Machine Learning based Combustion Control for Zero Carbon fuels

The Canadian Net-Zero Emissions Accountability Act targets net-zero greenhouse gas (GHG) emissions by 2050 with similar commitments around the globe. In the short-term, emissions from heavy-duty internal combustion engines (ICEs) that dominate the power generation in freight transportation industry can either be reduced or eliminated with zero-carbon fuels such as Hydrogen / Ammonia. One solution is the implementation of advanced combustion and optimal control strategies for the best performance and lifespan of the ICE. Model predictive control (MPC) is one of the most promising control strategies for handling these highly constrained nonlinear systems. The research will focus on integrating machine learning (ML) for the model and controller to discover state of the art control methods to optimize energy conversion in
mobile applications. The student will have the opportunity to gain experience in machine learning, MPC and experimental engine testing during their stay at the University of Alberta.

Faculty Supervisor:

David Gordon

Student:

Partner:

Rheinisch-Westfälische Technische Hochschule Aachen

Discipline:

Engineering

Sector:

Education

University:

University of Alberta

Program: