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DBSCAN-Based Thermal Runaway Diagnosis of Battery Systems for Electric Vehicles

Author

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  • Da Li

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing 100081, China
    Beijing BITNEI Corp., Ltd, Beijing 100081, China)

  • Zhaosheng Zhang

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing 100081, China
    Beijing BITNEI Corp., Ltd, Beijing 100081, China)

  • Peng Liu

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing 100081, China
    Beijing BITNEI Corp., Ltd, Beijing 100081, China)

  • Zhenpo Wang

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing 100081, China
    Beijing BITNEI Corp., Ltd, Beijing 100081, China)

Abstract

Battery system diagnosis and prognosis are essential for ensuring the safe operation of electric vehicles (EVs). This paper proposes a diagnosis method of thermal runaway for ternary lithium-ion battery systems based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering. Two-dimensional fault characteristics are first extracted according to battery voltage, and DBSCAN clustering is used to diagnose the potential thermal runaway cells (PTRC). The periodic risk assessing strategy is put forward to evaluate the fault risk of battery cells. The feasibility, reliability, stability, necessity, and robustness of the proposed algorithm are analyzed, and its effectiveness is verified based on datasets collected from real-world operating electric vehicles. The results show that the proposed method can accurately predict the locations of PTRC in the battery pack a few days before the thermal runaway occurrence.

Suggested Citation

  • Da Li & Zhaosheng Zhang & Peng Liu & Zhenpo Wang, 2019. "DBSCAN-Based Thermal Runaway Diagnosis of Battery Systems for Electric Vehicles," Energies, MDPI, vol. 12(15), pages 1-15, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:15:p:2977-:d:253995
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    References listed on IDEAS

    as
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    Cited by:

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