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Transient emission peaks have become an important fraction of the total emissions during the standardized test cycles for passenger car Diesel engines. This paper is concerned with their reduction, in particular of nitric oxides (NOx) and particulate matter (PM) emissions, by online receding horizon optimal control. It is based on former works in which alternative target quantities for engine control were proposed, namely in-cylinder oxygen concentrations before (O2,BC) and after combustion (O2,AC). The actual work is concerned with testing an in-cylinder oxygen concentrations based control in simulation as well as by a real-time implementation on a turbocharged common rail passenger car production Diesel engine. The promising results confirm the choice of these concentrations as sensible control references and the feasibility of a real-time use in a model predictive control implementation.

As a physical description of the emissions of a specific engine is seldom possible, we present here a method to design an online dynamic estimator for PM and NOx based on data. The design method is based on a systematic search of function candidates performed using genetic programming after data have been pre-treated in an adequate fashion. While data and a simple data pretreatment prove enough for NOx, some basic physical understanding is necessary to preset the method and obtain the required precision in the case of PM. The method has been applied for raw emissions of a production DI diesel engine and shows a remarkable prediction performance.