Statistical modeling and optimization for diesel engine calibration

The European standards concerning the pollutants emissions of automotive engine become more and more severe. Modern automotive engines are equipped with an increasing number of new technologies and controlling elements. The consequence of this evolution, is the increase of the number of the controllable parameters, the difficulty to understand the engine behavior, and to find the parameters settings that offer the best compromise across the entire engine map, especially between fuel consumption and emissions constraints. This paper deals with problem of engine calibration, using the minimum of experiments. The approach proposed consists in building a global emulator based on Kriging model, which was adapted to take into account a number of control parameters greater than 3, while existing software are limited to two control parameters. This model is used to predict an engine response, and is coupled with a genetic algorithm, in order to give a best setting of parameters, optimizing the fuel consumption within constraints on the emission of NO_¿ (nitrogen oxide). The main advantage of this approach is, its capacity to take into account a considerable number of controllable parameters in the optimization process, without lost in accuracy of model prediction.