Grant: Alliance Grants

The main objective of this project is to reduce emissions from the existing fleet of diesel powered vehicles though the integration of zero-carbon hydrogen fuel. This work looks at both hydrogen-diesel dual fuel (H2DF) combustion and 100% hydrogen combustion. This technology is proposed as a retro-fit solution which allows operators across Canada to immediately reduce their carbon footprint while allowing them to utilize their existing assets. This technology also enables hydrogen infrastructure to grow which is supported by the "Alberta Hydrogen Roadmap''. The addition of hydrogen allows for immediate reduction in Carbon Dioxide emissions, however, there remains challenges in commercializing the technology. These include high levels of hydrogen replacement of diesel, understanding the impact to the other engine emissions such as Nitrogen Oxide as well as the impact of the increased combustion temperatures on the long-term durability of the engines. Machine learning (ML) control and diagnostic methods can be used to quickly optimize energy systems such as combustion engines operating on zero-carbon fuels. ML-based models that are integrated into a model predictive controller (MPC) which is used to provide optimal control of the combustion process while considering process constraints. The project consortium is made of 5 Alberta-based project partners. Diesel Tech Industries (DTI) has been active in developing H2DF technology with commercial projects to convert existing fleets to H2DF operation. Two of these fleet operators are also project partners Diversified Transportation and the City of Edmonton both of which run bus fleets in Alberta and the fourth partner Velocity Truck is a transportation truck sales and service center. The proposed collaboration aims to leverage the extensive ML-based control and diagnostic experience at the University of Alberta into a commercial H2DF retrofit kit that will be sold to fleet operators across Canada to help transition away from carbon based energy carriers while also investigating 100% hydrogen combustion using ML-based control to help transition to carbon free transportation.