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Early Prognostics of Lithium-Ion Battery Pack Health

Author

Listed:
  • Jiwei Wang

    (Graduate School of Science and Technology, Hirosaki University, Hirosaki 036-8560, Japan)

  • Zhongwei Deng

    (College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China)

  • Kaile Peng

    (College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China)

  • Xinchen Deng

    (College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China)

  • Lijun Xu

    (Xinjiang Coal Mine Electromechanical Engineering Technology Research Center, Xinjiang Institute of Engineering, Urumqi 830023, China)

  • Guoqing Guan

    (Graduate School of Science and Technology, Hirosaki University, Hirosaki 036-8560, Japan)

  • Abuliti Abudula

    (Graduate School of Science and Technology, Hirosaki University, Hirosaki 036-8560, Japan)

Abstract

Accurate health prognostics of lithium-ion battery packs play a crucial role in timely maintenance and avoiding potential safety accidents in energy storage. To rapidly evaluate the health of newly developed battery packs, a method for predicting the future health of the battery pack using the aging data of the battery cells for their entire lifecycles and with the early cycling data of the battery pack is proposed. Firstly, health indicators (HIs) are extracted from the experimental data, and high correlations between the extracted HIs and the capacity are verified by the Pearson correlation analysis method. To predict the future health of the battery pack based on the HIs, degradation models of HIs are constructed by using an exponential function, long short-term memory network, and their weighted fusion. The future HIs of the battery pack are predicted according to the fusion degradation model. Then, based on the Gaussian process regression algorithm and battery pack data, a data-driven model is constructed to predict the health of the battery pack. Finally, the proposed method is validated with a series-connected battery pack with fifteen 100 Ah lithium iron phosphate battery cells. The mean absolute error and root mean square error of the health prediction of the battery pack are 7.17% and 7.81%, respectively, indicating that the proposed method has satisfactory accuracy.

Suggested Citation

  • Jiwei Wang & Zhongwei Deng & Kaile Peng & Xinchen Deng & Lijun Xu & Guoqing Guan & Abuliti Abudula, 2022. "Early Prognostics of Lithium-Ion Battery Pack Health," Sustainability, MDPI, vol. 14(4), pages 1-21, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:4:p:2313-:d:752108
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    References listed on IDEAS

    as
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    2. Galeotti, Matteo & Cinà, Lucio & Giammanco, Corrado & Cordiner, Stefano & Di Carlo, Aldo, 2015. "Performance analysis and SOH (state of health) evaluation of lithium polymer batteries through electrochemical impedance spectroscopy," Energy, Elsevier, vol. 89(C), pages 678-686.
    3. Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
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