LAPSE:2018.0916
Published Article
LAPSE:2018.0916
Comparisons of Modeling and State of Charge Estimation for Lithium-Ion Battery Based on Fractional Order and Integral Order Methods
Renxin Xiao, Jiangwei Shen, Xiaoyu Li, Wensheng Yan, Erdong Pan, Zheng Chen
November 27, 2018
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.
Keywords
extended Kalman filter, fractional order model, Genetic Algorithm, lithium-ion battery, parameters identification, state of charge
Suggested Citation
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. (2018). LAPSE:2018.0916
Author Affiliations
Xiao R: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China [ORCID]
Shen J: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
Li X: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
Yan W: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
Pan E: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
Chen Z: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
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Journal Name
Energies
Volume
9
Issue
3
Article Number
E184
Year
2016
Publication Date
2016-03-10
Published Version
ISSN
1996-1073
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PII: en9030184, Publication Type: Journal Article
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LAPSE:2018.0916
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doi:10.3390/en9030184
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Nov 27, 2018
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Calvin Tsay
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