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Evaluation of battery inconsistency in electric vehicles based on fusion of image features and temporal features

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  • Li, Zuxin
  • Zhu, Yuanjie
  • Cai, Zhiduan
  • Li, Yunhan
  • Chen, Yunze
  • Zhou, Zhe

Abstract

Inconsistencies in electric vehicle monobloc batteries can lead to reduced capacity and shortened service life, making accurate evaluation of battery inconsistency crucial for driving safety. This study addresses the challenges of early and potential inconsistency detection and the limitations of single-feature analysis. A novel method combining image and temporal features is proposed to assess battery inconsistency. The Gramian Angular Field technique is used to convert temporal data into 2D images, from which inconsistency information is extracted. Temporal features are further utilized to quantify the battery’s behavioral fluctuations over time. Additionally, a cumulative inconsistency metric is introduced to capture performance degradation and potential issues by analyzing inconsistency trends through a sliding window. In particular, an inconsistency-based cell and battery pack classification method is also proposed to accurately categorize inconsistency levels. Data validation across vehicles with varying driving ranges demonstrates that the method effectively identifies and quantifies inconsistencies, offers standardized evaluations for different vehicles, and exhibits high reliability, showcasing significant potential for application.

Suggested Citation

  • Li, Zuxin & Zhu, Yuanjie & Cai, Zhiduan & Li, Yunhan & Chen, Yunze & Zhou, Zhe, 2026. "Evaluation of battery inconsistency in electric vehicles based on fusion of image features and temporal features," Applied Energy, Elsevier, vol. 402(PB).
    • Handle: RePEc:eee:appene:v:402:y:2026:i:pb:s0306261925016903
    DOI: 10.1016/j.apenergy.2025.126960
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    References listed on IDEAS

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    1. 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).
    2. Fan, Xinyuan & Qi, Hongfeng & Zhang, Weige & Zhang, Yanru, 2024. "Experiment-free physical hybrid neural network approach for battery pack inconsistency estimation," Applied Energy, Elsevier, vol. 358(C).
    3. Jingzhao Zhang & Yanan Wang & Benben Jiang & Haowei He & Shaobo Huang & Chen Wang & Yang Zhang & Xuebing Han & Dongxu Guo & Guannan He & Minggao Ouyang, 2023. "Realistic fault detection of li-ion battery via dynamical deep learning," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    4. 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).
    5. An, Fulai & Zhang, Weige & Sun, Bingxiang & Jiang, Jiuchun & Fan, Xinyuan, 2023. "A novel battery pack inconsistency model and influence degree analysis of inconsistency on output energy," Energy, Elsevier, vol. 271(C).
    6. Togun, Hussein & Basem, Ali & Abdulrazzaq, Tuqa & Biswas, Nirmalendu & Abed, Azher M. & dhabab, Jameel M. & Chattopadhyay, Anirban & Slimi, Khalifa & Paul, Dipankar & Barmavatu, Praveen & Chrouda, Ama, 2025. "Development and comparative analysis between battery electric vehicles (BEV) and fuel cell electric vehicles (FCEV)," Applied Energy, Elsevier, vol. 388(C).
    7. Peng, Simin & Chen, Shengdong & Liu, Yong & Yu, Quanqing & Kan, Jiarong & Li, Rui, 2025. "State of power prediction joint fisher optimal segmentation and PO-BP neural network for a parallel battery pack considering cell inconsistency," Applied Energy, Elsevier, vol. 381(C).
    8. Hong, Jichao & Liang, Fengwei & Chen, Yingjie & Wang, Facheng & Zhang, Xinyang & Li, Kerui & Zhang, Huaqin & Yang, Jingsong & Zhang, Chi & Yang, Haixu & Ma, Shikun & Yang, Qianqian, 2024. "A novel battery abnormality diagnosis method using multi-scale normalized coefficient of variation in real-world vehicles," Energy, Elsevier, vol. 299(C).
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