• DocumentCode
    568108
  • Title

    Prediction of battery-SOC of pure electric vehicle

  • Author

    Wang, Cheng

  • Author_Institution
    Fac. of Transp. Eng., Huaiyin Inst. of Technol., Huai´´an, China
  • fYear
    2012
  • fDate
    14-17 July 2012
  • Firstpage
    466
  • Lastpage
    469
  • Abstract
    The accurate value of battery SOC is one of the prerequisites of a pure electric vehicle energy management to achieve optimal control. However, the battery is a highly nonlinear system and its charge-discharge process is difficult to establish accurate mathematical model. Taking into account nonlinear characteristics of BP neural network, and it has parallel structure and ability to learn, so it is suitable for online estimation of battery SOC value. Train BP neural network through Matlab programming, and predict the performance of the battery based on the built neural network model to get the battery SOC prediction. Simulation results show that the the established BP neural network has good adaptability, and is able to predict the mapping relationship between the battery voltage and SOC. This method is access to battery SOC prediction quickly and easily, the maximum error is less than 1% and the results meet the accuracy requirements.
  • Keywords
    backpropagation; battery powered vehicles; energy management systems; mathematical analysis; neurocontrollers; nonlinear control systems; optimal control; prediction theory; system-on-chip; BP neural network training; battery SOC value online estimation; battery-SOC prediction; charge-discharge process; mathematical model; nonlinear system; optimal control; parallel structure; pure electric vehicle energy management; Batteries; Biological neural networks; Mathematical model; Neurons; Predictive models; System-on-a-chip; BP Neural Network; Pure Electric Vehicle; SOC;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science & Education (ICCSE), 2012 7th International Conference on
  • Conference_Location
    Melbourne, VIC
  • Print_ISBN
    978-1-4673-0241-8
  • Type

    conf

  • DOI
    10.1109/ICCSE.2012.6295115
  • Filename
    6295115