A novel fractional order model based state-of-charge estimation method for lithium-ion battery

Hao Mu, Rui Xiong, Hongfei Zheng, Yuhua Chang and Zeyu Chen

Applied Energy, 2017, vol. 207, issue C, 384-393

Abstract: Accurate state of charge estimation of lithium-ion battery is directly related to the safe operation of electric vehicles and also an indispensable function of the battery management system. Four aspects of efforts are made to improve the estimation accuracy. First, for overcoming the drawbacks of equivalent circuit model and electrochemical model, the fractional order impedance model is built via electrochemical impedance spectroscopy data and the fractional element is used to describe the polarization effect in a simple and meaningful way. Second, the discrete state-space equations of the impedance model are inferred by Grünwald-Letnikov definition and parameters of the model including the order of the fractional element are identified together by genetic algorithm (GA) and the experiment data of the dynamic driving cycles. Third, the fractional order unscented Kalman filter technique is presented and the ‘short memory’ technique is employed to improve the computation efficiency of fractional operator. Lastly, experimental validation is implemented to verify the effectiveness of the proposed approach and results show that the SoC estimation accuracy can be improved by the proposed model and estimation method. The estimation error can be controlled within the range of 3%.

Keywords: Lithium-ion battery; Electrochemical impedance spectroscopy; Battery model; State of charge; Fractional order unscented Kalman filter (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations: View citations in EconPapers (22)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261917308668
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:207:y:2017:i:c:p:384-393

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2017.07.003

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().