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Prediction of remaining useful life and state of health of lithium batteries based on time series feature and Savitzky-Golay filter combined with gated recurrent unit neural network

Author

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  • Guo, Fei
  • Wu, Xiongwei
  • Liu, Lili
  • Ye, Jilei
  • Wang, Tao
  • Fu, Lijun
  • Wu, Yuping

Abstract

Prediction of state of health (SOH) and remaining useful life (RUL) of lithium batteries (LIBs) are of great significance to the safety management of new energy systems. In this paper, time series features highly related to the RUL are mined from easily available battery parameters of voltage, current and temperature. By combining Savitzky-Golay (SG) filter with gated recurrent unit (GRU) neural networks, we developed a prediction model for the SOH and RUL of LIBs. The SG filter is used to denoise the aging features and the GRU model is used to predict RUL of LIBs with different charging strategies. Experiments and verification show that the proposed SG-GRU prediction model is an effective method for different applications, which could give out accurate prediction results under various charging strategies and different batteries with fast prediction response. The prediction model can accurately track the nonlinear degradation trend of capacity during the whole cycle life, and the root mean square error of prediction can be controlled within 1%.

Suggested Citation

  • Guo, Fei & Wu, Xiongwei & Liu, Lili & Ye, Jilei & Wang, Tao & Fu, Lijun & Wu, Yuping, 2023. "Prediction of remaining useful life and state of health of lithium batteries based on time series feature and Savitzky-Golay filter combined with gated recurrent unit neural network," Energy, Elsevier, vol. 270(C).
    • Handle: RePEc:eee:energy:v:270:y:2023:i:c:s0360544223002748
    DOI: 10.1016/j.energy.2023.126880
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    6. 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).
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