Modeling and Optimization for Electric Vehicle Charging Infrastructure

We study how the electric vehicles (EVs) of today would perform in meeting the driving needs of vehicle owners, and propose an optimization model to find locations for charging stations needed to support EV usage. We take publicly available data from travel surveys that are person oriented and construct vehicle centric datasets. Chicago and Seattle metropolitan areas are selected as showcases for implementation. The statistical analysis of the datasets for these cities shows that a large majority of the vehicles travel less than the average range of EVs available in the marketplace. Since the distance traveled is not the only factor affecting EV range, we develop a user charging model that determines where and how to charge an EV given all the trips that the vehicle is supposed to make and the availability of the charging infrastructure. The vehicles that fail to complete all their trips are used as an input to an optimization model that yields optimal charging station locations. We present optimization results based on the Chicago and Seattle data.