| Phase: |
Theme |
| Theme: | Carbon CUS (T02) |
| Status: | Active |
| Start Date: | 2024-04-01 |
| End Date: | 2026-03-31 |
| Website: | |
| Principal Investigator |
| Koch, Charles Robert |
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 University of Alberta | Activity | 2024-05-30 | Koch, C. |
| Hydrogen-Diesel Dual Fuel Combustion Characterization for an Internal Combustion Engine | Publication | 2024-01-01 | Jakub Tyler McNally |
| 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., Koch, C., Shahbakhti, M. |
| Transfer of Reinforcement Learning-Based Powertrain Controllers From Model- to Hardware-in-The-Loop University of Alberta | Publication | 2025-01-01 | Mario Picerno, Lucas Koch, Kevin Badalian, Marius Wegener, Joschka Schaub, Koch, C., 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., Koch, C. |
