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Machine Learning Control technology applied to decarbonizing H2 fueled vehicles (T02-A04)

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Machine Learning Control technology applied to decarbonizing H2 fueled vehicles (T02-A04)

Phase:	Theme
Theme:	Carbon CUS (T02)
StatusStatut:	Active
Start DateDate de début:	2024-04-01
End DateDate de fin:	2026-03-31
WebsiteSite web:	https://sites.ualberta.ca/~ckoch/

Aperçu de Project Overview

The overall goal of this project is to improve the efficiency/emissions of H2/Diesel and H2 Hybrid systems to quickly reduce CO2 and pollutants from heavy duty long haul trucks and other heavy duty vehicles. Some of the expected outcomes are to provide robust and accurate Machine Learning Control (MLC) of an H2/Diesel engine and to also develop MLC control techniques and methods applicable to H2 fueled vehicles. In collaboration with our industrial partner, we aim to answer the question of how much Machine Learning Control (MLC) can help improve the efficiency of H2/Diesel and 100% H2 fueled transportation.

Outputs

Title	Category	Date	Authors
Alberta Innovates AwardAlberta Innovates Scholarship	Award	2024-05-30	Hossein Mehnatkesh Ghadikolaei
Premier Danielle Smith in Engine Lab	Activity	2024-05-30	Charles Robert Koch
Hydrogen-Diesel Dual Fuel Combustion Characterization for an Internal Combustion Engine	Publication	2024-01-01	Jakub Tyler McNally
Dynamic emission analysis of a hydrogen/diesel dual-fuel engine using clustering method University of Alberta	Publication	2025-07-01	Hossein Mehnatkesh, Gordon, D., Charles Robert Koch
Machine learning-based model predictive control for balancing of combustion in hydrogen/diesel dual-fuel engine University of Alberta	Publication	2026-01-01	Hossein Mehnatkesh, Javad Kheyrollahi, Gordon, D., Charles Robert Koch
Physics-Informed Neural Networks for In-Cylinder Pressure Prediction in Hydrogen/Diesel Dual-Fuel Engines University of Alberta	Publication	2025-01-01	Hossein Mehnatkesh, Gordon, D., Charles Robert Koch
Real-time vehicular fuel consumption estimation using machine learning and on-board diagnostics data University of Alberta	Publication	2023-01-01	Hamidreza Abediasl, Amir Ansari, Hosseini, V., Charles Robert Koch, Shahbakhti, M.
Systematic framework for deep learning-based predictive injection control with Bayesian hyperparameter optimization for a hydrogen/diesel dual-fuel engine University of Alberta	Publication	2025-11-01	Hossein Mehnatkesh, Alexander Winkler, Edward Sperling, Javad Kheyrollahi, Shahbakhti, M., Gordon, D., Charles Robert Koch
Tailpipe NOx Emissions Modeling of a Heavy-Duty Diesel Truck Using Deep Learning Methods University of Alberta	Publication	2025-01-01	Saeid Shahpouri, Luo Jiang, Charles Robert Koch, Shahbakhti, M.
Transfer of Reinforcement Learning-Based Powertrain Controllers From Model- to Hardware-in-The-Loop	Publication	2025-01-01	Mario Picerno, Lucas Koch, Kevin Badalian, Marius Wegener, Joschka Schaub, Charles Robert Koch, Jakob Andert
Transient NOx emission modeling of a hydrogen-diesel engine using hybrid machine learning methods University of Alberta	Publication	2024-01-01	Saeid Shahpouri, Gordon, D., Shahbakhti, M., Charles Robert Koch

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