Synchronous estimation of state of health and remaining useful lifetime for lithium-ion battery using the incremental capacity and artificial neural networks

Abstract

Abstract The state of health (SOH) and remaining useful lifetime (RUL) estimation are important parameters for battery health forecasting as they reflect the health condition of battery and provide a basis for battery replacement. This study proposes a novel on-line synthesis method based on the fusion of partial incremental capacity and artificial neural network (ANN) to estimate SOH and RUL under constant current discharge. Firstly, the advanced filter methods are applied to smooth the initial incremental capacity curves. Then the strong correlation feature values are extracted from the partial incremental curves by using correlation analysis methods. Finally, two ANN models aiming at estimating SOH and RUL are established to estimate the SOH and RUL simultaneously. The training and verification results indicate that the proposed method has highly reliability and accuracy for SOH and RUL estimation.