• DocumentCode
    630292
  • Title

    High accuracy state-of-charge online estimation of EV/HEV lithium batteries based on Adaptive Wavelet Neural Network

  • Author

    Fengwu Zhou ; Lujun Wang ; Huiping Lin ; Zhengyu Lv

  • Author_Institution
    Coll. of Electr. Eng., Zhejiang Univ., Hangzhou, China
  • fYear
    2013
  • fDate
    3-6 June 2013
  • Firstpage
    513
  • Lastpage
    517
  • Abstract
    The state of charge online estimation of EV/HEV lithium battery with high accuracy is very important, Since it can be used to prolong the battery lifetime and improve its performances. Traditional SOC estimation algorithms have show their drawbacks apparently, so the Adaptive Wavelet Neural Network(AWNN) based SOC estimation model is presented. By using adaptive algorithm to train the model, the accurate online SOC estimation is implemented. The simulation and experiment results are given and show that the proposed algorithm is an effective and feasible method to estimate the SOC of the lithium battery with fastest convergence speed and most high accuracy.
  • Keywords
    battery powered vehicles; hybrid electric vehicles; learning (artificial intelligence); neural nets; power engineering computing; secondary cells; wavelet transforms; AWNN; EV lithium batteries; HEV lithium batteries; adaptive algorithm; adaptive wavelet neural network; convergence speed; high accuracy state-of-charge online estimation; hybrid electric vehicle; online SOC estimation algorithms; prolong battery lifetime; Batteries; Adaptive Wavelet Neural Network(AWNN); EV/HEV; convergence speed; state of charge(SOC);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    ECCE Asia Downunder (ECCE Asia), 2013 IEEE
  • Conference_Location
    Melbourne, VIC
  • Print_ISBN
    978-1-4799-0483-9
  • Type

    conf

  • DOI
    10.1109/ECCE-Asia.2013.6579145
  • Filename
    6579145