Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles

Fengchun Sun, Xiaosong Hu, Yuan Zou and Siguang Li

Energy, 2011, vol. 36, issue 5, 3531-3540

Abstract: An accurate battery State of Charge estimation is of great significance for battery electric vehicles and hybrid electric vehicles. This paper presents an adaptive unscented Kalman filtering method to estimate State of Charge of a lithium-ion battery for battery electric vehicles. The adaptive adjustment of the noise covariances in the State of Charge estimation process is implemented by an idea of covariance matching in the unscented Kalman filter context. Experimental results indicate that the adaptive unscented Kalman filter-based algorithm has a good performance in estimating the battery State of Charge. A comparison with the adaptive extended Kalman filter, extended Kalman filter, and unscented Kalman filter-based algorithms shows that the proposed State of Charge estimation method has a better accuracy.

Keywords: Battery management system; Electric vehicle; Adaptive unscented Kalman filter; State of charge; Lithium-ion battery (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (115)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:36:y:2011:i:5:p:3531-3540

DOI: 10.1016/j.energy.2011.03.059

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