A State of Charge Estimation Method Based on APSO-PF for Lithium-ion Battery

Abstract

This paper proposes an improved method for estimating the state of charge (SOC) of lithium-ion battery. Firstly, a first-order resistor and capacitance (RC) model is introduced. Secondly, the SOC and open-circuit voltage (OCV) relationship is identified through the constant current charge-discharge test, and the least-squares algorithm is used to identify the model parameters. Thirdly, an improved adaptive approach is proposed to solve the problems of particle swarm optimization (PSO), and adaptive particle swarm optimized particle filtering (APSO-PF) is proposed to estimate the SOC of li-ion battery Finally, two dynamic operation conditions are given to show the efficiency of APSO-PF by comparing with the application of particle filter (PF), particle swarm optimized particle filtering (PSO-PF) and APSO-PF in SOC estimation.