A deep learning framework for the joint prediction of the SOH and RUL of lithium-ion batteries based on bimodal images

Abstract

Accurately predicting the state of health (SOH) and remaining useful life (RUL) is significant for battery-powered electric devices. Since the images generated from raw cyclic data of the batteries can reveal more inherent battery degradation characteristics than raw cyclic data studied in most of previous studies, the bimodal images generated by two methods, such as images of curves and Gramian angular field (GAF), are effectively integrated into a unified deep learning framework to predict the health states of lithium-ion batteries. To this end, a bimodal fusion regression network (BFRN) is elaborately designed to jointly predict SOH and RUL of the battery, in which the features of bimodal images for the battery are fully extracted, interactively fused and aggregated by specific-designed network modules. Experiments on the public MIT/Stanford dataset indicate that the proposed method can achieve accurate joint state prediction for lithium-ion batteries, with the coefficient of determination (R2) for the SOH and RUL tasks are 0.98 and 0.93, respectively, which is superior to existing deep learning methods. Experiments on another public battery dataset demonstrate that it can be practical and generalized for the batteries with other chemistries.