A data-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles

Rui Xiong, Fengchun Sun, Xianzhi Gong and Chenchen Gao

Applied Energy, 2014, vol. 113, issue C, 1433 pages

Abstract: An accurate State of Charge (SoC) estimation method is one of the most significant and difficult techniques to promote the commercialization of electric vehicles. The paper attempts to make three contributions. (1) Through the recursive least square algorithm based identification method, the parameter of the lumped parameter battery model can be updated at each sampling interval with the real-time measurement of battery current and voltage, which is called the data-driven method. Note that the battery model has been improved with a simple electrochemical equation for describing the open circuit voltage against different aging levels and SoC. (2) Through the real-time updating technique of model parameter, a data-driven based adaptive SoC estimator is established with an adaptive extended Kalman filter. It has the potential to overcome the estimation error against battery degradation and varied operating environments. (3) The approach has been verified by different loading profiles of various health states of Lithium-ion polymer battery (LiPB) cells. The results indicate that the maximum estimation errors of voltage and SoC are less than 1% and 1.5% respectively.

Keywords: Electric vehicles; Lithium-ion polymer battery; Data-driven; Recursive least square; Adaptive extended Kalman filter; State of charge (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (67)

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DOI: 10.1016/j.apenergy.2013.09.006

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