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

被引:0
|
作者
Zheng, Hongyu [1 ]
Zong, Changfu [1 ]
机构
[1] Jilin Univ, State Key Lab Automot Simulat & Control, Changchun 130023, Peoples R China
关键词
Information fusion; State estimation; Power battery; Unscented kalman filter;
D O I
10.4028/www.scientific.net/AMM.303-306.975
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
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.
引用
收藏
页码:975 / 978
页数:4
相关论文
共 50 条
  • [1] Vehicle State Information Estimation with the Unscented Kalman Filter
    Ren, Hongbin
    Chen, Sizhong
    Liu, Gang
    Zheng, Kaifeng
    [J]. ADVANCES IN MECHANICAL ENGINEERING, 2014,
  • [2] Unscented Kalman filter for vehicle state estimation
    Antonov, S.
    Fehn, A.
    Kugi, A.
    [J]. VEHICLE SYSTEM DYNAMICS, 2011, 49 (09) : 1497 - 1520
  • [3] State and Parameters Estimation for Distributed Drive Electric Vehicle Based on Unscented Kalman Filter
    Song, Yitong
    Shu, Hongyu
    Chen, Xianbao
    Jing, Changqing
    Guo, Cheng
    [J]. Jixie Gongcheng Xuebao/Journal of Mechanical Engineering, 2020, 56 (16): : 204 - 213
  • [4] Battery State Estimation Using Unscented Kalman Filter
    Zhang, Fei
    Liu, Guangjun
    Fang, Lijin
    [J]. ICRA: 2009 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1-7, 2009, : 3574 - +
  • [5] A novel battery state estimation model based on unscented Kalman filter
    Jiabo Li
    Min Ye
    Kangping Gao
    Shengjie Jiao
    Xinxin Xu
    [J]. Ionics, 2021, 27 : 2673 - 2683
  • [6] A novel battery state estimation model based on unscented Kalman filter
    Li, Jiabo
    Ye, Min
    Gao, Kangping
    Jiao, Shengjie
    Xu, Xinxin
    [J]. IONICS, 2021, 27 (06) : 2673 - 2683
  • [7] Application of Unscented Kalman Filter to Vehicle State Estimation
    Zhu, Tianjun
    Zheng, Hongyan
    [J]. 2008 ISECS INTERNATIONAL COLLOQUIUM ON COMPUTING, COMMUNICATION, CONTROL, AND MANAGEMENT, VOL 2, PROCEEDINGS, 2008, : 135 - +
  • [8] Vehicle State Estimation Based on Adaptive Fading Unscented Kalman Filter
    Liu, Yingjie
    Cui, Dawei
    [J]. MATHEMATICAL PROBLEMS IN ENGINEERING, 2022, 2022
  • [9] Vehicle State Estimation Based on Unscented Kalman State Estimation
    Zhu, Tianjun
    Zheng, Hongyan
    [J]. PROCEEDINGS OF THE 2008 INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN, VOL 1, 2008, : 42 - +
  • [10] Comparison of vehicle state estimation based on nonlinear observer and unscented Kalman filter
    Lu, Sheng
    Lian, Ma-Jun
    Liu, Yang
    Zhao, Yang
    Xiao, Yang
    [J]. Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition), 2020, 50 (04): : 1288 - 1300