Computer Science Department Thesis Defense - Seyedmohammad Kashefi Mofrad

Event Date: 
Thursday, September 14, 2023 - 10:00am to 11:30am EDT
Event Location: 
Event Contact Name: 
Rachael Wang
Event Contact E-mail: 

Please join the Computer Science Department for the upcoming thesis defense:

Presenter: Seyedmohammad Kashefi Mofrad

Thesis title: Efficient Path Planning and Battery Management for Electric Vehicles

Abstract: The rapid advancement in battery technology has brought electric vehicles (EVs) into reality, and the increasing adoption of autonomous electric vehicles (AEVs) has presented significant challenges. Existing research in the realm of IoT has extensively explored EV transportation systems, focusing on aspects like routing, energy management, and grid system equilibrium. In this context, this thesis readdresses the challenge of determining the fastest route for AEVs considering the battery charging time.

Diverging from the current state-of-the-art, our work delves into the prospect of not only minimizing travel time but also maximizing battery life for the optimal utilization of electric vehicles. We commence by formalizing the problem of ”Efficient Path Planning and Battery Management for Electric Vehicles” as a mixed integer linear programming (MILP) model, thereby deriving its optimal solutions mathematically. Given the inherent complexity of the optimization model, we introduce a range of heuristic algorithms designed to address the problem at scale. Furthermore, we establish the problem’s NP-hard nature.

Recognizing the dynamic environment within which EVs operate, we transform the problem into a Markov Decision Process (MDP) and propose a Q-Learning-based reinforcement learning (RL) algorithm to solve it. Our thorough analysis and evaluations, spanning small and large node networks, underscore the efficacy of our proposed methodologies, while also identifying the superior approach in practical scenarios.

This study signifies a significant stride towards unlocking the full potential of Autonomous EVs, optimizing both travel time and battery life. Through this research, we aim to provide valuable insights into the efficient utilization of AEVs, thereby contributing to the advancement of sustainable and intelligent transportation systems

Committee Members:
Dr. Dariush Ebrahimi (supervisor, committee chair), Dr. Sabah Mohammed (co-supervisor), Dr. Thiago E Alves de Oliveira, Dr. Salimur Choudhury (Queen’s University)

Please contact for the Zoom link.
Everyone is welcome.