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- W2163047117 abstract "Reducing the number of tests on vehicles is one of the most important requirementsfor increasing cost efficiency in the calibration process of engine control units (ECU).Here, employing virtual vehicles for a model-based calibration of ECUs is essential.Modelling components for virtual vehicles can be a tedious and time-consuming task.In this context, data-based modelling techniques can be an attractive alternative tophysical models to increase efficiency in the modelling process. Data-based models canincorporate unknown nonlinearities encoded in the sampled data, resulting in more accuratemodels in practice. In combination with automated measurement, data-basedmodelling can help to significantly accelerate the calibration process. Furthermore,the fast simulation speed of the resulting models allows their implementation intoreal-time simulation environments, such as Hardware-in-the-Loop (HiL) systems, andthus enables a model-based calibration of the related ECU software function. However,generating appropriate data for learning dynamic models, i.e., the transient Design ofExperiments (DoE), is not straightforward, since system boundaries and permissibleexcitation frequencies are not known beforehand. Thus the training data of thesystem measurement will be inconsistent and the main challenge of the identificationprocess is to deal with this data to achieve a globally valid model. Furthermore, whendealing with dynamic systems in an automotive context, the Engine Control Unittypically changes operating modes while driving. Thus nonlinearities and changes ofphysical structures appear, which need to be considered in the model. In this thesis,a modelling system called the Local Gaussian Process Regression (LGPR), is usedand adapted in order to receive a flexible modelling approach, which allows an iterativemodelling process and obtains robust and globally valid dynamic models. Theadapted LGPR approach is employed for the ECU calibration of dynamical automotivesystems, which is critical regarding system excitation. Using LGPR, it is possibleto measure the system iteratively while exploring the relevant state-space regions andimproving the quality of the model step by step. The results show that LGPR isbeneficial for iterative modelling of dynamical systems. Compared to the traditional Gaussian Process Regression (GPR) modelling approach, LGPR yields better resultsregarding the variable system dynamics." @default.
- W2163047117 created "2016-06-24" @default.
- W2163047117 creator A5009210830 @default.
- W2163047117 date "2015-02-06" @default.
- W2163047117 modified "2023-09-27" @default.
- W2163047117 title "Model-based Calibration of Engine Control UnitsUsing Gaussian Process Regression" @default.
- W2163047117 hasPublicationYear "2015" @default.
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