Human ride comfort prediction of drive train using modeling method based on artificial neural networks

Abstract

This article demontrates a systematic approach for human ride comfort objectification. The main objective is to integrate the prediction of the subjective comfort evaluation into the concept phase of product development process. By means of the human sensation model based on the Artificial Neural Networks (ANN), the subjectively sensed convenience of each passenger is estimated. In this paper, two examples of the implementation of the proposed methods are discussed. The first example represents an investigation of comfort-relevant design parameters of the drive train, such as the friction coefficient gradient of the clutch friction pair, the mass of inertia and the damping of the dual mass flywheel. The consequent vibrational properties and the subjective assessments during the start-up situation are predicted. The second example considers the determination of gear rattle tendency of a 5-speed manual transmission. To predict the gear rattle presence and to evaluate the resulted annoying level, the ANN-based models representing the NVH experts are elaborated. As a result, a good correlation of the subjective ratings and the predicted evaluations is attained. Consequently, the proposed method can be effectively applied to compare different gearbox solutions for new product concept. Hence, the development time and costs might be significantly reduced.