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Life prediction model for lithium-ion battery considering fast-charging protocol

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  • Zhang, Chen
  • Wang, Hongmin
  • Wu, Lifeng

Abstract

With the development of fast-charging technology, fast-charging protocols have been widely used in electric vehicles. However, charging the same type of lithium-ion batteries with different fast-charging protocols has an impact on the cycle life of the battery. Considering the impact of fast-charging protocols on battery life, this paper proposes a life prediction model for lithium-ion batteries that charge with fast-charging protocols. First, the charging-based features are extracted from charge data using dilated convolutional network. Second, the discharging-based features extracted from the discharge curve are enhanced using deep neural network. Finally, the charging-based features and the discharging-based features are merged and fed into random forest regression to predict the cycle life of lithium-ion batteries. Considering that hyperparameters of the model have great influence on the prediction performance, the Bayesian optimization algorithm is used to optimize the hyperparameters of random forest regression. Two public datasets are used for validation. The experimental results show that the proposed method is significantly superior to other existing algorithms for battery cycle life prediction. The relative importance of the charging-based features and the discharging-based features for predicting battery life is also explored.

Suggested Citation

  • Zhang, Chen & Wang, Hongmin & Wu, Lifeng, 2023. "Life prediction model for lithium-ion battery considering fast-charging protocol," Energy, Elsevier, vol. 263(PE).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pe:s0360544222029954
    DOI: 10.1016/j.energy.2022.126109
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    References listed on IDEAS

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    Cited by:

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    3. Harasis, Salman & Khan, Irfan & Massoud, Ahmed, 2024. "Enabling large-scale integration of electric bus fleets in harsh environments: Possibilities, potentials, and challenges," Energy, Elsevier, vol. 300(C).
    4. Oyewole, Isaiah & Hassanieh, Wael & Chelbi, Meriam & Chehade, Abdallah, 2025. "Uncertainty-aware deep learning with physics-informed bayesian sampling for lithium-ion battery prognostics," Applied Energy, Elsevier, vol. 402(PA).
    5. Yang, Minxing & Sun, Xiaofei & Liu, Rui & Wang, Lingzhi & Zhao, Fei & Mei, Xuesong, 2024. "Predict the lifetime of lithium-ion batteries using early cycles: A review," Applied Energy, Elsevier, vol. 376(PA).
    6. Hou, Jing & Su, Taian & Gao, Tian & Yang, Yan & Xue, Wei, 2025. "Early prediction of battery lifetime for lithium-ion batteries based on a hybrid clustered CNN model," Energy, Elsevier, vol. 319(C).
    7. Liu, Wei & Teh, Jiashen, 2025. "Remaining useful life prediction of lithium-ion batteries based on an incremental internal resistance aging model and a gated recurrent unit neural network," Energy, Elsevier, vol. 333(C).
    8. Shi, Shuoshuo & Xu, Xiaodong & Tang, Shengjin & Sun, Xiaoyan & Wang, Fengfei & Ye, Hui & Guo, Junbin & Yu, Chuanqiang & Han, Xuebing, 2025. "Life extension of lithium-ion batteries using bidirectional pulse current: Focusing on frequency," Energy, Elsevier, vol. 335(C).
    9. Lin, Mingqiang & Wu, Jian & Meng, Jinhao & Wang, Wei & Wu, Ji, 2023. "State of health estimation with attentional long short-term memory network for lithium-ion batteries," Energy, Elsevier, vol. 268(C).
    10. Bi, Jianlong & Fei, Zicheng & Wang, Jin, 2026. "Adaptive confidence-calibrated semi-supervised ensemble for early-cycle battery lifetime prediction," Applied Energy, Elsevier, vol. 409(C).

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