• DocumentCode
    233513
  • Title

    The research on vehicle driving pattern characteristic parameters search algorithm based on parallel computing

  • Author

    Song Wen ; Zhang Xin ; Tian Yi ; Zhang Xinn ; Song Jianfeng

  • Author_Institution
    Sch. of Mech. Electr. & Control Eng., Beijing Jiaotong Univ., Beijing, China
  • fYear
    2014
  • fDate
    28-30 July 2014
  • Firstpage
    147
  • Lastpage
    150
  • Abstract
    There is close relationship between the driving pattern and the energy saving of hybrid electric vehicle. In order to improve the vehicle driving pattern recognition model and the intelligent control strategy of hybrid electric vehicle, a vehicle driving pattern characteristic parameters search algorithm based on parallel computing is created in this paper. This algorithm mainly includes assigning the work threads according to the numbers of CPU nucleuses in genetic algorithm on average and choosing the better starting point from each set in floating search algorithm. The results shows the vehicle driving pattern characteristic parameters search algorithm based on parallel computing could reduce the searching time greatly and satisfy the demand of driving pattern recognition.
  • Keywords
    genetic algorithms; hybrid electric vehicles; intelligent control; pattern recognition; road vehicles; search problems; floating search algorithm; genetic algorithm; hybrid electric vehicle; intelligent control strategy; parallel computing; vehicle driving pattern characteristic parameters search algorithm; vehicle driving pattern recognition model; Algorithm design and analysis; Central Processing Unit; Genetic algorithms; Instruction sets; Parallel processing; Pattern recognition; Vehicle driving; driving pattern characteristic parameters search algorithm; floating search algorithm; genetic algorithm; parallel computing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control Conference (CCC), 2014 33rd Chinese
  • Conference_Location
    Nanjing
  • Type

    conf

  • DOI
    10.1109/ChiCC.2014.6896612
  • Filename
    6896612