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Smart Mobility and Electrified Vehicles Research at McMaster University

✓ Fully Funded 🎓 Computer Science 🎓 Electrical Engineering 🎓 Mechanical Engineering autonomous systems mechatronics artificial intelligence smart mobility electrified vehicles energy storage systems electric powertrains hybrid powertrains

Explore breakthroughs in smart mobility by researching electric and autonomous vehicle technologies at McMaster University. Contribute to innovations in AI, energy systems, and mechatronics within a leading automotive resource center.

AI-generated overview

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Why This Research Matters

This research tackles critical challenges in transportation, promoting cleaner, safer, and more efficient mobility solutions. Advancements in electric and autonomous vehicles will reduce environmental impacts, improve urban traffic safety, and drive sustainable innovations responding to global climate and energy concerns.

Artificial Intelligence Mechatronics Engineering Energy Storage Systems Electric Powertrains Hybrid Powertrains Autonomous Systems

Project Description

Project Overview

This PhD and MSc research opportunity is based at the McMaster Automotive Resource Centre (MARC), a leading Canadian facility focused on electrified vehicles and smart mobility innovation. The research encompasses Artificial Intelligence, Mechatronics Engineering, Energy Storage Systems, Electric & Hybrid Powertrains, and Autonomous Systems.

These interdisciplinary projects aim to develop cleaner, smarter, and more efficient transportation solutions addressing climate change, energy efficiency, and urban safety challenges.

What You Will Do

As part of the MARC team, you will engage in cutting-edge research using state-of-the-art laboratories and testing infrastructure. You will collaborate with industry partners and academic departments to push forward innovations in autonomous and electrified transportation systems.

The role involves practical problem-solving, teamwork, and applying research findings to real-world mobility challenges.

Expected Outcomes

Your work will contribute to advancing technology in smart mobility, potentially redefining transportation, reducing environmental footprints, and enhancing the safety and connectivity of urban environments.

The research will generate impactful findings that support global transitions towards sustainable and autonomous vehicle systems.

Why This Matters

This research is integral to addressing pressing transportation challenges worldwide. By enhancing electric and autonomous vehicle technologies, the work aids in combating climate change, improving energy usage, and fostering safer, more efficient urban mobility. McMaster University’s leadership and collaborative environment further amplify the potential benefits of this work.

Entry Requirements

Applicants should hold a Bachelor’s degree in Mechanical Engineering, Electrical Engineering, Computer Science, or a closely related field. Strong academic performance and demonstrated research interest are expected.

How to Apply

To express interest, candidates should complete the official Expression of Interest form at http://bit.ly/4mbyoW2. For more details, refer to the LinkedIn post: https://www.linkedin.com/posts/dr-ryan-ahmed_phd-mastersprogram-fullyfunded-ugcPost-7449485280740442115-Euo_

Eligibility

UK/Home
EU
International

Supervisor Profile

DR
Dr. Ryan Ahmed
McMaster University

Dr. Ryan Ahmed leads the research group at McMaster University's Automotive Resource Centre, specializing in electrified vehicles and smart mobility innovations. His work integrates interdisciplinary approaches across mechanical, electrical, and computer engineering to advance autonomous systems and sustainable transportation technologies. He is recognized for fostering collaborations between academia and industry.

Key Publications

2018 952 citations
Long short-term memory networks for accurate state-of-charge estimation of Li-ion batteries
2014 196 citations
Automotive internal-combustion-engine fault detection and classification using artificial neural network techniques
2014 188 citations
Reduced-order electrochemical model parameters identification and soc estimation for healthy and aged li-ion batteries part i: Parameterization model development for healthy …
2015 180 citations
Model-based parameter identification of healthy and aged li-ion batteries for electric vehicle applications
2014 140 citations
Reduced-order electrochemical model parameters identification and state of charge estimation for healthy and aged Li-ion batteries—Part II: Aged battery model and state of …

Research Contributions

Application of long short-term memory networks for accurate state-of-charge (SOC) estimation of Li-ion batteries.
Enhanced battery management leading to improved performance and reliability of Li-ion batteries in various applications.
Automotive internal combustion engine fault detection and classification using artificial neural network techniques.
Improved fault detection methods leading to better vehicle maintenance and reduced breakdowns.
Development and parameter identification of reduced-order and electrochemical models for healthy and aged Li-ion batteries.
Provided accurate models for battery state estimation enabling more efficient electric vehicle battery management.

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