Abstract

A key factor in promoting sustainable economies, industries, and society is the use of electric vehicles (EVs). They are an important step toward a greener future since they reduce greenhouse gas emissions, improve air quality, and encourage energy independence. The demand for charging stations (CSs) will undoubtedly rise as the global EV market continues to grow over the next few decades, requiring significant investments from both public and private players. By creating a cutting-edge AI model, this work tackles the crucial problems of EVs range prediction and charging optimization decisions. The contributions are summarized as the below: 1. Developed AI models to predict estimated range for EVs without explicitly stating the use of data. 2. Implemented a binary decision-making process to determine the necessity of EV charging, enhancing energy management strategies and mitigating range anxiety. 3. Proposed a comprehensive solution incorporating vehicle-to-vehicle (V2V) and grid- to-vehicle (G2V) energy sharing, selecting optimal charging stations (CS1, CS2) or vehicles (V2, V3) based on factors like waiting time, distance, and energy provision. 4. Curated a dataset highlighting essential data variables crucial for optimizing AI models for V2V energy sharing, facilitating the development of sustainable transportation solutions. The technology, when integrated into the V2V framework, creates a strong foundation for self-governing energy and enables effective energy sharing between EVs. These developments have important ramifications for encouraging the use of EVs, improving customer satisfaction, and furthering sustainable transportation programs

Library of Congress Subject Headings

Electric vehicles--Fuel systems; Electric vehicles--Batteries; Vehicular ad hoc networks (Computer networks); Battery charging stations (Electric vehicles); Artificial intelligence

Publication Date

4-2024

Document Type

Thesis

Student Type

Graduate

Degree Name

Electrical Engineering (MS)

Department, Program, or Center

Electrical Engineering

Advisor

Jinane Mounsef

Advisor/Committee Member

Abdulla Ismail

Advisor/Committee Member

Ioannis Karamitsos

Campus

RIT Dubai

Plan Codes

EEEE-MS

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