A chaotic firefly - Particle filtering method of dynamic migration modeling for the state-of-charge and state-of-health co-estimation of a lithium-ion battery performance. (15th January 2023) - Qiao, Jialu; Wang, Shunli; Yu, Chunmei; Yang, Xiao; Fernandez, Carlos
Abstract: In this research, a novel dynamic migration model is proposed, which can better describe the dynamic characteristics of the lithium-ion batteries under different aging states by adjusting the battery parameters in real-time. A novel chaotic firefly - particle filtering method is proposed, which realizes particle optimization by simulating the behavior of fireflies in nature attracting each other through light, and finds a new optimal solution by chaotic mapping a group of particles to different solution space, to realize high-precision state-of-charge and state-of-health co-estimation. Compared with the traditional particle filtering algorithm, the state-of-charge and state-of-health estimation accuracy of the proposed algorithm under the Hybrid Pulse Power Characterization condition is improved by 1.48% and 0.38% respectively, and that under the Beijing bus dynamic stress test condition is improved by 0.67% and 0.63% respectively. The proposed novel battery model and algorithm are of great significance in improving the condition monitoring quality of the battery management system. Highlights: Dynamic migration model can accurately describe battery aging characteristics. Intelligent firefly algorithm can help particle filter to achieve particle optimization. Chaotic mapping enables the algorithm to find the global optimal solution. Battery parameters obtained under high health state can be used for the state estimation of batteries under severe aging.