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
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"
Conference_Titel :
Connected Vehicles and Expo (ICCVE), 2014 International Conference on
DOI :
10.1109/ICCVE.2014.7297559