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C. Hametner:
"Operating Regime Based Dynamic Engine Modelling";
Talk: Third International Workshop on Surrogate Modelling and Space Mapping For Engineering Optimization (SMSMEO 2012), Reykjavik, Iceland (invited); 2012-08-09 - 2012-08-11; in: "Proceedings of the Third International Workshop on Surrogate Modelling and Space Mapping For Engineering Optimization", (2012).

In the automotive industry shorter development cycles and a reduced number of prototypes lead to an increasing demand for efficient and reliable modelling tools for transient calibration purposes of combustion engines. The fundamental requirement for an integrated methodology for the complete model based calibration process is that the experiment on the testbed as well as the model structure are designed in a way that the model is able to cover all relevant effects. Such models will then allow to apply the parameter variations for dynamic calibration to a virtual plant instead of the real plant.
The present contribution describes a new approach for combustion engine model identification for transient calibration purposes. The proposed modelling concept is based on a split of the engine process into several local operating regimes, represented by the dominant influence of load and speed. This strategy allows to overcome the highly nonlinear dynamic complexity of the engine. In the second step each operating regime is identified by one dynamic local model network with a certain number of local linear models. Finally, the global nonlinear dynamic model is formed by weighted aggregation of the local dynamic nonlinear models. Thus, the underlying model structure combines physical knowledge and experimental models.
The split of the engine process using prior knowledge allows to build a model with locally different complexity. The main advantages of the operating regime model are its effectiveness when dealing with different noise levels or sensitivities in different operating regimes and the ability to dramatically reduce the complexity of the experiment design, especially when a multiplicity of constraints (e.g. in terms of engine protection) has to be taken into account. A second important factor is that operating regime models can handle the situation where some inputs are only active or available in a certain operational condition (in a certain operating area), where conventional strategies would result in ill-conditioned parameter estimates.
The proposed operating regime model with physical a priori information embedded in its structure provides excellent generalisation properties and the real-time application in an HiL environment has been realised in the course of this work. The benefits of the proposed concepts are demonstrated by means of real measurement data from a state-of-the-art diesel engine.