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Hybrid Physico-Data Framework for Predictive Modeling of PEM Fuel Cells
This project aims to develop computationally efficient and reliable computer models to help design longer-lasting hydrogen fuel cells. We combine our physical understanding of how these devices work with modern artificial intelligence tools to predict performance losses and lifetime under realistic operating conditions. During the internship, I will work with Prof. Marc Secanell’s Energy Systems Design Laboratory at the University of Alberta and my home institute at Forschungszentrum Jülich/RWTH Aachen to link their advanced electrochemical models with new machine-learning approaches. The collaboration will create practical modelling tools that can be used by researchers and industry partners, strengthen long-term cooperation between Canada and Germany in clean energy, and provide specialized training at the interface of engineering, materials science, and AI.
Faculty Supervisor:
Marc Secanell Gallart
Student:
Partner:
Rheinisch-Westfälische Technische Hochschule Aachen
Discipline:
Engineering
Sector:
Education
University:
University of Alberta
Program:
Globalink Research Award
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Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.