State-of-charge of lithium-ion battery based on equivalent circuit model – Relevance vector machine fusion model considering varying ambient temperatures

Abstract

Ambient temperature significantly impacts battery characteristics and state-of-charge (SOC) accuracy, so it is crucial to estimate SOC under various temperatures. Most circuit models of lithium-ion batteries have poor temperature adaptability, especially at low temperatures. To solve this challenge, this paper proposes a fusion model of the Equivalent Circuit Model-Relevance Vector Machine to enhance SOC estimation under various temperatures. First, a second-order equivalent circuit model based on the forgetting factor recursive least squares is built to obtain the SOC value. Second, build a data-driven battery model based on the relevance vector machine (RVM) algorithm with a moving window method, and the SOC value is obtained based on the RVM model and the unscented Kalman filter algorithm. The weights of the models are merged using the Bayesian principle. The experiments verify that the fusion model accurately estimates SOC for different types of batteries under variable ambient temperature environments and exhibits strong robustness.