State of Energy Estimation Based on AUKF for Lithium Battery Used on Pure Electric Vehicle

Abstract:

State of Energy can be used to predict the driving mileage of electric vehicles, design the control strategy of vehicle energy distribution, and improve the safety of electric vehicle. Accurate estimaion of state of energy is one of the key technologies in the study on battery management system of electric vehicle. In this paper, the State of Energy is estimated by using Unscented Kalman Filter, while the process noise and measurement noise is adjusted by using the Sage-Husa adaptive algorithm, as a result the estimation accuracy is improved. The result shows that the State of Energy estimation by using Adaptive Unscented Kalman Filter algorithm is satisfactory to electric vehicle.

• Periodical: Advanced Materials Research (Volumes 608-609)
• Pages: 1627-1630
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.608-609.1627
• Online since: December 2012
• Authors: Hong Wei Liu, Hai Feng Wang, Chong Guo
• Keywords: Adaptive Algorithm, Battery Model, State of Energy, Unscented Kalman Filter (UKF)

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