Remaining useful life prediction of lithium-ion batteries based on Monte Carlo Dropout and gated recurrent unit

Lithium-ion batteries have been widely used for energy storage systems and vehicle industries, highly accurate remaining useful life (RUL) prediction of lithium-ion batteries is one of the key technologies on prognostics and health management. However, the uncertainty quantification and reliability of RUL prediction is ignored. To describe the uncertainty of the RUL prediction and avoid the over-fitting phenomenon, a model combining Monte Carlo Dropout (MC_dropout) and gated recurrent unit (GRU) is proposed. Firstly, the indirect health indicator is extracted and gray relation analysis (GRA) is used to analyze the relation with capacity Then, dropout method is combined with GRU model to avoid the phenomenon of gradient disappearance and over-fitting. The probability distribution of the prediction results and 95% confidence interval is obtained through the MC_dropout method, which established the uncertainty of the prediction model. Finally, propagation neural network based on particle swarm optimization (BPNN-PSO), the least-squares support vector machine (LS-SVM) is compared with the proposed method. The results show that the proposed model not only has superiority point prediction of RUL compared with other methods, but also can describe the uncertainty of RUL prediction. (C) 2021 Chang'an University. Published by Elsevier Ltd.

Automated summary

This paper proposes a model combining MC_dropout and GRU to describe the uncertainty of RUL prediction for lithium-ion batteries, and avoids over-fitting phenomenon. The results show that the proposed model has superiority in RUL point prediction and can describe the uncertainty of the prediction.