Residual Life Prediction of Lithium Battery Based on Small Sample Data Sets

Abstract

In the recycling process of lithium-ion battery, the poor external environment will lead to the degradation of battery performance, so it is necessary to predict the residual life of lithiumion battery. However, the aging cycle of lithium-ion battery is long, so it is difficult to obtain sufficient aging data in a short time. Aiming at the problem of low prediction accuracy of lithium battery residual life under the condition of small samples, a BP neural network modelling algorithm fused with expert knowledge is proposed. Firstly, indirect health factors were extracted from the aging data of lithium battery in NASA PCOE laboratory. Secondly, the initial weight and threshold of BP neural network were optimized by genetic algorithm, and the initial multiplier value of the augmented Lagrange function was set according to Spearman correlation coefficient. Finally, the expert knowledge is integrated into the training process of BP neural network through the augmented Lagrange multiplier method. Simulation results show that the proposed algorithm has higher prediction accuracy than the traditional BP neural network under the condition of small samples.