Date of Award:

8-2025

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Electrical and Computer Engineering

Committee Chair(s)

Greg Droge

Committee

Greg Droge

Committee

Burak Sarsilmaz

Committee

Hongjie Wang

Abstract

Electric vehicles and electric-vehicle fleets are gaining widespread usage. A major challenge of managing an electric-vehicle fleet is scheduling when the vehicles can charge and when the vehicles can complete their tasks. Finding smart ways to schedule electric-vehicle charging reduces both the cost of charging vehicles and increases the health of the grid and the environment. This best-of-both-worlds outcome is because of the pricing structure that electric utility companies use. When minimizing the cost of charging the vehicles, it is important to consider that vehicle fleets have constraints on when they can charge. Vehicles are usually limited by the tasks they need to perform. Examples of these tasks include buses attending to their stops and packages being delivered to houses. Since the objective is to minimize the cost of charging, and there are constraints on when the vehicles can charge, this problem can be formulated as an optimization problem. Minimizing a cost given constraints creates an optimization problem. An optimization solution approach called Ant Colony Optimization is used firstly to create energy-efficient routes and secondly to schedule the routes in a way that aims to minimize the cost of charging the electric-vehicle fleet. The electric-vehicle fleet in this work has different types of electric vehicles and different schedule constraints based on the tasks the vehicles complete. The ant colony approach makes good solutions in a relatively short period of time.

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