Control and machine learning for internal combustion engines and their exhaust aftertreatment systems

点击翻译按钮获取中文摘要

The short to medium term objective of this research program is to develop methods to reduce CO2 and other harmful emissions from internal combustion engines using carbon free or renewable fuels with the focus on heavy duty engines. Canada and many other countries, plan to reduce greenhouse gas emissions to zero by 2050. This is causing tremendous changes and innovation in the transportation sector. Heavy Duty Class 8 trucks in North America move 60% freight ton-km and produce over 75% of the CO2 emissions of road freight. One promising way to reduce CO2 emissions in these transport trucks is to decrease fuel usage by maximizing the thermal efficiency and using renewable fuels or zero carbon fuels such as hydrogen (H2). The impact of this work is to quickly reduce CO2 emissions from these trucks in Canada. For example, a H2/Diesel dual fuel 40:60% (energy split) in heavy duty freight trucks reduces the life cycle CO2 32% when using blue H2 for trucks in Alberta with a projected reduction of 1.5 megatonnes of CO2/year by 2039. Current Internal Combustion Engine calibration methods have become so complex there is strong industry demand to embed knowledge and constraints of the system in a model based control. Innovative engine concepts such as H2 or H2/Diesel dual fuel, which can quickly reduce CO2, are being delayed into the market due to the complexity and cost of current control/calibration methods to meet Real Driving Emission (RDE) requirements. The focus of this proposal will be the development of methods and their application of Machine Learning control to H2/Diesel dual fuel, 100% H2 fuel and other renewable fuels in heavy duty freight trucks to quickly reduce CO2. A detailed physical understanding of the combustion and exhaust aftertreatment system including the sensors/actuators is needed. This is incorporated into a simulation model suitable for model based control. For example, to decrease CO2 in freight trucks using Diesel/H2 or 100% H2 requires using an understanding of knock, preignition, NOx and particulates emissions to develop control strategies. The long term objective is that the proposed research will lead to ground breaking advances in (1) In-cycle control for engine combustion enabling advanced combustion; (2) new methods of combining Machine Learning with Model Predictive Control for complex constrained engineering systems; (3) Machine Learning-Control for robust and easy to calibrate engine control that minimizes fuel and emissions using carbon neutral fuels; and (4) training of highly qualified people to work in the H2 economy. This will have a long term impact to allow Canada to remain competitive in complex engineering technologies, such as transportation, that require control for optimal performance.