By applying statistical geometry to analyzing urban road networks, KAUST researchers have advanced understanding of how wireless charging roads might influence driver behavior and city planning in a future where electric vehicles (EVs) dominate the car market. (Stochastic geometry is a branch of applied probability particularly adapted to the study of random phenomena on a plane.)

In an open-access paper published in the IEEE Open Journal on Vehicular Technology used tools from stochastic geometry to establish a framework that enables evaluating the performance of charging roads deployment in metropolitan cities.

Our work is motivated by the global trend of moving towards green transportation and EVs. Efficient dynamic charging systems, such as wireless power transfer systems installed under roads, are being developed by researchers and technology companies around the world as a way to charge EVs while driving without the need to stop. In this context, there is a need to mathematically analyze the large-scale deployment of charging roads in metropolitan cities.

—Mustafa Kishk, co-author

The researchers 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, they analyzed the distribution of the distance to the nearest charging road. Next, they derived 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.

Our main challenge is that the metrics used to evaluate the performance of dynamic charging deployment, such as the distance to the nearest charging road on a random trip, depend on the starting and ending points of each trip. To correctly capture those metrics, we had to explicitly list all possible situations, compute the metrics in each case and evaluate how likely it is for each situation to happen in reality. For this, we used an approach called stochastic geometry to model and analyze how these metrics are affected by factors such as the density of roads and the frequency of dynamic charging deployment.

—Duc Minh Nguyen, first author

Applying this analysis to the Manhattan area of New York, which has a road density of one road every 63 meters, Kishk and Nguyen, with research leader Mohamed-Slim Alouini, determined that a driver would have an 80% chance of encountering a charging road after driving for 500 meters when wireless charging is installed on 20% of roads.

Resources

• Nguyen, D.M., Kishk, M.A. & Alouini, M.-S. (2020) “Modeling and analysis of dynamic charging for EVs: A stochastic geometry approach.” IEEE Open Journal on Vehicular Technology 1, 1 doi: 10.1109/OJVT.2020.3032588