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UOW

PhD Student in Electromagnetic Design of Electric Machines for EV Applications

✓ Funded (Competition) ⏰ Closing Soon 🎓 Electrical Engineering 🎓 Mechanical Engineering finite element method electric machines electromagnetic design electric vehicles permanent magnet motors reluctance machines motor prototyping dynamometer testing

Explore advanced electromagnetic design and analysis of electric motors for EVs using FEM simulations and experimental validation. Collaborate with industry and develop prototype motors to optimize performance and efficiency in vehicle powertrains.

AI-generated overview

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Why This Research Matters

This research supports the development of high-efficiency, reliable electric motors essential for the widespread adoption of electric vehicles. Improved motor designs contribute to reducing emissions and enhancing the sustainability of automotive transport.

Electromagnetic Design Electric Machines Finite Element Method Electric Vehicle Applications Motor Prototyping Performance Testing

Project Description

Project Overview

This PhD project focuses on the electromagnetic design and analysis of electric machines tailored for electric vehicle (EV) applications. The research involves finite element method (FEM) based simulations and experimental validation to optimize electric motor performance, efficiency, and thermal behavior. The goal is to advance the understanding and capabilities of permanent magnet and reluctance machines for automotive powertrain systems.

What You Will Do

You will perform detailed electromagnetic simulations using tools like ANSYS, Motor-CAD, and MATLAB/Simulink, including advanced machine modeling in reference frames and control strategy development. Building and testing prototypes with industrial dynamometer systems will validate your models. You will analyze data to evaluate efficiency, losses, and thermal effects, and troubleshoot discrepancies by improving design or control approaches. You will also publish in high-impact journals and present at academic and industry conferences, contribute to technical reports, and collaborate with research staff, industry partners, and mentor juniors.

Expected Outcomes

The project aims to deliver enhanced design methodologies for electric motors improving torque-speed characteristics, efficiency, and reliability. Validated simulation models will offer more accurate predictions for EV motor performance. The research will generate publications and contribute to the adoption of improved motor technologies in electric vehicles.

Why This Matters

With the increasing shift towards electric vehicles, developing efficient and reliable electric motors is critical for sustainable transportation. This research addresses industry needs for high-performance motor designs validated through rigorous simulation and testing, supporting cleaner and more energy-efficient automotive solutions.

Entry Requirements

Master's degree in electrical engineering or a related field with strong background in electric machine design, electromagnetic analysis, FEM-based modeling, and powertrain systems. Knowledge of machine modeling in reference frames and control strategies for permanent magnet and/or reluctance machines. Demonstrated research experience with publications. Proficiency in simulation tools such as ANSYS, Motor-CAD, MATLAB/Simulink.

How to Apply

Send CV and academic transcripts to Dr. Narayan Kar at nkar@uwindsor.ca before 2026-05-10.

Eligibility

UK/Home
EU
International

Supervisor Profile

DN
Dr. Narayan Kar
University of Windsor
6672 Citations
39 h-index
Google Scholar

Dr. Narayan Kar is a researcher at the University of Windsor with extensive expertise in electric machine design and analysis, focusing on finite element modeling and validation of electric motors for EV applications. He has published over 350 papers and collaborates closely with industry for applied research. His work spans the development of data-driven models and experimental techniques to improve motor performance.

Key Publications

2026
Effect of severe plastic deformation via co-extrusion on microstructure and electrical conductivity of copper clad aluminum conductors for e-motor applications
2026
A Structured Survey of Anomaly Types and Classification-Based Detection Models in IoT
2026
Domain-Invariant Deep Learning Framework for Real-Time PMSM Rotor Temperature Prediction
Advanced data-driven machine learning models were developed for reliable real-time rotor temperature estimation in PMSMs.
2026
Dynamic Impedance-Aligned Phase Angle Compensation for Targeted Harmonic Mitigation in PMSM Drives
Introduced a control method to suppress critical harmonics in interior permanent-magnet synchronous motor drives to enhance performance.
2026
Utilization of Copper Clad Aluminum Hairpin Windings to Mitigate Eddy Current Losses
Explored copper-clad aluminum hairpin windings to reduce eddy current losses for improved traction e-motor electromagnetic performance.

Research Contributions

Development of advanced machine learning frameworks for real-time rotor temperature prediction in permanent magnet synchronous motors.
Improves reliability and efficiency of electric motors in automotive applications through enhanced thermal management.
Innovations in electric motor design, including use of copper clad aluminum conductors and mitigation of eddy current losses.
Enhances electromagnetic performance and efficiency in electric vehicle traction motors, supporting EV adoption.
Design and implementation of control strategies to mitigate harmonic distortions in PMSM drives.
Enables higher performance and reduced energy losses in electric motor drives used in electrified vehicles.

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