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Thermal runaway boundary recognition and early detection of lithium battery based on machine vision algorithm

Author

Listed:
  • Hong, Jichao
  • Zhang, Lei
  • Zhang, Chi
  • Li, Meng
  • Liang, Fengwei
  • Li, Kerui
  • Tang, Aihua
  • Chen, Yuan
  • Huang, Zhongguo
  • Ma, Fei

Abstract

To improve the safety of battery energy storage systems, it is of great significance to study the thermal runaway (TR) mechanism and early detection. In this paper, a machine vision-based TR detection algorithm is proposed by combining electrical signals and battery deformation, and the voltage, temperature and morphology changes of lithium soft-packed batteries in the TR process are experimentally investigated. In this paper, the thermal stability characteristics of lithium soft-packed batteries are studied by overcharging ternary lithium soft-packed batteries with different charging multiplicity and graded caution. In this paper, a machine vision detection algorithm based on YOLOv5 is proposed to realize the boundary recognition of thermal runaway of batteries by detecting the normal, bulging and TR states of batteries and using the battery morphology, and the detection accuracy of this method reaches more than 98 %. Finally, a new TR early warning strategy is proposed and combined with a fire-fighting robot to provide a new idea for battery storage safety in power plants and other places. This research provides a valuable guidance program for the safety of lithium battery storage in the future, especially for the safety monitoring and fire fighting in the scenario of energy storage without electrical signals.

Suggested Citation

  • Hong, Jichao & Zhang, Lei & Zhang, Chi & Li, Meng & Liang, Fengwei & Li, Kerui & Tang, Aihua & Chen, Yuan & Huang, Zhongguo & Ma, Fei, 2025. "Thermal runaway boundary recognition and early detection of lithium battery based on machine vision algorithm," Energy, Elsevier, vol. 340(C).
    • Handle: RePEc:eee:energy:v:340:y:2025:i:c:s0360544225049278
    DOI: 10.1016/j.energy.2025.139285
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    References listed on IDEAS

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    1. Yao, Yongming & Wang, Jie & Zhou, Zhicong & Li, Hang & Liu, Huiying & Li, Tianyu, 2023. "Grey Markov prediction-based hierarchical model predictive control energy management for fuel cell/battery hybrid unmanned aerial vehicles," Energy, Elsevier, vol. 262(PA).
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    3. Hong, Jichao & Zhang, Huaqin & Zhang, Xinyang & Yang, Haixu & Chen, Yingjie & Wang, Facheng & Huang, Zhongguo & Wang, Wei, 2024. "Online accurate voltage prediction with sparse data for the whole life cycle of Lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 369(C).
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