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

被引:4
作者
Liu, Hongwei [1 ]
Wang, Haifeng [1 ]
Guo, Chong [1 ]
机构
[1] Jilin Univ, State Key Lab Automot Simulat & Control, Changchun 130023, Peoples R China
来源
PROGRESS IN RENEWABLE AND SUSTAINABLE ENERGY, PTS 1 AND 2 | 2013年 / 608-609卷
关键词
State of Energy; Battery model; Unscented Kalman Filter; Adaptive algorithm;
D O I
10.4028/www.scientific.net/AMR.608-609.1627
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
引用
收藏
页码:1627 / 1630
页数:4
相关论文
共 4 条
  • [1] [Anonymous], 2003, FREEDOM CAR BATTERY
  • [2] Charkhgard M., 2010, IEEE Trans. on Industrial Electronics, V57
  • [3] Plerr G L, 2001, P 19 INT EL VEH S BU, P193
  • [4] An optimal adaptive Kalman filter
    Yang, Yuanxi
    Gao, Weiguang
    [J]. JOURNAL OF GEODESY, 2006, 80 (04) : 177 - 183