Prognostics of lithium-ion batteries health state based on adaptive mode decomposition and long short-term memory neural network

Abstract

In the field of battery health state prognostics, the inaccurate lithium-ion battery's health status prediction is usually caused by the capacity regeneration (CR) phenomenon triggered by relaxation effects during the degradation process. To address this issue, we pay more attention to the main rapid degradation trend of battery capacity instead of many researches focusing on only CR phenomenon. This paper presents a prognostic framework called CEEMDAN-LSTM to decouple the normal capacity degradation process while eliminating local regenerative capacity, capturing degradation characteristics for battery state-of-health (SOH) prognostics. It introduces the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose the original battery capacity degradation curve into principal trend sequence and other high-frequency subsequences, and Pearson correlation coefficient (PCC) is calculated to remove irrelevant high-frequency subsequences. Predictions task is completed by bidirectional long short-term memory network (Bi-LSTM) and long short-term memory network (LSTM) groups. Experimental validations are conducted on two lithium-ion battery datasets from NASA Ames Research Center and the Advanced Life-Cycle Engineering Center of the University of Maryland. The results demonstrate that the proposed framework achieves more accurate SOH prediction than many previous mainstream methods.