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posted on 2020-12-15, 19:28 authored by Akhil PrasadAkhil Prasad

Presently, internal combustion engines provide power to move the majority of vehicles on the roadway. While battery-powered electric vehicles provide an alternative, their widespread acceptance is hindered by range anxiety and longer charging/refueling times. Dynamic wireless power transfer (DWPT) has been proposed as a means to reduce both range anxiety and charging/refueling times. In DWPT, power is provided to a vehicle in motion using electromagnetic fields transmitted by a transmitter embedded within the roadway to a receiver at the underside of the vehicle. For commercial vehicles, DWPT often requires transferring hundreds of kW through a relatively large airgap (> 20 cm). This requires a high-power DC-AC converter at the transmitting end and a DC-AC converter within the vehicle.

In this research, a focus is on the development of models that can be used to support the design of DWPT systems. These include finite element-based models of the transmitter/receiver that are used to predict power transfer, coil loss, and core loss in DWPT systems. The transmitter/receiver models are coupled to behavioral models of power electronic converters to predict converter efficiency, mass, and volume based upon switching frequency, transmitter/receiver currents, and source voltage. To date, these models have been used to explore alternative designs for a DWPT intended to power Class 8-9 vehicles on IN interstates. Specifically, the models have been embedded within a genetic algorithm-based multi-objective optimization in which the objectives include minimizing system mass and minimizing loss. Several designs from the optimization are evaluated to consider practicality of the proposed designs.


Degree Type

Master of Science


Electrical and Computer Engineering

Campus location

West Lafayette

Advisor/Supervisor/Committee Chair

Dr. Steven D. Pekarek

Additional Committee Member 2

Dr. Dionysios Aliprantis

Additional Committee Member 3

Dr. Shreyas Sundaram