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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Contributors:
    Fan, Yuan (author) / Chi, Qiang (author) / Fang, Xiaohan (author) / Tian, Jiaqiang (author) / Li, Mince (author) / Liu, Xinghua (author)


    Publication date :

    2025-08-01


    Size :

    4807694 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Improved particle filter algorithm based on chaos particle swarm optimization

    Wang, Ershen / Pang, Tao / Qu, Pingping et al. | British Library Online Contents | 2016



    Particle filter for state of charge and state of health estimation for lithium–iron phosphate batteries

    Schwunk, Simon / Armbruster, Nils / Straub, Sebastian et al. | Tema Archive | 2013


    Improved multi-objective particle swarm optimization algorithm

    Baoning, L. / Weiguo, Z. / Guangwen, L. et al. | British Library Online Contents | 2013