• DocumentCode
    3241665
  • Title

    Artificial neural network for predictions of vehicle drivable range and period

  • Author

    Chen, Yuan-Lin ; Chih Hsien Huang ; Kuo, Yao-Wen ; Wang, Shung-Sung

  • Author_Institution
    Inst. of Electro-Mech. Eng., Ming Chi Univ. of Technol., New Taipei, Taiwan
  • fYear
    2012
  • fDate
    24-27 July 2012
  • Firstpage
    329
  • Lastpage
    333
  • Abstract
    An artificial neural network for predicting the drivable range and period for vehicles under remaining fuel is presented in this paper. The driver´s driving behaviors, vehicle condition and traffic route are all taking into consideration. The presented method could learn from the statuses of vehicle such as vehicle speed and engine speed and traffic route to make a prediction of vehicle drivable range and period under remaining fuel. The experimental results are presented for to guarantee the artificial neural network could estimate the vehicle drivable range and period accurately.
  • Keywords
    neural nets; road traffic; traffic engineering computing; artificial neural network; driving behaviors; engine speed; remaining fuel; traffic route; vehicle condition; vehicle drivable range; vehicle speed; Artificial neural networks; Engines; Estimation; Fuels; Neurons; Training; Vehicles; artificial neural network; driver´s driving behaviors; vehicle drivable period; vehicle drivable range;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Vehicular Electronics and Safety (ICVES), 2012 IEEE International Conference on
  • Conference_Location
    Istanbul
  • Print_ISBN
    978-1-4673-0992-9
  • Type

    conf

  • DOI
    10.1109/ICVES.2012.6294324
  • Filename
    6294324