Modeling and Analysis of Dynamic Charging for EVs: A Stochastic Geometry Approach
Type
ArticleKAUST Department
Communication Theory LabComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering
Electrical Engineering Program
Date
2020Preprint Posting Date
2020-09-08Permanent link to this record
http://hdl.handle.net/10754/665133
Metadata
Show full item recordAbstract
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.Citation
Nguyen, D. M., Kishk, M., & Alouini, M.-S. (2020). Modeling and Analysis of Dynamic Charging for EVs: A Stochastic Geometry Approach. IEEE Open Journal of Vehicular Technology, 1–1. doi:10.1109/ojvt.2020.3032588arXiv
2009.03726Additional Links
https://ieeexplore.ieee.org/document/9233928/https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9233928
ae974a485f413a2113503eed53cd6c53
10.1109/OJVT.2020.3032588