An Empirical-Informed Model for the Early Degradation Trajectory Prediction of Lithium-ion Battery

Abstract

Early prediction of the lithium-ion (Li-ion) battery degradation trajectory is of great importance to arrange the maintenance of battery energy storage systems (BESSs). Although extensive data driven methods have achieved a super good performance in state of health (SOH) and remaining useful life (RUL) prediction, the nonlinear characteristics of the Li-ion battery degradation trajectory still prevent an accurate prediction once very limited cycling data known in advance. To solve this issue, this paper proposes an empirical-informed model for the degradation trajectory prediction with only few data from the Li-ion battery's early cycling stage, which integrates the experience based knowledge to train the data driven model. A novel experience based model is proposed to describe the battery degradation curve, which further guides the training procedure of the long-short term memory (LSTM) network. In addition, XGBoost is selected to use a perceptually important point (PIP) based feature for providing the reference capacities. In this way, the proposed method can implement an end-to-end early prediction of the battery trajectory using only partial charging voltage as the input. The performance of the proposed method is verified on three datasets.