• DocumentCode
    1792325
  • Title

    Estimation of power battery SOC based on improved BP neural network

  • Author

    Chao Dong ; Guanlan Wang

  • Author_Institution
    Tianjin Key Lab. of Control Theor. & Applic. in Complicated Syst., Tianjin Univ. of Technol., Tianjin, China
  • fYear
    2014
  • fDate
    3-6 Aug. 2014
  • Firstpage
    2022
  • Lastpage
    2027
  • Abstract
    According to the limitations and shortcomings of BP neural network in estimating the battery state of charge(State of Charge, SOC), such as slow convergence speed and poor generalization, this paper puts forward an improved BP neural network method of battery SOC estimation. Train the improved BP neural network with the experimental data. It compares the trained neural network of SOC with the real values, and uses Matlab to simulate in order to verify the correctness of the algorithm.
  • Keywords
    backpropagation; battery powered vehicles; neural nets; power engineering computing; secondary cells; BP neural network method; Matlab; battery state of charge; convergence speed; electric vehicles; lithium-ion battery; power battery SOC estimation; Algorithm design and analysis; Batteries; Biological neural networks; Genetic algorithms; System-on-chip; Training; BP neural network; SOC; power battery;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Mechatronics and Automation (ICMA), 2014 IEEE International Conference on
  • Conference_Location
    Tianjin
  • Print_ISBN
    978-1-4799-3978-7
  • Type

    conf

  • DOI
    10.1109/ICMA.2014.6886014
  • Filename
    6886014