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- W4309770819 endingPage "126164" @default.
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- W4309770819 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." @default.
- W4309770819 created "2022-11-29" @default.
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- W4309770819 date "2023-01-01" @default.
- W4309770819 modified "2023-10-12" @default.
- W4309770819 title "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" @default.
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- W4309770819 doi "https://doi.org/10.1016/j.energy.2022.126164" @default.
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