comparisons of modeling and state of charge estimation for lithium-ion battery based on fractional order and integral order methods

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ID: 176260
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.
Reference Key
xiao2016energiescomparisons Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Renxin Xiao;Jiangwei Shen;Xiaoyu Li;Wensheng Yan;Erdong Pan;Zheng Chen
Journal acs combinatorial science
Year 2016
DOI
10.3390/en9030184
URL
http://www.mdpi.com/1996-1073/9/3/184
Keywords
genetic algorithm extended kalman filter lithium-ion battery

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