Modeling and Analysis of Dynamic Charging for EVs: A Stochastic Geometry Approach

TL;DR

This paper develops a stochastic geometry framework to evaluate the deployment and performance of dynamic wireless charging roads for electric vehicles in cities, aiding urban planning and route optimization.

Contribution

It introduces a novel stochastic geometry-based model to analyze the distribution and probability of EVs encountering charging roads during trips, facilitating deployment strategies.

Findings

Derived the distribution of distances to nearest charging roads

Calculated the probability of trips passing through charging roads

Provided insights for urban planning and route optimization

Abstract

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.