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Prediction of nonlinear degradation knee-point and remaining useful life for lithium-ion batteries using relaxation voltage

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Listed:
  • Fan, Wenjun
  • Zhu, Jiangong
  • Qiao, Dongdong
  • Jiang, Bo
  • Wang, Xueyuan
  • Wei, Xuezhe
  • Dai, Haifeng

Abstract

Lithium-ion batteries behave nonlinear degradation during long-term usage. Prediction of the nonlinear degradation is of guiding significance in taking proactive measures to prolong battery life and ensure battery safety. In this study, a new nonlinear degradation knee-point prediction method is proposed utilizing relaxation voltage as the feature sequence, and it is the first attempt with the joint prediction of the knee-point and remaining useful life. A remaining useful life prediction framework integrating degradation features of the knee-point is established, which leads to stable improvements in the accuracy of remaining useful life prediction. Through transfer learning, the proposed joint prediction method is validated on different battery datasets, obtaining mean absolute errors within 26 cycles for the knee-point and remaining useful life prediction, with root mean square errors below 28 cycles. The predicted results can serve as evaluation indicators for various application scenarios, including battery design, ability evaluation, and functionality enhancement.

Suggested Citation

  • Fan, Wenjun & Zhu, Jiangong & Qiao, Dongdong & Jiang, Bo & Wang, Xueyuan & Wei, Xuezhe & Dai, Haifeng, 2024. "Prediction of nonlinear degradation knee-point and remaining useful life for lithium-ion batteries using relaxation voltage," Energy, Elsevier, vol. 294(C).
    • Handle: RePEc:eee:energy:v:294:y:2024:i:c:s0360544224006728
    DOI: 10.1016/j.energy.2024.130900
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    References listed on IDEAS

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    1. 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).
    2. Wang, Yixiu & Zhu, Jiangong & Cao, Liang & Gopaluni, Bhushan & Cao, Yankai, 2023. "Long Short-Term Memory Network with Transfer Learning for Lithium-ion Battery Capacity Fade and Cycle Life Prediction," Applied Energy, Elsevier, vol. 350(C).
    3. Dai, Haifeng & Jiang, Bo & Hu, Xiaosong & Lin, Xianke & Wei, Xuezhe & Pecht, Michael, 2021. "Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    4. Ruan, Haokai & Wei, Zhongbao & Shang, Wentao & Wang, Xuechao & He, Hongwen, 2023. "Artificial Intelligence-based health diagnostic of Lithium-ion battery leveraging transient stage of constant current and constant voltage charging," Applied Energy, Elsevier, vol. 336(C).
    5. Sohn, Suyeon & Byun, Ha-Eun & Lee, Jay H., 2022. "Two-stage deep learning for online prediction of knee-point in Li-ion battery capacity degradation," Applied Energy, Elsevier, vol. 328(C).
    6. Lai, Xin & Huang, Yunfeng & Deng, Cong & Gu, Huanghui & Han, Xuebing & Zheng, Yuejiu & Ouyang, Minggao, 2021. "Sorting, regrouping, and echelon utilization of the large-scale retired lithium batteries: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    7. 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.
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    Cited by:

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    2. Du, Jingcai & Zhang, Caiping & Li, Shuowei & Zhang, Linjing & Zhang, Weige, 2024. "Aging abnormality detection of lithium-ion batteries combining feature engineering and deep learning," Energy, Elsevier, vol. 297(C).

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