• DocumentCode
    3671619
  • Title

    Intelligent energy consumption estimation for electric vehicles: Business processes and services

  • Author

    Vasileios Asthenopoulos;Pavlos Kosmides;Evgenia Adamopoulou;Konstantinos Demestichas

  • Author_Institution
    Institute of Communication and Computer Systems - ICCS, National Technical University of Athens, Zografou-Athens, Greece
  • fYear
    2014
  • Firstpage
    293
  • Lastpage
    298
  • Abstract
    Nowadays, Fully Electric Vehicles are in the spotlight of energy-efficient and sustainable mobility. Their overall efficiency however, as well as their commercial viability, depend strongly on the degree of confidence they offer to the driver in terms of energy savings and range characteristics. To this end, advanced consumption prediction mechanisms must be implemented in order to enable the provision of energy-based routing functionalities. In this context, this paper presents an innovative energy consumption estimation service that relies on the vehicles´ travelling history and experience and deploys machine learning mechanisms in order to obtain accurate, robust and cost-efficient estimations.
  • Keywords
    "Training","Databases","Maximum likelihood estimation","Engines","Roads","Energy consumption"
  • Publisher
    ieee
  • Conference_Titel
    Connected Vehicles and Expo (ICCVE), 2014 International Conference on
  • Type

    conf

  • DOI
    10.1109/ICCVE.2014.7297559
  • Filename
    7297559