Express Charge Point Management Methods for Electric Vehicle Charging Stations
Assoc. Prof. Dr. Nilgün Fescioglu Unver, TOBB University
March 29, at 1:40 p.m.
EA-409
One of the most effective precautions to limit the environmental pollution is increasing electric vehicle usage in transportation area. Electric Vehicles Initiative (EVI) member 16 countries target having 100 million electric vehicles on their roads by the year 2030. It takes approximately 30 minutes to fully charge an electric vehicle with a high speed charger. The length of the charging duration, increases the number of vehicles waiting at a charging station and the time the vehicles have to wait at the stations. The waiting time can be reduced by the help of Intelligent Transportation Systems (ITS) which adapt information and communication technologies into transportation area. Introducing express charging concept for electric vehicle charging stations can reduce the waiting time even more for a certain portion of the vehicles. This study aims developing express charge station management methods for electric vehicle charging stations. In this study we focus on three different Express charge point management methods. The first method, dynamically re-orders the queue of vehicles waiting for charging according to the specific charging needs of the vehicles. The second and third method handle the vehicles in two classes (High and Low Priority) and automatically assign charge points to classes such that the relative average waiting time between these classes is kept at a pre-specified level, and enables the station to show a self-controlling behavior. These methods differ from each other by the techniques they use and the level of private vehicle information required. These self-controlling system methods are applicable to other domains such as production systems.
Nilgun Fescioglu-Unver - is an Associate Professor in the Industrial Engineering Department of TOBB University of Economics and Technology, Ankara, Turkey. She holds a Ph.D. from Northeastern University in Industrial Engineering, an M.S. in Information Systems from Northeastern University, an MBA degree and M.S. and BS in Mechanical Engineering from Middle East Technical University, Turkey. Her research interests include simulation and adaptive systems applications in health care, production and energy sectors.
