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

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.