A novel intelligent weight decreasing firefly-particle filtering method for accurate state-of-charge estimation of lithium-ion batteries

Accurate state-of-charge estimation plays an extremely crucial role in battery management systems. To realize the real-time and precise state-of-charge estimation, an intelligent weight decreasing firefly-particle filtering algorithm is proposed. In this research, the second-order RC equivalent circuit model is established, and the parameters are identified online, and state-of-charge particles simulate the attraction behavior of fireflies in nature and approach the global optimal value to complete the particle optimization process. The linear weight decreasing strategy is introduced to avoid the algorithm falling into local optimization. The data of different complex conditions are used to verify the feasibility of the proposed algorithm; the results show that the root-mean-square error of intelligent weight decreasing firefly-particle filtering method when the initial SOC value is set to 1 under Hybrid Pulse Power Characterization and Beijing Bus Dynamic Stress Test condition can be controlled within 0.60% and 1.12%, respectively, which verifies that the proposed algorithm has high accuracy in state-of-charge estimation of lithium-ion batteries. The algorithm proposed in this article provides a theoretical basis for real-time state monitoring and security of battery management systems.