Model-based State-of-charge Estimation Approach of the Lithium-ion Battery Using an Improved Adaptive Particle Filter

Accurate state of charge (SoC) estimation is of great significance for a lithium-ion battery. This paper presents an adaptive particle filter (APF)-based SoC estimation algorithm for lithium-ion batteries in electric vehicles. Firstly, the lithium-ion battery is modeled using the resistance-capacitance network based one-state hysteresis equivalent circuit model and its parameters are determined by the particle swarm optimization method. Then, an improved adaptive particle filter has been proposed and applied to the battery SoC estimation. Finally, the two typical lithium-ion battery, LiFePO4 and NMC lithium-ion, have been used to verify the proposed SoC estimator.