• DocumentCode
    1734642
  • Title

    Estimation of state of charge of battery based on Extended Kalman Filtering

  • Author

    Chen Huangjie ; Ma Yan ; Zhao Haiyan

  • Author_Institution
    Dept. of Control Sci. & Eng., Jilin Univ., Changchun, China
  • fYear
    2013
  • Firstpage
    7632
  • Lastpage
    7635
  • Abstract
    Estimation of state of charge (SOC) is the key technique for electric vehicle power management system. A simple battery nonlinear equivalent model under AMESim is built. By using Extended Kalman Filter method, battery SOC is estimated on-line through optimal estimation in the sense of minimum-variance when battery current fluctuates wildly. Co-simulation result shows that Extended Kalman Filter method which can estimate SOC with error less than 1.8% has high accuracy.
  • Keywords
    Kalman filters; electric vehicles; estimation theory; system-on-chip; battery SOC; battery nonlinear equivalent model; electric vehicle power management system; extended Kalman filter method; extended Kalman filtering; minimum-variance; state of charge of battery; Batteries; Electronic mail; Estimation; Kalman filters; Software packages; System-on-chip; AMESim; Electric Vehicle; Extended Kalman Filtering; State of Charge (SOC);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control Conference (CCC), 2013 32nd Chinese
  • Conference_Location
    Xi´an
  • Type

    conf

  • Filename
    6640783