol. 297(C).
    18. 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).
    19. Daniels, Rojo Kurian & Langeh, Harsh & Kumar, Vikas & Chouhan, Satyendra Singh & Prabhakar, Aneesh, 2024. "Faulty cell prediction accuracy comparison of machine learning algorithms using temperature sensor placement optimization approach in immersion cooled Li-ion battery modules," Applied Energy, Elsevier, vol. 367(C).
    20. Liu, Qiquan & Ma, Jian & Zhao, Xuan & Zhang, Kai & Xiangli, Kang & Meng, Dean, 2024. "A novel method for fault diagnosis and type identification of cell voltage inconsistency in electric vehicles using weighted Euclidean distance evaluation and statistical analysis," Energy, Elsevier, vol. 293(C).

    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:316:y:2025:i:c:s0360544225002324. 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.