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Toward safe carbon–neutral transportation: Battery internal short circuit diagnosis based on cloud data for electric vehicles

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  • Qiao, Dongdong
  • Wei, Xuezhe
  • Fan, Wenjun
  • Jiang, Bo
  • Lai, Xin
  • Zheng, Yuejiu
  • Tang, Xiaolin
  • Dai, Haifeng

Abstract

To promote electrified transportation and achieve carbon neutrality, Li-ion batteries with excellent energy storage performance are widely adopted in electric vehicles (EVs). However, internal short circuit (ISC) of batteries is a serious safety hazard of EVs. ISC diagnosis using the powerful storage and computing capabilities of the cloud platform is a promising method to enhance the safety performance of battery packs in EVs, but the recording period of cloud data is larger than that of battery management systems in EVs, which brings great challenges to the existing ISC diagnosis methods. In this paper, the new mean-normalization is employed to amplify the voltage characteristics of the ISC cell in the battery pack. The adaptive Kalman filter algorithm is deployed to filter the mean-normalization values to obtain the vivid ISC features. Furthermore, the density-based spatial clustering of applications with noise algorithm is exploited to automatically detect and locate the ISC cell. Even with the 40 s sparse data in the real vehicle, the ISC cell also can be timely diagnosed within 2.51 h.

Suggested Citation

  • Qiao, Dongdong & Wei, Xuezhe & Fan, Wenjun & Jiang, Bo & Lai, Xin & Zheng, Yuejiu & Tang, Xiaolin & Dai, Haifeng, 2022. "Toward safe carbon–neutral transportation: Battery internal short circuit diagnosis based on cloud data for electric vehicles," Applied Energy, Elsevier, vol. 317(C).
    • Handle: RePEc:eee:appene:v:317:y:2022:i:c:s0306261922005402
    DOI: 10.1016/j.apenergy.2022.119168
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    2. Chenqiang Luo & Zhendong Zhang & Dongdong Qiao & Xin Lai & Yongying Li & Shunli Wang, 2022. "Life Prediction under Charging Process of Lithium-Ion Batteries Based on AutoML," Energies, MDPI, vol. 15(13), pages 1-15, June.
    3. Jiang, Bo & Zhu, Yuli & Zhu, Jiangong & Wei, Xuezhe & Dai, Haifeng, 2023. "An adaptive capacity estimation approach for lithium-ion battery using 10-min relaxation voltage within high state of charge range," Energy, Elsevier, vol. 263(PC).
    4. Li, Da & Deng, Junjun & Zhang, Zhaosheng & Liu, Peng & Wang, Zhenpo, 2023. "Multi-dimension statistical analysis and selection of safety-representing features for battery pack in real-world electric vehicles," Applied Energy, Elsevier, vol. 343(C).
    5. Siyi Tao & Bo Jiang & Xuezhe Wei & Haifeng Dai, 2023. "A Systematic and Comparative Study of Distinct Recurrent Neural Networks for Lithium-Ion Battery State-of-Charge Estimation in Electric Vehicles," Energies, MDPI, vol. 16(4), pages 1-17, February.
    6. 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).

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