The integration of customer demands in the early phase of product development process is one of most important aspects in the field of automotive engineering. In addition to a customer survey which generally requires drive tests of the real prototypes, methods based on the virtual product design have been applied more and more frequently. Due to the potential of simulation methods, the development time can be shortened and the costly prototypes as well as the time-consuming drive tests can be partially excluded. Earlier studies have demonstrated a capability of the developed methods and tools to support the customer-oriented drive train design by means of the prediction of the subjective comfort evaluation. In this case, the potential customers are classified into three groups based on their comfort expectation and style of driving. The rating from the customer point of view is accordingly achieved by modeling of the human sensation from the way the individual passengers make their evaluation by means of the Artificial Neural Networks (ANN). The objective of the current research is to implement the developed methods in the design phase of the drive train development process to enhance the customer comfortability. This article presents a systematic approach to apply the simulation methods in order to investigate different design parameters of the drive train and to determine the consequent customer comfort evaluation during each driving situation, the vehicle start-up as an example. For this purpose, the modification of the elaborated vehicle model is carried out by variation of the comfort-relevant design parameters, such as the friction coefficient gradient of the clutch friction pair, the mass of inertia and the damping of the components, like the dual mass flywheel. Depending on each drive train configuration and driver demand on the vehicle start-up, the simulated driving situation with different effects on the occurrence of the rotary vibration is evaluated by means of the human sensation model. This is developed during the drive tests on the basis of driver rating behavior. Based on the predicted comfort evaluations from different types of customer, the decisions made by the developer such as the determination of the clutch disk property or the damping setting of component can be efficiently supported during the drive train design. Hence, a new drive train concept can be tested and improved in such a way that the satisfaction of a target customer group from the first prototypes is obtained.

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