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The Remaining Useful Life Forecasting Method of Energy Storage Batteries Using Empirical Mode Decomposition to Correct the Forecasting Error of the Long Short-Term Memory Model

Author

Listed:
  • Tao Yan

    (China Electric Power Research Institute, Beijing 100192, China)

  • Jizhong Chen

    (China Electric Power Research Institute, Beijing 100192, China)

  • Dong Hui

    (China Electric Power Research Institute, Beijing 100192, China)

  • Xiangjun Li

    (China Electric Power Research Institute, Beijing 100192, China)

  • Delong Zhang

    (School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300384, China)

Abstract

Energy storage has a flexible regulatory effect, which is important for improving the consumption of new energy and sustainable development. The remaining useful life (RUL) forecasting of energy storage batteries is of significance for improving the economic benefit and safety of energy storage power stations. However, the low accuracy of the current RUL forecasting method remains a problem, especially the limited research on forecasting errors. In this paper, a method for forecasting the RUL of energy storage batteries using empirical mode decomposition (EMD) to correct long short-term memory (LSTM) forecasting errors is proposed. Firstly, the RUL forecasting model of energy storage batteries based on LSTM neural networks is constructed. The forecasting error of the LSTM model is obtained and compared with the real RUL. Secondly, the EMD method is used to decompose the forecasting error into many components. The time series of EMD components are forecasted by different LSTM models. The forecasting values of different time series are added to determine the corrected forecasting error and improve the forecasting accuracy. Finally, a simulation analysis shows that the proposed method can effectively improve the forecasting effect of the RUL of energy storage batteries.

Suggested Citation

  • Tao Yan & Jizhong Chen & Dong Hui & Xiangjun Li & Delong Zhang, 2024. "The Remaining Useful Life Forecasting Method of Energy Storage Batteries Using Empirical Mode Decomposition to Correct the Forecasting Error of the Long Short-Term Memory Model," Sustainability, MDPI, vol. 16(5), pages 1-14, February.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:5:p:1920-:d:1346415
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    References listed on IDEAS

    as
    1. Xuemei Li & Hao Chang & Ruichao Wei & Shenshi Huang & Shaozhang Chen & Zhiwei He & Dongxu Ouyang, 2023. "Online Prediction of Electric Vehicle Battery Failure Using LSTM Network," Energies, MDPI, vol. 16(12), pages 1-14, June.
    2. 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).
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