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State of health prediction of lithium-ion batteries based on bidirectional gated recurrent unit and transformer

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  • Jia, Chenyu
  • Tian, Yukai
  • Shi, Yuanhao
  • Jia, Jianfang
  • Wen, Jie
  • Zeng, Jianchao

Abstract

Lithium-ion batteries have been widely used in various aspects of our lives, playing a crucial role in numerous applications. The state of health (SOH) serves as a pivotal indicator, and accurate prediction of SOH is essential for the safe utilization, management, and maintenance of lithium-ion batteries. In order to accurately predict SOH, a hybrid prediction model by combining bidirectional gated recurrent unit (BiGRU) and Transformer with multi-head attention mechanism (AM) is proposed, which can effectively address the challenge of long time series prediction. In the proposed prediction model, the indirect health indicator (HI), which can characterize the degradation of lithium-ion batteries, is fed into the BiGRU to learn the hidden states of the input features and thus further extract time series features. On this basis, multiple attention is given to the Transformer encoder layer and the input feature vectors, which gives it a better performance in the long-term dependence of the time series. The study based on the lithium-ion battery data from NASA Prediction Center of Excellence (PCoE) shows that the proposed BiGRU-Transformer model has higher accuracy, better robustness and generalisation capability.

Suggested Citation

  • Jia, Chenyu & Tian, Yukai & Shi, Yuanhao & Jia, Jianfang & Wen, Jie & Zeng, Jianchao, 2023. "State of health prediction of lithium-ion batteries based on bidirectional gated recurrent unit and transformer," Energy, Elsevier, vol. 285(C).
    • Handle: RePEc:eee:energy:v:285:y:2023:i:c:s0360544223027950
    DOI: 10.1016/j.energy.2023.129401
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    References listed on IDEAS

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    1. Li, Xiaoyu & Yuan, Changgui & Wang, Zhenpo, 2020. "State of health estimation for Li-ion battery via partial incremental capacity analysis based on support vector regression," Energy, Elsevier, vol. 203(C).
    2. Yang, Fangfang & Li, Weihua & Li, Chuan & Miao, Qiang, 2019. "State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network," Energy, Elsevier, vol. 175(C), pages 66-75.
    3. Gu, Xinyu & See, K.W. & Li, Penghua & Shan, Kangheng & Wang, Yunpeng & Zhao, Liang & Lim, Kai Chin & Zhang, Neng, 2023. "A novel state-of-health estimation for the lithium-ion battery using a convolutional neural network and transformer model," Energy, Elsevier, vol. 262(PB).
    4. Fan, Linchuan & Chai, Yi & Chen, Xiaolong, 2022. "Trend attention fully convolutional network for remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
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    Cited by:

    1. Miao, Mengqi & Yang, Pu & Yue, Shang & Zhou, Ruixu & Yu, Jianbo, 2024. "Multi-source self-supervised domain adaptation network for VRLA battery anomaly detection of data center under non-ideal conditions," Energy, Elsevier, vol. 299(C).

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