Lithium-ion battery modeling and parameter identification based on fractional theory

Minghui Hu, Yunxiao Li, Shuxian Li, Chunyun Fu, Datong Qin and Zonghua Li

Energy, 2018, vol. 165, issue PB, 153-163

Abstract: To effectively use and manage lithium-ion batteries and accurately estimate battery states such as state of charge and state of health, battery models with good robustness, accuracy and low-complexity need to be established. So the models can be embedded in microprocessors and provide accurate results in real-time. Firstly, this paper analyzes the electrochemical impedance spectrogram of lithium-ion battery, and adopts impedance elements with fractional order characteristics such as constant phase element and Warburg element to improve the second-order RC integer equivalent circuit model based on the fractional calculus theory. Secondly, a fractional-order equivalent circuit model of lithium-ion battery is established, which can accurately describe the electrochemical processes such as charge transfer reaction, double-layer effect, mass transfer and diffusion of lithium-ion battery. Thirdly, based on the mixed-swarm-based cooperative particle swarm optimization, parameter identification of the fractional-order equivalent circuit model is conducted using the federal city driving schedule experimental data in the time domain. The simulation results show that the model has higher accuracy and better robustness against different driving conditions, different SOC ranges and different temperatures than the second-order RC equivalent circuit model. The SOC estimation accuracy based on the fractional-order equivalent circuit model of lithium-ion battery is validated.

Keywords: Lithium-ion battery; Fractional-order model; Parameter identification; Particle swarm algorithm; Electric vehicle (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (14)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:165:y:2018:i:pb:p:153-163

DOI: 10.1016/j.energy.2018.09.101

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