Information Fusion for State Estimation of Power Battery in Electric Vehicle Based on Unscented Kalman Filter

Hong Yu Zheng; Chang Fu Zong

doi:10.4028/www.scientific.net/AMM.303-306.975

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 303-306Information Fusion for State Estimation of Power...

Information Fusion for State Estimation of Power Battery in Electric Vehicle Based on Unscented Kalman Filter

Abstract:

The power battery state of charge (SOC) in electric vehicles is not easy to measure accurately or apply a sensor but the expense is increased. However the variable of SOC is great importance to control of electric vehicles. A power battery model is built by the Partnership for a New Generation of Vehicles (PNGV) model to estimate the state of SOC. In order to make a high accurate estimate for SOC value, an information fusion algorithm based on unscented kalman filter (UKF) is introduced to design an observer. The test results show that the observer based information fusion and UKF are effective and accuracy, so it is may apply it the electric vehicle control and observation.

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Periodical:

Applied Mechanics and Materials (Volumes 303-306)

Pages:

975-978

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.303-306.975

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Online since:

February 2013

Authors:

Hong Yu Zheng, Chang Fu Zong

Keywords:

Information Fusion, Power Battery, State Estimation, Unscented Kalman Filter

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