Date of Award:

5-2025

Document Type:

Dissertation

Degree Name:

Doctor of Philosophy (PhD)

Department:

Electrical and Computer Engineering

Committee Chair(s)

Greg Droge

Committee

Greg Droge

Committee

Jake Gunther

Committee

Donald Cripps

Committee

Hongjie Wang

Committee

Mario Harper

Abstract

While electric vehicles (EVs) are becoming increasingly popular and can provide cost, maintenance, and environmental benefits, they present unique challenges for regular operation, especially in fleets of vehicles. The limited range, long charging times, and high power requirements of EVs complicate the operations of fleets comprised of EVs. Additionally, the charging costs of EVs can be complex, depending on the time of charging, the energy charged, and the power level of the charging session. These complex charging costs can be significantly affected by the presence of uncontrolled loads. These challenges are further complicated by the inevitable variations in the real world, caused by conditions such as traffic congestion, weather, and equipment malfunctions.

Accordingly, the scheduling of EV charging in fleets is a complex, but necessary, problem to address. This research develops methods to schedule the charging of fleets of EVs that are cost-effective, robust, and scalable. Exact, but computationally expensive, approaches are developed to schedule when and how much each vehicle should charge to minimize the cost of charging while maintaining the operational constraints of the fleet. A technique for adjusting the schedules based on up-to-date arrival time, battery level, and other feedback is demonstrated. These developments are significant steps towards addressing the challenges of scheduling the charging of EVs in fleets and enabling the further adoption of EVs in vehicle fleets.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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