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

Duc Minh Nguyen, Mustafa Abdelsalam Kishk, Mohamed-Slim Alouini

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


With the increasing demand for greener and more energy efficient transportation solutions, EVs have emerged to be the future of transportation across the globe. One of the biggest bottlenecks of EVs is the battery. Small batteries limit the EVs driving range, while big batteries are expensive and not environment-friendly. One potential solution to this challenge is the deployment of charging roads. 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. 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.
Original languageEnglish (US)
Pages (from-to)1-1
Number of pages1
JournalIEEE Open Journal of Vehicular Technology
StatePublished - 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-10-23


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