Date of Award

8-2026

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Electrical and Computer Engineering (Holcomb Dept. of)

Committee Chair/Advisor

Dr. Ganesh Kumar Venayagamoorthy, Committee Chair

Committee Member

Dr. Dingrui Li

Committee Member

Dr. Rajendra Singh

Abstract

Electric vehicles (EVs) are becoming more common because they help reduce pollution and support the shift away from fossil fuels. However, when many EVs charge at the same time, they can place a large demand on the electric power grid. If this charging is not managed properly, it can overload parts of the distribution system, increase waiting time for EV users, and create the need for expensive grid upgrades. This develops a smarter way to manage EV charging and discharging using electric vehicle grid interfaces (EVGIs) developed by Venayagamoorthy in 2024. An EVGI is a charging interface that can support two-way power flow. In grid-to-vehicle (G2V) operation, power flows from the grid to the EV battery for charging. In vehicle-to-grid (V2G) operation, power can flow from the EV battery back to the grid to support local demand when needed. This allows EVs to act not only as electricity consumers, but also as flexible energy resources. A decision tree-based scheduling method is developed in this study to schedule which EVs should charge, which EVs should provide V2G support, and when EVs should wait in the queue. The proposed method is tested on a modified IEEE 34-bus distribution system using a 24- hour synthetic EV dataset. The results show that coordinated EVGI scheduling can serve more charging EVs, use V2G support when needed, and keep the grid operating within safe limits. The study also shows that the number and type of EVGI chargers at each node strongly a!ect how many EVs can be served. Overall, this study shows that smarter EV scheduling can help reduce stress on the electric grid and improve EV charging service without immediately requiring major infrastructure upgrades. The proposed approach provides a practical method for managing EV charging and grid support in future EV-integrated power distribution systems.

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