State of health estimation of lithium-ion battery using energy accumulation-based feature extraction and improved relevance vector regression

Abstract

The state of health (SOH) of lithium-ion battery is an important component of intelligent battery management system. Precise SOH estimation provides feasible and safe guidance for the energy system driven by lithium-ion battery. A novel SOH estimation method using energy accumulation of equal discharge voltage difference (EAEDVD) features extraction and improved relevance vector regression (RVR) is developed. Firstly, the EAEDVD based health feature is extracted from the discharge process as the parameters to describe battery aging, which considers the limitation of incomplete discharge greatly hindering the possibility of extracting traditional aging feature from the whole cycle. Then, the health features with high correlation are selected via integrated correlation analysis from EAEDVD curve smoothed by filter method, which eliminate redundant information. Second, this paper constructs a RVR model to estimate SOH, aiming at the problem of RVR model parameter selection, sparrow search algorithm (SSA) is developed to optimize the model parameters for improving the performance via finding the optimal solution. Finally, the feasibility is verified based on two battery datasets from NASA and laboratory. For the four batteries in the NASA dataset, the error indicators are all within 1 %, and for the two batteries from the laboratory, the mean error indicators are 0.75 %, 1.05 % and 1.05 % respectively, which indicates that proposed method has high accuracy, strong robustness and applicability.