A Bayesian Mixture Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries

Abstract

Remaining useful life (RUL) is one of the essential ingredients in the battery management system. However, due to the characteristic of the dynamic and time-varying electrochemical system with nonlinear and complicated internal mechanisms, the uncertainty of RUL estimation has been expanded, and it is difficult to give an accurate time to reach the end of life. This article proposes the Bayesian mixture neural network (BMNN), a probabilistic deep learning method, to obtain more accurate RUL prediction and provide uncertainty estimation, while the quasi-Gramian angular field (Q-GAF) beneficial to identify prior distribution is utilized to transform time-series sequence into temporal images. BMNN consists of the Bayesian convolutional neural network (BCNN) extracting features in temporal images and Bayesian long short-term memory (B-LSTM) learning correlation between retention capacity and other degradation inducements. After concatenating two terms, the variational Bayesian neural network outputs the distribution of prediction results. In the experimental stage, the performance of the proposed method is validated on four different lithium-ion battery datasets and demonstrates higher stability, lower uncertainty, and more accuracy than other methods.