State-of-Charge Estimation of Lithium-Ion Batteries Using an Adaptive Particle Filter Based on an Improved Particle Swarm Optimization Algorithm

The state of charge (SOC) of lithium-ion batteries is a crucial parameter in battery management systems (BMSs). The particle swarm optimization (PSO) algorithm boasts advantages such as fast iteration speed and low computational complexity. However, a notable drawback of PSO is its tendency toward premature convergence. Therefore, this article presents a novel improved PSO. First, Lévy flight is used to generate random particles and exhibit wandering characteristics, thus effectively enhancing population diversity and avoiding the trapping of PSO in local optima. Second, the integration of dual-chaos theory with the golden sine algorithm (Golden-SA) optimizes the search performance of the particle swarm, with separate reconstructions of the fitness function and optimizations of the velocity update. Third, the bias-correction exponentially weighted moving average (BEWMA) method is further introduced to reduce the impact of noise and errors. It assigns reasonable weights to observation data at different time instances, enabling effective monitoring and propagation of varying errors. Ultimately, when data is used at $0~^{\circ }$ C, the root mean square error (RMSE) is 0.6844% and 0.4385%, respectively. The experimental results provide compelling evidence that they meet the operational requirements of BMSs.