Online State of Health Estimation for Lithium-Ion Battery Based on Historical Charging Data Assisted Reconstruction of Partial Charging Curve

Estimating the state of health (SOH) of lithium-ion batteries (LIBs) based on data-driven methods are widely used by extracting health features (HFs) from complete charging measurements. Since the user’s charging habits are different, it is, however, difficult to obtain complete HFs under random charging conditions. This article proposes an SOH online estimation method based on random charging curves to solve this problem. First, rough localization of the random charging curve is accomplished to obtain its position relative to the full charging curve and get the data needed for the reconstruction model by backtracking the historical charging data. Second, the extreme learning machine optimized by genetic algorithm and hill climbing algorithm (GA-HC-ELM) is applied to reconstruct the partial charging curve. Then, HFs are extracted from the reconstructed curves, and Gaussian process regression (GPR) is applied for fast SOH estimation. Finally, three commonly used public datasets were selected to conduct a feasibility analysis of the proposed method. The results show that the charging curve reconstruction average root-mean-square error (RMSE) is within 1.01 mV in some typical cases, and the SOH estimation RMSE is stable within 1.22% in most cases, indicating that this method has a reliable SOH estimation effect under random charging conditions.