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Impedance-based forecasting of lithium-ion battery performance amid uneven usage

Author

Listed:
  • Penelope K. Jones

    (University of Cambridge
    The Alan Turing Institute)

  • Ulrich Stimming

    (Chemistry, School of Natural and Environmental Sciences, Newcastle University)

  • Alpha A. Lee

    (University of Cambridge)

Abstract

Accurate forecasting of lithium-ion battery performance is essential for easing consumer concerns about the safety and reliability of electric vehicles. Most research on battery health prognostics focuses on the research and development setting where cells are subjected to the same usage patterns. However, in practical operation, there is great variability in use across cells and cycles, thus making forecasting challenging. To address this challenge, here we propose a combination of electrochemical impedance spectroscopy measurements with probabilistic machine learning methods. Making use of a dataset of 88 commercial lithium-ion coin cells generated via multistage charging and discharging (with currents randomly changed between cycles), we show that future discharge capacities can be predicted with calibrated uncertainties, given the future cycling protocol and a single electrochemical impedance spectroscopy measurement made immediately before charging, and without any knowledge of usage history. The results are robust to cell manufacturer, the distribution of cycling protocols, and temperature. The research outcome also suggests that battery health is better quantified by a multidimensional vector rather than a scalar state of health.

Suggested Citation

  • Penelope K. Jones & Ulrich Stimming & Alpha A. Lee, 2022. "Impedance-based forecasting of lithium-ion battery performance amid uneven usage," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-32422-w
    DOI: 10.1038/s41467-022-32422-w
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    References listed on IDEAS

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    Cited by:

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    2. Wang, Huan & Li, Yan-Fu & Zhang, Ying, 2023. "Bioinspired spiking spatiotemporal attention framework for lithium-ion batteries state-of-health estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    3. Jiang, Nanhua & Zhang, Jiawei & Jiang, Weiran & Ren, Yao & Lin, Jing & Khoo, Edwin & Song, Ziyou, 2024. "Driving behavior-guided battery health monitoring for electric vehicles using extreme learning machine," Applied Energy, Elsevier, vol. 364(C).
    4. Liu, Xutao & Tao, Shengyu & Fu, Shiyi & Ma, Ruifei & Cao, Tingwei & Fan, Hongtao & Zuo, Junxiong & Zhang, Xuan & Wang, Yu & Sun, Yaojie, 2024. "Binary multi-frequency signal for accurate and rapid electrochemical impedance spectroscopy acquisition in lithium-ion batteries," Applied Energy, Elsevier, vol. 364(C).
    5. He, Rong & He, Yongling & Xie, Wenlong & Guo, Bin & Yang, Shichun, 2023. "Comparative analysis for commercial li-ion batteries degradation using the distribution of relaxation time method based on electrochemical impedance spectroscopy," Energy, Elsevier, vol. 263(PD).
    6. Huang, Yaodi & Zhang, Pengcheng & Lu, Jiahuan & Xiong, Rui & Cai, Zhongmin, 2024. "A transferable long-term lithium-ion battery aging trajectory prediction model considering internal resistance and capacity regeneration phenomenon," Applied Energy, Elsevier, vol. 360(C).
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