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Internal short circuit detection and evaluation in battery packs based on transformation matrix and an improved state-space model

Author

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  • Yang, Qifan
  • Sun, Jinlei
  • Kang, Yongzhe
  • Ma, Hongzhong
  • Duan, Dawei

Abstract

Internal short circuit (ISC) is a serious safety hazard for lithium-ion battery packs. How to comprehensively detect and evaluate ISC in battery packs remains a challenging problem. Motivated by this, this paper proposes an ISC detection method based on the transformation matrix and an ISC resistance calculation method based on an improved state-space model. Specifically, benefiting from the descriptive ability of the transformation matrix for curve changes, the shear element in the transformation matrix is used to capture the skewed and downward voltage pattern of the ISC cell in the battery pack. An online detection flow is designed based on the opposite variation relationship between the shear elements calculated from adjacent cells. Moreover, an improved state-space model is developed to directly estimate the ISC current, which ensures that the weight of the ISC current in the model is unrestricted. A dual extended Kalman filter with separated time scales is deployed to obtain accurate state estimations. Experimental results validate the effectiveness and advantages of the ISC detection method, as well as the high accuracy of the ISC resistance calculation method.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:276:y:2023:i:c:s0360544223009490
    DOI: 10.1016/j.energy.2023.127555
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    References listed on IDEAS

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    Cited by:

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    2. 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).
    3. Liu, Qiquan & Ma, Jian & Zhao, Xuan & He, Yilin & Zhang, Kai & Peng, Jun & Yuan, Xiaolei, 2025. "Battery internal short circuit detection based on curvilinear Euclidean distance assessment and adaptive clustering method analysis in multi-factor coupling scenarios," Energy, Elsevier, vol. 333(C).
    4. 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).
    5. Zhao, Shiwen & Peng, Qiao & Du, Dajun & Fei, Minrui & Peng, Chen & Li, Heng & Wu, Yue & Li, Kang & Liu, Kailong, 2026. "Enhancing safety of lithium-ion batteries in sustainable energy systems through intelligent minor short-circuits fault detection," Renewable and Sustainable Energy Reviews, Elsevier, vol. 229(C).
    6. Liu, Zhaohuan & Xu, Jun & Zhao, Zixiang & Zou, Zhongyue & Mei, Xuesong, 2025. "A novel battery active equalization-based internal short circuit fault diagnosis method for lithium-ion batteries," Energy, Elsevier, vol. 333(C).
    7. 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).
    8. 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).
    9. Liu, Kailong & Zhao, Shiwen & Zhao, Guangcai & Fang, Jingyang & Ren, Yaxing & Peng, Qiao, 2026. "Internal short-circuit diagnosis for lithium-ion batteries using autoencoder with temporal convolutional network and self-attention mechanism," Energy, Elsevier, vol. 347(C).

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