Constrained control of Homogeneous Charge Compression Ignition (HCCI) engines

Abstract

Homogeneous Charge Compression Ignition (HCCI) engines provide a possible solution for affordable, efficient and clean-burning power sources for either stationary power generator or advanced vehicles. In fact, HCCI engines integrate the advantages of both the spark ignition (SI) and compression ignition (CI) engines: (i) high fuel efficiency resulting from high compression ratio and rapid heat release and (ii) low NOx and low particulate matter (PM) emissions due to low cylinder peak temperature. Control of the HCCI engine, however, is difficult since its ignition cannot be directly actuated. The autoignition timing of HCCI combustion is determined by the cylinder charge conditions, rather than the spark timing or the fuel injection timing that are used to initiate combustion in SI and CI engines, respectively. Instead, controlled autoignition requires regulation of the charge properties, especially charge temperature, as demonstrated by many experimental results. Moreover, in order to realize the HCCI technology, several constraints in the system need to be considered. First, the combustion rate or cylinder pressure gradient should be constrained to avoid knocking. Since point-wise in time constraints in the combustion rate is needed constrained control will be necessary for the fuel and rebreathing lift commands. Second, the actuator constrains such as the limit range of the valve lift should also be explicitly take into account. For the purpose of model-based control development, we intend to first develop a HCCI engine model by extending a single cylinder HCCI engine model. The existence of a physics-based engine model, pointwise-in-time constraints and multiple actuators motivate the application of model-based optimization-control approaches such as Model Predictive Control (MPC). The model is used to predict the behavior of the system over a future horizon and an optimization-based methodology ensures optimal performance and satisfaction of constraints.