Modeling and Assessment of Dynamic Charging for Electric Vehicles in Metropolitan Cities

Abstract

Electric vehicles (EVs) have emerged to be the future of transportation as the world observes its rising demand and usage across continents. However, currently, one of the biggest bottlenecks of EVs is the battery. Small batteries limit the EVs driving range, while big batteries are expensive and not environmentally friendly. One potential solution to this challenge is the deployment of charging roads, i.e., dynamic wireless charging systems installed under the roads that enable EVs to be charged while driving. In this thesis, we establish a framework using stochastic geometry to study the performance of deploying charging roads in metropolitan cities. We first present the course of actions that a driver may take when driving from a random source to a random destination, and then analyze the distribution of the distance to the nearest charging road and the probability that the trip passes through at least one charging road. These probability distributions assist not only urban planners and policy makers in designing deployment plans of dynamic wireless charging systems, but also drivers and automobile manufacturers in choosing the best driving routes given the road conditions and level of energy of EVs.