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Algorithm to Determine the Knee Point on Capacity Fade Curves of Lithium-Ion Cells

Author

Listed:
  • Weiping Diao

    (Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, USA)

  • Saurabh Saxena

    (Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, USA)

  • Bongtae Han

    (Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, USA)

  • Michael Pecht

    (Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, USA)

Abstract

Lithium-ion batteries typically exhibit a transition to a more rapid capacity fade trend when subjected to extended charge–discharge cycles and storage conditions. The identification of the knee point can be valuable to identify the more severe degradation trend, and to provide guidance when scheduling battery replacements and planning secondary uses of the battery. However, a concise and repeatable determination of a knee point has not been documented. This paper provides a definition of the knee point which can be used as a degradation metric, and develops an algorithm to identify it. The algorithm is implemented on various data cases, and the results indicate that the approach provides repeatable knee point identification.

Suggested Citation

  • Weiping Diao & Saurabh Saxena & Bongtae Han & Michael Pecht, 2019. "Algorithm to Determine the Knee Point on Capacity Fade Curves of Lithium-Ion Cells," Energies, MDPI, vol. 12(15), pages 1-9, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:15:p:2910-:d:252600
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    References listed on IDEAS

    as
    1. Xuebing Han & Minggao Ouyang & Languang Lu & Jianqiu Li, 2014. "Cycle Life of Commercial Lithium-Ion Batteries with Lithium Titanium Oxide Anodes in Electric Vehicles," Energies, MDPI, vol. 7(8), pages 1-15, July.
    2. Diao, Weiping & Xue, Nan & Bhattacharjee, Vikram & Jiang, Jiuchun & Karabasoglu, Orkun & Pecht, Michael, 2018. "Active battery cell equalization based on residual available energy maximization," Applied Energy, Elsevier, vol. 210(C), pages 690-698.
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    Citations

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    Cited by:

    1. Calum Strange & Shawn Li & Richard Gilchrist & Gonçalo dos Reis, 2021. "Elbows of Internal Resistance Rise Curves in Li-Ion Cells," Energies, MDPI, vol. 14(4), pages 1-15, February.
    2. Weiping Diao & Chetan Kulkarni & Michael Pecht, 2021. "Development of an Informative Lithium-Ion Battery Datasheet," Energies, MDPI, vol. 14(17), pages 1-19, September.
    3. Wang, Cong & Chen, Yunxia, 2024. "Unsupervised dynamic prognostics for abnormal degradation of lithium-ion battery," Applied Energy, Elsevier, vol. 365(C).
    4. Lv, Haichao & Kang, Lixia & Liu, Yongzhong, 2023. "Analysis of strategies to maximize the cycle life of lithium-ion batteries based on aging trajectory prediction," Energy, Elsevier, vol. 275(C).
    5. Gomez, William & Wang, Fu-Kwun & Chou, Jia-Hong, 2024. "Li-ion battery capacity prediction using improved temporal fusion transformer model," Energy, Elsevier, vol. 296(C).
    6. Calum Strange & Rasheed Ibraheem & Gonçalo dos Reis, 2023. "Online Lifetime Prediction for Lithium-Ion Batteries with Cycle-by-Cycle Updates, Variance Reduction, and Model Ensembling," Energies, MDPI, vol. 16(7), pages 1-14, April.
    7. Zhang, Jianping & Zhang, Yinjie & Fu, Jian & Zhao, Dawen & Liu, Ping & Zhang, Zhiwei, 2024. "Capacity fading knee-point recognition method and life prediction for lithium-ion batteries using segmented capacity degradation model," Reliability Engineering and System Safety, Elsevier, vol. 251(C).

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