Stricter legal emission limits and increasing customer expectations lead to a growing number of controllable engine components and thus to a higher engine control complexity. For engine development, however, this means much greater time and effort is required to find the optimal combination of all selectable parameters. This trend can be observed in the field of Gasoline as well as for Diesel engines. At the same time, the development time from the first idea up to the introduction of a new production engine has become even shorter, and the costs have to be reduced. Since the number of measuring points required for complete operational-test measurements rises exponentially with the number of input variables, it is quite obvious that full factorial measurements are no longer possible. Therefore the method 'Design of Experiments' (DoE) is widely accepted as a suitable tool in the automotive sector and among its suppliers. In the meantime the term 'DoE'/'DoE-Process' covers often also the measurement procedure and the modeling. Likewise, this method is broadly applied in the IAV (author's note: IAV is a German provider of engineering services to the automotive industry) during the advanced development stage up to the production engine applications. Whereas DoE is used mainly in the area of steady-state applications recent research work shows a great potential also to optimize transient engine behavior. This paper will give an overview about the usage of statistical methods (mainly Design of Experiments) in the production engine calibration. 'Engine calibration' is the term for finding the optimal settings of the engine controller unit; optimal in terms of minimal emissions, minimal fuel consumption, good drivability and other brand specific goals.
Dynamic engine emission modeling has been attracting a lot of attention over the last years. Applications of dynamic engine modeling include model based calibration or rapid measurement, i.e. methods for saving measurement time.Whereas physical models usually show a high complexity, data driven models are estimated with significantly less effort. In this paper, we show the use of a multichannel sinusoidal excitation sequence for a nonlinear dynamic emission model. This training sequence is used for modeling transient emissions and exhaust temperature. As validation, a measured trace from a new European driving cycle and a FTP cycle is used.
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