State-of-charge estimation for lithium-ion battery using the Gauss-Hermite particle filter technique

Abstract

The state of charge (SOC) of lithium-ion batteries is the core parameter of the battery management system. Accurate SOC can guide the effective management of the battery system and prevent the battery overcharge and over-discharge, which can extend the battery life. This paper uses an electrochemistry battery model for the SOC estimation of lithium-ion batteries, and then, the forgetting factor least squares method is used for model identification. Then, the Gauss-Hermite particle filter (GHPF) technique is proposed to estimate the SOC. The experiment results show that the GHPF not only improves the estimation accuracy but also reduces the number of sampling particles, which reduces the complexity of the algorithm. The method shows the superiority on accuracy and real-time performance over the standard PF, EPF, and UPF methods.