• DocumentCode
    3004929
  • Title

    LiFePO4 battery pack capacity estimation for electric vehicles based on unscented Kalman filter

  • Author

    Lei Zhao ; Guoqing Xu ; Weimin Li ; Taimoor, Zahid ; Zhibin Song

  • Author_Institution
    Shenzhen Inst. of Adv. Technol., Shenzhen, China
  • fYear
    2013
  • fDate
    26-28 Aug. 2013
  • Firstpage
    301
  • Lastpage
    305
  • Abstract
    As is known to all, an accurate on-line estimation of the battery capacity is important for forecasting the EV driving range. But because of the different driving environment and the property of the battery, it is hard to estimate the capacity of the battery pack. This paper presents an unscented Kalman filtering method to estimate the state of charge of LiFePO4 battery pack. Five comparison experiments with different open circuit voltage curves shows that the unscented Kalman filter has a better performance than extended kalman filter.
  • Keywords
    Kalman filters; battery powered vehicles; iron compounds; lithium compounds; phosphorus compounds; secondary cells; EV driving range; LiFePO4; battery pack capacity estimation; electric vehicles; unscented Kalman filter; Batteries; Computational modeling; Estimation; Integrated circuit modeling; Kalman filters; Noise; System-on-chip; Thevenin model; battery management system(BMS); extended kalman filter (EKF); state of charge(SOC); unscented Kalman filter(UKF);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information and Automation (ICIA), 2013 IEEE International Conference on
  • Conference_Location
    Yinchuan
  • Type

    conf

  • DOI
    10.1109/ICInfA.2013.6720314
  • Filename
    6720314