1. Integration of Electric Aircraft into Canadian Aviation Systems: Operations, Infrastructure, and Optimization
This project investigates the integration of electric aircraft (e-planes) into Canadian aviation systems, with a focus on both flight operations and supporting energy infrastructure. As aviation contributes significantly to greenhouse gas emissions, electrification offers a promising pathway toward low-carbon, sustainable air transportation, particularly for pilot training, regional mobility, and short-haul routes. The project combines aviation analysis and electrical engineering approaches to address key challenges limiting the adoption of e-planes in Canada. On the aviation side, the research evaluates the performance, reliability, and operational feasibility of electric aircraft under diverse Canadian weather conditions, including extreme cold, which can significantly affect battery performance and flight endurance. Real-world flight data, collected from a certified electric aircraft (Pipistrel Velis Electro), will be used to assess operational constraints, training suitability, and route feasibility. On the infrastructure side, the project focuses on designing airport microgrids that integrate renewable energy sources and energy storage systems to support e-plane charging. These microgrids will be optimized to ensure cost-effective, low-emission, and reliable energy supply, while also contributing to overall airport energy demands. Advanced optimization and machine learning techniques will be applied to determine optimal charging schedules, battery usage, and infrastructure sizing. By integrating these two components, the project develops decision-support tools that enable efficient coordination between aircraft operations and energy systems. The outcomes will include validated models for e-plane deployment, optimized infrastructure designs, and recommendations for policy and industry adoption. Overall, this research supports Canada’s transition toward net-zero aviation, enhances the competitiveness of its aviation sector, and positions the country as a leader in electric and sustainable aviation technologies.
Research area, student roles & skills
Research area: My research focuses on the integration of emerging energy and electrification technologies into complex infrastructure systems, with an emphasis on sustainable aviation and power systems. It combines optimization, machine learning, and energy system modeling to design and operate electric aircraft, airport microgrids, and renewable-integrated energy networks, enabling low-carbon transportation and resilient infrastructure in the Canadian context.
Student roles:
The student will contribute to the development of analytical and optimization tools for integrating electric aircraft into Canadian aviation systems, with a focus on energy infrastructure and operational planning.
Specifically, the student will:
Develop and implement optimization models to support the scheduling of electric aircraft operations and charging strategies
Assist in designing and simulating airport microgrid systems, including the integration of renewable energy sources and battery storage
Analyze operational and energy data to evaluate system performance, efficiency, and reliability
Support the development of decision-support tools that coordinate aircraft operations with infrastructure constraints
Collaborate with interdisciplinary team members (aviation and engineering) to ensure realistic and practical solutions
Contribute to technical reports, presentations, and research publications
Through this role, the student will gain hands-on experience in applying optimization, power systems engineering, and data-driven methods to a real-world problem in sustainable aviation.
Skills required:
The ideal student should have a background in electrical engineering, energy systems, or a related discipline, with a strong interest in optimization and data-driven decision-making. Experience or coursework in power systems, renewable energy integration, or microgrid design is highly desirable.
The student should be familiar with optimization techniques (e.g., linear/nonlinear optimization, scheduling, or decision-support models) and have programming skills in tools such as Python, MATLAB, or similar platforms. Exposure to machine learning methods is considered an asset.
In addition to technical skills, the student should demonstrate enthusiasm for sustainable and electric aviation, and an interest in interdisciplinary research that connects