Development of high-performance solid oxide fuel cell stack with low degradation rate and fault tolerance for commercial applications (T06-A02)

Phase:	Theme
Theme:	Grids and Storage (T06)
Status:	Active
Start Date:	2023-04-01
End Date:	2026-03-31
Principal Investigator
Shahbakhti, Mahdi

Project Overview

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.

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Title	Category	Date	Authors
Transfer learning-based deep neural network model for performance prediction of hydrogen-fueled solid oxide fuel cells University of Alberta	Publication	2025-01-01	Zeynab Salehi, Mohamadali Tofigh, "Ali Kharazmi", "Daniel Smith", Amir Hanifi, Koch, C., Shahbakhti, M.
Performance Prediction of a Range of Diverse Solid Oxide Fuel Cells using Deep Learning and Principal Component Analysis University of Alberta	Publication	2024-10-30	Zeynab Salehi, Mohamadali Tofigh, Sajad Vafaeenezhad, Koch, C., Shahbakhti, M.
Control-oriented Modeling of a Solid Oxide Fuel Cell Affected by Redox Cycling using a Novel Deep Learning Approach University of Alberta	Publication	2024-10-27	Mohamadali Tofigh, Masood Fakouri Hasanabadi, Amir Hanifi, Koch, C., Shahbakhti, M.
Temporal Dilated Convolution and Nonlinear Autoregressive Network for Predicting Solid Oxide Fuel Cell Performance University of Alberta	Publication	2024-10-01	Mohamadali Tofigh, "Ali Kharazmi", "Daniel Smith", Amir Hanifi, Koch, C.
Developing a Time-Efficient Model for Solid Oxide Fuel Cells Using Self-Supervised Convolutional Autoencoder and Stateful LSTM Network	Publication	2024-07-08	Mohamadali Tofigh, Zeynab Salehi, Amir Hanifi, Koch, C., Shahbakhti, M.
Thermodynamic and Economic Analysis of a Novel Concept for Methane Pyrolysis in Molten Salt Combined with Heliostat Solar Field	Publication	2024-06-01	Amir Hanifi, Shahbakhti, M.
2024 Canadian Hydrogen Convention	Activity	2024-04-23	Shahbakhti, M., Amir Hanifi
Transient Modeling of a Solid Oxide Fuel Cell using an Efficient Deep Learning HY-CNN-NARX Paradigm	Publication	2024-04-14	Mohamadali Tofigh, Zeynab Salehi, Amir Hanifi, Koch, C., Shahbakhti, M.
SOFC Database	Publication	2024-03-01	Zeynab Salehi, Mohamadali Tofigh, Masood Fakouri Hasanabadi, Amir Hanifi, Shahbakhti, M., Koch, C.
SOFC applications as combined Heat and Power Units for Canada	Activity	2024-03-01	Amir Hanifi

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Development of high-performance solid oxide fuel cell stack with low degradation rate and fault tolerance for commercial applications (T06-A02)

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Development of high-performance solid oxide fuel cell stack with low degradation rate and fault tolerance for commercial applications (T06-A02)

Project Overview

Outputs