SOC Estimation Algorithm of Lithium-Ion Batteries Based on Particle Swarm Optimization

The accurate state of charge (SOC) estimation for electric vehicle (EV) and superconducting magnetic energy storage (SMES) is quite important. For the SOC estimation with Kalman filter method, the inaccurate battery model results in estimation error. The traditional ampere-hour integral approach causes cumulative errors. The neural network method doesn't need precise battery model, but it needs much training time and low SOC estimating accuracy. The particle swarm optimization (PSO) algorithm for SOC estimation is proposed, since it can perform the nonlinear and dynamic characteristics of lithium-ion batteries, and the voltage, resistance and temperature are adopted as input vectors and the SOC is employed as output vector. The experimental results show that the estimation method for SOC in this paper has well precision with fast convergence.