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Energies 2016, 9(3), 184; doi:10.3390/en9030184

Comparisons of Modeling and State of Charge Estimation for Lithium-Ion Battery Based on Fractional Order and Integral Order Methods

Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
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Academic Editor: Shengshui Zhang
Received: 7 January 2016 / Revised: 27 February 2016 / Accepted: 1 March 2016 / Published: 10 March 2016
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Abstract

In order to properly manage lithium-ion batteries of electric vehicles (EVs), it is essential to build the battery model and estimate the state of charge (SOC). In this paper, the fractional order forms of Thevenin and partnership for a new generation of vehicles (PNGV) models are built, of which the model parameters including the fractional orders and the corresponding resistance and capacitance values are simultaneously identified based on genetic algorithm (GA). The relationships between different model parameters and SOC are established and analyzed. The calculation precisions of the fractional order model (FOM) and integral order model (IOM) are validated and compared under hybrid test cycles. Finally, extended Kalman filter (EKF) is employed to estimate the SOC based on different models. The results prove that the FOMs can simulate the output voltage more accurately and the fractional order EKF (FOEKF) can estimate the SOC more precisely under dynamic conditions. View Full-Text
Keywords: fractional order model; extended Kalman filter; genetic algorithm; lithium-ion battery; parameters identification; state of charge fractional order model; extended Kalman filter; genetic algorithm; lithium-ion battery; parameters identification; state of charge
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Xiao, R.; Shen, J.; Li, X.; Yan, W.; Pan, E.; Chen, Z. Comparisons of Modeling and State of Charge Estimation for Lithium-Ion Battery Based on Fractional Order and Integral Order Methods. Energies 2016, 9, 184.

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