This project, led by the University of Alberta and Cummins Inc. develops the first demonstration-scale 0.5 kW solid oxide fuel cell (SOFC) stack as a combined heat and power (CHP) unit for residential applications in Canada. Such CHPs at the scale of 5 kW are meant to deliver clean heat and electricity to Canadian households. Currently, the power degradation rate of commercial 1 to 5-kW SOFC stacks is about 1% per 1000 hours, exceeding the required 0.2% degradation rate to make SOFCs a commercially viable technology. The objective of this project is to reduce the power degradation rate of the demo scale CHP to below 0.2% per 1000h and increase its fuel conversion efficiency to higher than 90%. This target will be achieved by developing large planar fuel cells using novel cathode materials developed at UAlberta, optimizing the stack design using computational fluid dynamics (CFD), and controlling the operation of the stack using innovative artificial intelligence and machine learning techniques.
SOFC applications as combined Heat and Power Units for Canada
Government Briefings
Transient Modeling of a Solid Oxide Fuel Cell using an Efficient Deep Learning HY-CNN-NARX Paradigm
Peer-Reviewed Journal Article
2024 Canadian Hydrogen Convention
Conference/Symposium/Workshop Contribution
CFD modeling and analysis of anode supported solid oxide fuel cells
Master Thesis
Canadian Hydrogen Convention
Conference/Symposium/Workshop Contribution
Control-oriented Modeling of a Solid Oxide Fuel Cell Affected by Redox Cycling using a Novel Deep Learning Approach
Conference Proceedings
Design, thermodynamic, and economic analyses of a green hydrogen storage concept based on solid oxide electrolyzer/fuel cells and heliostat solar field
Peer-Reviewed Journal Article
Developing a Time-Efficient Model for Solid Oxide Fuel Cells Using Self-Supervised Convolutional Autoencoder and Stateful LSTM Network
Conference Proceedings
Invention disclosure: SOFC diagnostics and control with use of customized power electronics
Misc: Invention disclosure
Modeling and microstructural study of anode-supported solid oxide fuel cells: Experimental and thermodynamic analyses
Peer-Reviewed Journal Article
Performance Prediction of a Range of Diverse Solid Oxide Fuel Cells using Deep Learning and Principal Component Analysis
Conference Proceedings
SOFC Database
Database
Temporal Dilated Convolution and Nonlinear Autoregressive Network for Predicting Solid Oxide Fuel Cell Performance
Peer-Reviewed Journal Article
Thermodynamic and Economic Analysis of a Novel Concept for Methane Pyrolysis in Molten Salt Combined with Heliostat Solar Field
Peer-Reviewed Journal Article
Transfer learning-based deep neural network model for performance prediction of hydrogen-fueled solid oxide fuel cells
Peer-Reviewed Journal Article