An electrochemical-thermal coupling model for lithium-ion battery state-of-charge estimation with improve dual particle filter framework

Abstract

Lithium-ion battery state-of-charge (SOC) estimation plays an indispensable role in the battery management system functionalities. To achieve SOC estimation with high accuracy, a precise battery model is required. Among various battery models, electrochemical model (EM) stands out for its ability to provide insights into the electrochemical mechanisms of the battery. However, the model parameters will vary with the battery running and aging, besides, the model-based methods cannot well handle the model errors. Therefore, to ensure estimation accuracy, selecting a more insightful battery model and a more effective estimation algorithm becomes crucial. This paper proposes an electrochemical model-based SOC estimation method with online parameter adjustment and an optimized algorithm. Firstly, a single particle model (SPM) with electrolyte and thermal dynamics is introduced and simplified by an approximate solution. Secondly, the particle swarm optimization (PSO) method is used to solve the problem of the particle filter (PF) algorithm, such as particle degradation and particle diversity decrease. Finally, during SOC estimating, a double-scale dual particle filter (D-PF) is utilized to adjust parameters as battery aging and temperature changes. The battery tests are applied under different working conditions and temperatures. Experimental results show that the error of SOC estimation reduces significantly compared to the previous electrochemical model-based method, and the proposed method has a higher accuracy and effectiveness.