A two-stage integrated method for early prediction of remaining useful life of lithium-ion batteries?

This article puts forward a two-stage integrated method to predict the remaining useful life (RUL) of lithium-ion batteries (LIBs). At the first stage, a convolutional neural network (CNN) is employed to preliminarily estimate the cycle life of each testing LIB, where the network structure of the CNN is carefully designed to extract the discharge capacity features. By analyzing the cycle lives, an LIB which has the most similar degradation mode to each testing LIB is chosen from the training dataset. The capacities of the selected LIB are identified based on a double exponential model (DEM). At the second stage, the identified DEM is utilized as the initial mean function of the Gaussian process regression (GPR) algorithm. The GPR algorithm is then applied to early RUL prediction of each testing LIB in a personalized manner. To verify the efficacy of the proposed method, four LIBs with long-term cycle lives are selected as the testing dataset. Experimental results show the superior performance of the proposed method over the standard CNN-based RUL prediction method and the standard GPR-based RUL prediction method.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This article proposes a two-stage integrated method for predicting the remaining useful life (RUL) of lithium-ion batteries. In the first stage, a convolutional neural network (CNN) is used to estimate the cycle life of each battery, and a similar degradation mode is chosen for capacity identification. In the second stage, a personalized prediction is made using the identified parameters. Experimental results demonstrate the superiority of this method over standard CNN-based and GPR-based prediction methods.