  With the increasing demand for greener and more energy efficient
transportation solutions, electric vehicles (EVs) have emerged to be the future
of transportation across the globe. 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 paper, we use tools from stochastic geometry
to establish a framework that enables evaluating the performance of charging
roads deployment in metropolitan cities. We first present the course of actions
that a driver should take when driving from a random source to a random
destination in order to maximize dynamic charging during the trip. Next, we
analyze the distribution of the distance to the nearest charging road. This
distribution is vital for studying multiple performance metrics such as the
trip efficiency, which we define as the fraction of the total trip spent on
charging roads. Next, we derive the probability that a given trip passes
through at least one charging road. The derived probability distributions can
be used to assist urban planners and policy makers in designing the deployment
plans of dynamic wireless charging systems. In addition, they can also be used
by drivers and automobile manufacturers in choosing the best driving routes
given the road conditions and level of energy of EV battery.
