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

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 adjus...

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Bibliographic Details
Published in:Energy (Oxford) 2011-05, Vol.36 (5), p.3531-3540
Main Authors: Sun, Fengchun, Hu, Xiaosong, Zou, Yuan, Li, Siguang
Format: Article
Language:English
Subjects:
Adaptive algorithms
Adaptive unscented Kalman filter
algorithms
Applied sciences
batteries
Battery
Battery management system
Charge
covariance
Direct energy conversion and energy accumulation
Electric batteries
Electric charge
Electric vehicle
Electric vehicles
Electrical engineering. Electrical power engineering
Electrical power engineering
Electrochemical conversion: primary and secondary batteries, fuel cells
Exact sciences and technology
Kalman filtering
Lithium-ion batteries
Lithium-ion battery
State of charge
vehicles (equipment)
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Summary: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. ► Adaptive unscented Kalman filtering is proposed to estimate State of Charge of a lithium-ion battery for electric vehicles. ► The proposed method has a good performance in estimating the battery State of Charge. ► A comparison with three other Kalman filtering algorithms shows that the proposed method has a better accuracy.
ISSN:0360-5442
DOI:10.1016/j.energy.2011.03.059