Load Demand Modeling of Large-Scale Charging Infrastructure for Electric Vehicles In-Motion

Abstract

As electric vehicles (EVs) become increasingly common in transportation infrastructures, the need to strengthen and diversify the EV charging systems becomes more necessary. Dynamic Wireless Power Transfer (DWPT) roadways allow EVs to be recharged while in-motion, thus allowing to improve the driving ranges and facilitating the widespread adoption of EVs. One major challenge to adopt large-scale DWPT networks is to efficiently and accurately develop load demand models to comprehend the complex behavior on power distribution grid due to difficulty in developing power electronic simulations for charging systems consisting of either numerous transmitter pads or high traffic volumes. This paper proposes a novel modified Toeplitz convolution method for efficient large-scale DWPT load demand modeling. The proposed method achieves more accurate modeling of DWPT systems from a few transmitter pads to tens of miles in real-world traffic scenarios with light computational load. Test results for a small-scale DWPT system are first generated to validate the accuracy of the proposed method before scaling to large-scale load demand modeling where real-world traffic flow data is utilized in DWPT networks ranging from 2-10 miles. A comparative analysis is further performed for the scenarios under consideration to demonstrate the efficiency and accuracy of the proposed method.