Exploratory spatial distribution of dynamic wireless charging demand for EVs

Abstract

This paper demonstrates a framework to optimize the investment of dynamic wireless charging (DWC) infrastructure for charging-in-motion services. The services require DWC infrastructure deployed on public roads to extend battery lifespan and reduce battery sizes while increasing driving range simultaneously. Since it would be financially infeasible to have such investments serving only few vehicles, estimation of power demand in real world applications will be valuable to the deployment. We propose a traffic-based power demand (TBPD) framework to estimate the demand since not only number and type of vehicles but also their spatial distribution of power demands have to be considered for the optimization. Monte Carlo simulations are incorporated into the proposed framework to estimate both number and type of vehicles as well as their speed profiles in a road network. Spatial distribution of power demands is derived with the simulated speed profiles and lays a foundation for the optimization. An example of applying the proposed framework in a corridor in Chattanooga, TN is presented for further discussion. Based on the power demand estimation, it is found that road segments between slightly upstream to and farther downstream from a stop line are ideal candidates to server the purpose.