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Remaining useful life prediction of lithium-ion batteries using a hybrid model

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  • Yao, Fang
  • He, Wenxuan
  • Wu, Youxi
  • Ding, Fei
  • Meng, Defang

Abstract

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is critical to the stable operation and timely maintenance of a battery system. However, the capacity of an operating battery is difficult to measure, and some prediction models cannot provide an uncertainty expression. To tackle this issue, this paper proposes a hybrid prediction model PSO-ELM-RVM, which integrates particle swarm optimization (PSO), an extreme learning machine (ELM), and relevance vector machine (RVM). Firstly, an indirect health indicator during the constant current charge process is extracted and preprocessed. Secondly, the relationship between the health indicator and capacity is established by RVM, and the health indicator prediction model is constructed based on ELM. PSO is used to optimize the parameters of both the RVM and ELM models. Finally, the health indicator prediction results are added in the RVM model to obtain the predicted capacity with a confidence interval. Compared with the battery failure threshold, the prediction results of RUL can be obtained. The experimental results validate that the proposed model can effectively predict the RUL of lithium-ion batteries.

Suggested Citation

  • Yao, Fang & He, Wenxuan & Wu, Youxi & Ding, Fei & Meng, Defang, 2022. "Remaining useful life prediction of lithium-ion batteries using a hybrid model," Energy, Elsevier, vol. 248(C).
    • Handle: RePEc:eee:energy:v:248:y:2022:i:c:s0360544222005254
    DOI: 10.1016/j.energy.2022.123622
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Wenyu Qu & Guici Chen & Tingting Zhang, 2022. "An Adaptive Noise Reduction Approach for Remaining Useful Life Prediction of Lithium-Ion Batteries," Energies, MDPI, vol. 15(19), pages 1-18, October.
    2. Wei, Meng & Balaya, Palani & Ye, Min & Song, Ziyou, 2022. "Remaining useful life prediction for 18650 sodium-ion batteries based on incremental capacity analysis," Energy, Elsevier, vol. 261(PA).
    3. Xuliang Tang & Heng Wan & Weiwen Wang & Mengxu Gu & Linfeng Wang & Linfeng Gan, 2023. "Lithium-Ion Battery Remaining Useful Life Prediction Based on Hybrid Model," Sustainability, MDPI, vol. 15(7), pages 1-18, April.
    4. Chunling Wu & Juncheng Fu & Xinrong Huang & Xianfeng Xu & Jinhao Meng, 2023. "Lithium-Ion Battery Health State Prediction Based on VMD and DBO-SVR," Energies, MDPI, vol. 16(10), pages 1-16, May.
    5. Jia, Zhuangzhuang & Huang, Zonghou & Zhai, Hongju & Qin, Pen & Zhang, Yue & Li, Yawen & Wang, Qingsong, 2022. "Experimental investigation on thermal runaway propagation of 18,650 lithium-ion battery modules with two cathode materials at low pressure," Energy, Elsevier, vol. 251(C).
    6. Yongsheng Shi & Tailin Li & Leicheng Wang & Hongzhou Lu & Yujun Hu & Beichen He & Xinran Zhai, 2023. "A Method for Predicting the Life of Lithium-Ion Batteries Based on Successive Variational Mode Decomposition and Optimized Long Short-Term Memory," Energies, MDPI, vol. 16(16), pages 1-16, August.
    7. Mingsan Ouyang & Peicheng Shen, 2022. "Prediction of Remaining Useful Life of Lithium Batteries Based on WOA-VMD and LSTM," Energies, MDPI, vol. 15(23), pages 1-20, November.

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