State of health estimation of lithium-ion batteries based on a novel indirect health indicator

Abstract

Accurately estimating the state of health (SOH) of lithium-ion batteries is necessary to ensure the battery system’s safe, stable, and efficient operation. It can be directly predicted by capacity, but the latter is difficult to measure online. Therefore, this paper first extracts new indirect health indicators from the voltage and current curves during charging and optimizes them using the Kalman filter. The Pearson correlation analysis method shows that the extracted HIs have a good correlation with the capacity. On this basis, Gaussian process regression (GPR) is modified into multi-kernel Gaussian process regression (MKGPR) by using the squared exponential covariance function and the periodic covariance function to refine the accurateness of SOH estimation. The hyper-parameters of the MKGPR model are solved employing particle swarm optimization (PSO) to reduce the errors caused by the artificial adjustment. Finally, the lithium-ion battery data set provided by NASA is used to evaluate the given method, and the experimental findings reveal that the proposed approach has high accuracy and stability.