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Remaining useful life prediction of lithium-ion battery using a novel particle filter framework with grey neural network

Author

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  • Chen, Lin
  • Ding, Yunhui
  • Liu, Bohao
  • Wu, Shuxiao
  • Wang, Yaodong
  • Pan, Haihong

Abstract

Remaining Useful Life (RUL) prediction of lithium-ion batteries is critically vital to ensure the safety and reliability of EVs. Because of the complex aging mechanism, accurate prediction of RUL with traditional methods always requires a large number of data, it is hard for traditional methods to guarantee the prediction accuracy when useful data are insufficient. In this paper, a grey neural network (GNN) model fused grey model (GM) and BPNN is proposed to estimate the capacity online with the inputs of new health indicators. Additionally, the sliding-window grey model (SGM) is employed to track the degradation trend of the battery, and the trend equation is set as the state transition equation of Particle Filter algorithm (PF). Meanwhile, the estimation values by GNN model are used as observation values of the PF to construct the GNN fused sliding-window grey model based on PF framework (GNN-SGMPF) for prediction of battery RUL. Moreover, the performance of GNN-SGMPF was verified by two types of batteries under various loading profiles (NEDC/UDDS/JP1015) and temperatures (10 °C/25 °C/40 °C). The results indicate the proposed GNN algorithm can effectively estimate degradation capacity with the MAE is less than 2.2%, and the GNN-SGMPF had a remarkable ability of transfer application, practicability, and universality.

Suggested Citation

  • Chen, Lin & Ding, Yunhui & Liu, Bohao & Wu, Shuxiao & Wang, Yaodong & Pan, Haihong, 2022. "Remaining useful life prediction of lithium-ion battery using a novel particle filter framework with grey neural network," Energy, Elsevier, vol. 244(PA).
    • Handle: RePEc:eee:energy:v:244:y:2022:i:pa:s0360544221028309
    DOI: 10.1016/j.energy.2021.122581
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    References listed on IDEAS

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    1. Chen, Lin & Wang, Huimin & Liu, Bohao & Wang, Yijue & Ding, Yunhui & Pan, Haihong, 2021. "Battery state-of-health estimation based on a metabolic extreme learning machine combining degradation state model and error compensation," Energy, Elsevier, vol. 215(PA).
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    Citations

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    Cited by:

    1. Zhao, Bo & Zhang, Weige & Zhang, Yanru & Zhang, Caiping & Zhang, Chi & Zhang, Junwei, 2024. "Research on the remaining useful life prediction method for lithium-ion batteries by fusion of feature engineering and deep learning," Applied Energy, Elsevier, vol. 358(C).
    2. Zhou, Yifei & Wang, Shunli & Xie, Yanxing & Shen, Xianfeng & Fernandez, Carlos, 2023. "Remaining useful life prediction and state of health diagnosis for lithium-ion batteries based on improved grey wolf optimization algorithm-deep extreme learning machine algorithm," Energy, Elsevier, vol. 285(C).
    3. Tang, Aihua & Jiang, Yihan & Nie, Yuwei & Yu, Quanqing & Shen, Weixiang & Pecht, Michael G., 2023. "Health and lifespan prediction considering degradation patterns of lithium-ion batteries based on transferable attention neural network," Energy, Elsevier, vol. 279(C).
    4. Du, Jingcai & Zhang, Caiping & Li, Shuowei & Zhang, Linjing & Zhang, Weige, 2024. "Two-stage prediction method for capacity aging trajectories of lithium-ion batteries based on Siamese-convolutional neural network," Energy, Elsevier, vol. 295(C).
    5. Zhou, Yifei & Wang, Shunli & Xie, Yanxing & Zeng, Jiawei & Fernandez, Carlos, 2024. "Remaining useful life prediction and state of health diagnosis of lithium-ion batteries with multiscale health features based on optimized CatBoost algorithm," Energy, Elsevier, vol. 300(C).
    6. 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).
    7. Zheng, Jianfei & Ren, Jincheng & Zhang, Jianxun & Pei, Hong & Zhang, Zhengxin, 2023. "A lifetime prediction method for Lithium-ion batteries considering storage degradation of spare parts," Energy, Elsevier, vol. 282(C).
    8. Guo, Junyu & Wan, Jia-Lun & Yang, Yan & Dai, Le & Tang, Aimin & Huang, Bangkui & Zhang, Fangfang & Li, He, 2023. "A deep feature learning method for remaining useful life prediction of drilling pumps," Energy, Elsevier, vol. 282(C).
    9. Liyuan Shao & Yong Zhang & Xiujuan Zheng & Xin He & Yufeng Zheng & Zhiwei Liu, 2023. "A Review of Remaining Useful Life Prediction for Energy Storage Components Based on Stochastic Filtering Methods," Energies, MDPI, vol. 16(3), pages 1-22, February.

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