Improved Particle Swarm Optimization Particle Filtering Method for Lithium-Ion Battery SOC Estimation

Lithium-ion batteries, being the main energy source for electric vehicles, need precise State of Charge (SOC) estimation to maintain system reliability. Traditional methods for SOC estimation do not meet the desired accuracy for engineering applications. This study introduces an advanced approach for SOC estimation in lithium-ion batteries by utilizing Particle Swarm Optimization (PSO) enhanced Particle Filtering. At the outset, a second-order RC equivalent circuit model is developed, and its parameters are determined. Considering significant differences in the open-circuit voltage (OCV) and SOC curves during charging and discharging, three types of OCV-SOC fitting curves are designed to define the state and observation equations. In conclusion, a particle filter-based SOC estimation model is built, and an improved PSO method is introduced to optimize state value determination. The experimental outcomes indicate that the proposed method surpasses conventional SOC estimation techniques in performance.