Reduced order model with non-linear parameter optimization for hydrogen combustion engine control*

Using hydrogen as a fuel in spark ignition (SI) internal combustion engines offers an environmentally friendly alternative to fossil fuels, while retaining many benefits of conventional powertrains. Even though the hydrogen SI engine can rely on mature mechanical parts from conventional gasoline engines, the control software has to be adapted to the change in fuel. This includes a rapid switching between lean and stochiometric operation while ensuring engine protection and very low pollutant emissions. A model predictive controller (MPC) offers the possibility to systematically consider opposing control objectives and constraints for transient operation while reducing the development and calibration effort. As a basis for the real-time capable MPC, a reduced order physical model with empirical components is developed to describe the processes in the combustion chamber. Taking single cylinder engine testbench measurements into account from early stages of the engine development, some of the model’s parameters are optimized to increase prediction accuracy. The resulting non-linear parameter optimization reduces the mean square errors of the main outputs relative air-fuel-ratio and indicated mean effective pressure from 0.1475 and 0.5426 bar² to 0.0218 and 0.1020 bar² within a wide operating area.