Deep-learning based spatio-temporal generative model on assessing state-of-health for Li-ion batteries with partially-cycled profiles

Abstract

Accurately estimating the state-of-health (SOH) of lithium-ion batteries is emerging as a hot topic because of the rapid increase in electric appliance usage. However, versatile applicability to various battery compositions and diverse cycling conditions, and prediction only with partial data still remain challenges. In this paper, a Deep-learning-based Graphical approach to Estimation of Lithium-ion batteries SOH (D-GELS) was developed to predict the SOH covering three cathode materials, LiFePO4, LiNiCoAlO2, and LiNiCOMnO2. D-GELS shows an accurate performance for SOH prediction, less than 0.012 of RMSE, was predicted regardless of cathode materials, and its applicability was confirmed. Furthermore, D-GELS was capable of predicting the SOH using partially-cycled data, since less than 0.046 of RMSE was observed even with 50% of the image missing. When using partially-cycled profiles, significant economic benefits can be seen in used battery management, as the number of assessed batteries increases greatly, leading to cost savings.