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Prediction of the Remaining Useful Life of Supercapacitors at Different Temperatures Based on Improved Long Short-Term Memory

Author

Listed:
  • Ning Ma

    (School of Electrical Engineering, Weihai Innovation Research Institute, Qingdao University, Qingdao 266000, China
    College of Mechanical and Electrical Engineering, Qingdao University, Qingdao 266000, China)

  • Huaixian Yin

    (College of Mechanical and Electrical Engineering, Qingdao University, Qingdao 266000, China)

  • Kai Wang

    (School of Electrical Engineering, Weihai Innovation Research Institute, Qingdao University, Qingdao 266000, China)

Abstract

As a novel type of energy storage element, supercapacitors have been extensively used in power systems, transportation and industry due to their high power density, long cycle life and good low-temperature performance. The health status of supercapacitors is of vital importance to the safe operation of the entire energy storage system. In order to improve the prediction accuracy of the remaining useful life (RUL) of supercapacitors, this paper proposes a method based on the Harris hawks optimization (HHO) algorithm and long short-term memory (LSTM) recurrent neural networks (RNNs). The HHO algorithm has the advantages of a wide global search range and a high convergence speed. Therefore, the HHO algorithm is used to optimize the initial learning rate of LSTM RNNs and the number of hidden-layer units, so as to improve the stability and reliability of the system. The root mean square error (RMSE) between the predicted result and the observed result reduced to 0.0207, 0.026 and 0.0341. The prediction results show that the HHO-LSTM has higher accuracy and robustness than traditional LSTM and GRU (gate recurrent unit) models.

Suggested Citation

  • Ning Ma & Huaixian Yin & Kai Wang, 2023. "Prediction of the Remaining Useful Life of Supercapacitors at Different Temperatures Based on Improved Long Short-Term Memory," Energies, MDPI, vol. 16(14), pages 1-14, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5240-:d:1189495
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    References listed on IDEAS

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