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Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles

Author

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  • Jichao Hong

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Beijing Co-Innovation Center for Electric Vehicles Lecturer, Beijing 100081, China)

  • Zhenpo Wang

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Beijing Co-Innovation Center for Electric Vehicles Lecturer, Beijing 100081, China)

  • Peng Liu

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Beijing Co-Innovation Center for Electric Vehicles Lecturer, Beijing 100081, China)

Abstract

A thermal runaway prognosis scheme for battery systems in electric vehicles is proposed based on the big data platform and entropy method. It realizes the diagnosis and prognosis of thermal runaway simultaneously, which is caused by the temperature fault through monitoring battery temperature during vehicular operations. A vast quantity of real-time voltage monitoring data is derived from the National Service and Management Center for Electric Vehicles (NSMC-EV) in Beijing. Furthermore, a thermal security management strategy for thermal runaway is presented under the Z-score approach. The abnormity coefficient is introduced to present real-time precautions of temperature abnormity. The results illustrated that the proposed method can accurately forecast both the time and location of the temperature fault within battery packs. The presented method is flexible in all disorder systems and possesses widespread application potential in not only electric vehicles, but also other areas with complex abnormal fluctuating environments.

Suggested Citation

  • Jichao Hong & Zhenpo Wang & Peng Liu, 2017. "Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles," Energies, MDPI, vol. 10(7), pages 1-16, July.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:919-:d:103562
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    References listed on IDEAS

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    Cited by:

    1. Hong, Jichao & Wang, Zhenpo & Chen, Wen & Yao, Yongtao, 2019. "Synchronous multi-parameter prediction of battery systems on electric vehicles using long short-term memory networks," Applied Energy, Elsevier, vol. 254(C).
    2. Ma, Zhikai & Huo, Qian & Wang, Wei & Zhang, Tao, 2023. "Voltage-temperature aware thermal runaway alarming framework for electric vehicles via deep learning with attention mechanism in time-frequency domain," Energy, Elsevier, vol. 278(C).
    3. 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).
    4. Hong, Jichao & Wang, Zhenpo & Zhang, Tiezhu & Yin, Huaixian & Zhang, Hongxin & Huo, Wei & Zhang, Yi & Li, Yuanyuan, 2019. "Research on integration simulation and balance control of a novel load isolated pure electric driving system," Energy, Elsevier, vol. 189(C).
    5. Rui Xiong & Hailong Li & Xuan Zhou, 2017. "Advanced Energy Storage Technologies and Their Applications (AESA2017)," Energies, MDPI, vol. 10(9), pages 1-3, September.
    6. Lai, Xin & Huang, Yunfeng & Deng, Cong & Gu, Huanghui & Han, Xuebing & Zheng, Yuejiu & Ouyang, Minggao, 2021. "Sorting, regrouping, and echelon utilization of the large-scale retired lithium batteries: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    7. Jian Yang & Jaewook Jung & Samira Ghorbanpour & Sekyung Han, 2022. "Data–Driven Fault Diagnosis and Cause Analysis of Battery Pack with Real Data," Energies, MDPI, vol. 15(5), pages 1-19, February.
    8. Sattarzadeh, Sara & Padisala, Shanthan K. & Shi, Ying & Mishra, Partha Pratim & Smith, Kandler & Dey, Satadru, 2023. "Feedback-based fault-tolerant and health-adaptive optimal charging of batteries," Applied Energy, Elsevier, vol. 343(C).
    9. Hyuk Jung & Bohyun Moon & Gwang Goo Lee, 2020. "Development of Experimental Apparatus for Fire Resistance Test of Rechargeable Energy Storage System in x EV," Energies, MDPI, vol. 13(2), pages 1-14, January.

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