Model-Based Optimal Control for PCCI Combustion Engines

Abstract New combustion methods for engines have been recently researched very intensively. In diesel engines, the homogenisation of the air-fuel mixture by early fuel injection has significant effects on emission reduction. The paper presents a model-based optimal control strategy for premixed charge compression ignition (PCCI) low temperature combustion in diesel engines. In order to understand the basic properties of the PCCI mode, static and dynamic measurements were conducted using a real conventional diesel engine. The main inputs of the combustion process are the exhaust gas recirculation rate and injection parameters. Outputs are the indicated mean effective pressure and the fuel mass conversion balance point. The process has very fast, almost proportional dynamics over the engine's working cycles. Focusing on the static behaviour of the process, a nonlinear neural network model is used for identification. Successive linearisation of the nonlinear network is used as predictive controller model. The presented controller structure is able to consider constraints and can be computed very fast. Finally, the controller is validated under real time conditions by experimental tests at the engine test bench. Although the controller structure contains a model and a convex optimisation step with regards to constraints, its implementation is very simple, as no observer is used, and the linearised model consists of static gains only.