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

Author

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  • 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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    2. Lin, Yupeng & Wan, Fu & Yang, Da & Li, Shufan & Liu, Ruiqi & Yin, Wenwei & Mu, Jingyi & Chen, Weigen, 2025. "Battery degradation trajectory early prediction with degradation recognition and physics-guided under different charging strategies," Energy, Elsevier, vol. 336(C).
    3. Ni, Yulong & Li, Xiaoyu & Zhang, He & Wang, Tiansi & Song, Kai & Zhu, Chunbo & Xu, Jianing, 2025. "Online identification of knee point in conventional and accelerated aging lithium-ion batteries using linear regression and Bayesian inference methods," Applied Energy, Elsevier, vol. 388(C).
    4. Liu, Zhi-Feng & Huang, Ya-He & Zhang, Shu-Rui & Luo, Xing-Fu & Chen, Xiao-Rui & Lin, Jun-Jie & Tang, Yu & Guo, Liang & Li, Ji-Xiang, 2025. "A collaborative interaction gate-based deep learning model with optimal bandwidth adjustment strategies for lithium-ion battery capacity point-interval forecasting," Applied Energy, Elsevier, vol. 377(PD).
    5. 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).
    6. Fan, Wenjun & Wang, Xueyuan & Yuan, Yongjun & Zhou, Xiao & Jiang, Bo & Qian, Long & Wei, Xuezhe & Dai, Haifeng, 2026. "Consistency sorting of retired lithium-ion batteries: From the perspective of maximizing remaining useful discharge," Applied Energy, Elsevier, vol. 402(PB).
    7. Ni, Yulong & Song, Kai & Pei, Lei & Li, Xiaoyu & Wang, Tiansi & Zhang, He & Zhu, Chunbo & Xu, Jianing, 2025. "State-of-health estimation and knee point identification of lithium-ion battery based on data-driven and mechanism model," Applied Energy, Elsevier, vol. 385(C).
    8. Xia, Baozhou & Ye, Min & Wei, Meng & Wang, Qiao & Lian, Gaoqi & Li, Yan, 2025. "SOH estimation of lithium-ion batteries with local health indicators in multi-stage fast charging protocols," Energy, Elsevier, vol. 334(C).

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