Journal
ENERGIES
Volume 9, Issue 3, Pages -Publisher
MDPI
DOI: 10.3390/en9030184
Keywords
genetic algorithm; state of charge; parameters identification; fractional order model; lithium-ion battery; extended Kalman filter
Categories
Funding
- National Science Foundation of China [51567012]
- key project of the education department of Yunnan province [2015Z023]
- talent training program of Yunnan province [KKSY201302084]
- innovation fund of advanced techniques for new energy vehicles of Kunming University of Science and Technology [14078368]
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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.
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