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

Author:
Sun, Fengchun, Hu, Xiaosong, Zou, Yuan, Li, Siguang
Source:
Energy 2011 v.36 no.5 pp. 3531-3540
ISSN:
0360-5442
Subject:
algorithms, batteries, covariance, vehicles (equipment)
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.
Agid:
953325