Energy Storage Science and Technology ›› 2023, Vol. 12 ›› Issue (7): 2220-2228.doi: 10.19799/j.cnki.2095-4239.2023.0298

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Combined GRU-MLR method for predicting the remaining useful life of lithium batteries via multiscale decomposition

Minghu WU1,2(), Chengpeng YUE2, Fan ZHANG1,2(), Junxiao LI3, Wei HUANG2, Sheng HU1,2, Jing TANG2   

  1. 1.Hubei University of Technology, Hubei Provincial Key Laboratory of Solar Energy Efficient Utilization and Energy Storage Operation Control
    2.School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, Hubei, China
    3.Zhengzhou Nissan Automobile Co. , Ltd. , Zhengzhou 450046, Henan, China
  • Received:2023-05-04 Revised:2023-05-21 Online:2023-07-05 Published:2023-07-25
  • Contact: Fan ZHANG E-mail:18239368097@163.com;15938907139@163.com

Abstract:

Accurately predicting the remaining useful life (RUL) of lithium batteries can ensure timely understanding of the internal performance degradation of the battery, reduce the risks associated with battery use, and provide a reliable theoretical basis for routine maintenance. To improve the accuracy and stability of prediction results, a lithium-battery RUL prediction model based on the combination of ensemble empirical mode decomposition (EEMD) and gated recurrent unit (GRU) with multiple linear regression (MLR) is proposed. First, the model decomposes the lithium-battery capacity data into several high-frequency and low-frequency components using the EEMD algorithm to reduce noise interference in the capacity data. Then, based on the characteristics of each component, the model builds prediction submodels based on the obtained high-frequency and low-frequency sequences using the GRU and MLR networks, respectively. Finally, the predicted values of each submodel are superimposed and fused to obtain the RUL of the battery based on the public data on lithium batteries provided by NASA and Oxford; furthermore, using different prediction starting points, the obtained results are compared with those of other single and combined models. The experimental results show that the EEMD-GRU-MLR prediction model can provide accurate RUL results, compared with LSTM, GRU, and EEMD-GRU prediction models, with the maximum mean absolute error decreased by 0.0311, 0.0234, and 0.0182, respectively, and the maximum root mean square error decreased by 0.0235, 0.0153, and 0.0098, respectively, This proves the satisfactory ability of the proposed EEMD-GRU-MLR model to predict the RUL of lithium batteries.

Key words: lithium battery, remaining useful life, ensemble empirical mode decomposition, gated recurrent unit network, multiple linear regression

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