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Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation

Author

Listed:
  • Jiangong Zhu

    (Tongji University
    Karlsruhe Institute of Technology (KIT))

  • Yixiu Wang

    (University of British Columbia)

  • Yuan Huang

    (Tongji University
    Karlsruhe Institute of Technology (KIT))

  • R. Bhushan Gopaluni

    (University of British Columbia)

  • Yankai Cao

    (University of British Columbia)

  • Michael Heere

    (Karlsruhe Institute of Technology (KIT)
    Institute of Internal Combustion Engines)

  • Martin J. Mühlbauer

    (Karlsruhe Institute of Technology (KIT))

  • Liuda Mereacre

    (Karlsruhe Institute of Technology (KIT))

  • Haifeng Dai

    (Tongji University)

  • Xinhua Liu

    (Beihang University)

  • Anatoliy Senyshyn

    (Technische Universität München, Lichtenbergstr. 1, 85748 Garching b)

  • Xuezhe Wei

    (Tongji University)

  • Michael Knapp

    (Karlsruhe Institute of Technology (KIT))

  • Helmut Ehrenberg

    (Karlsruhe Institute of Technology (KIT))

Abstract

Accurate capacity estimation is crucial for the reliable and safe operation of lithium-ion batteries. In particular, exploiting the relaxation voltage curve features could enable battery capacity estimation without additional cycling information. Here, we report the study of three datasets comprising 130 commercial lithium-ion cells cycled under various conditions to evaluate the capacity estimation approach. One dataset is collected for model building from batteries with LiNi0.86Co0.11Al0.03O2-based positive electrodes. The other two datasets, used for validation, are obtained from batteries with LiNi0.83Co0.11Mn0.07O2-based positive electrodes and batteries with the blend of Li(NiCoMn)O2 - Li(NiCoAl)O2 positive electrodes. Base models that use machine learning methods are employed to estimate the battery capacity using features derived from the relaxation voltage profiles. The best model achieves a root-mean-square error of 1.1% for the dataset used for the model building. A transfer learning model is then developed by adding a featured linear transformation to the base model. This extended model achieves a root-mean-square error of less than 1.7% on the datasets used for the model validation, indicating the successful applicability of the capacity estimation approach utilizing cell voltage relaxation.

Suggested Citation

  • Jiangong Zhu & Yixiu Wang & Yuan Huang & R. Bhushan Gopaluni & Yankai Cao & Michael Heere & Martin J. Mühlbauer & Liuda Mereacre & Haifeng Dai & Xinhua Liu & Anatoliy Senyshyn & Xuezhe Wei & Michael K, 2022. "Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29837-w
    DOI: 10.1038/s41467-022-29837-w
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    References listed on IDEAS

    as
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