Abstract

AbstractThe performance of model-based engine calibration is highly dependent on the type of the modelling. In this paper, a type of modelling which has not been used for engine calibration yet, the gaussian process model, is introduced and compared to other state of the art models. Starting from the requirements on the modelling, it can be shown from the theory that the gaussian process modelling is suitable for modelling stationary nonlinear engine mappings and has various advantages compared to other algorithms. Therefore, time and costs on the test-bed can be reduced if one is using gaussian process models for engine calibration. This theoretical result is validated by an application of an diesel engine, where NOx, consumption and soot are modeled.

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