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The design of cars is mainly based on the use of computational models to analyze structural vibrations and internal acoustic levels. Considering the very high complexity of such structural-acoustic systems, and in order to improve the robustness of such computational structural-acoustic models, both model uncertainties and data uncertainties must be taken into account. In this context, a probabilistic approach of uncertainties is implemented in an adapted computational structural-acoustic model. The two main problems are the experimental identification of the parameters controlling the uncertainty levels and the experimental validation. Relevant experiments have especially been developed for this research in order to constitute an experimental database devoted to structural vibrations and internal acoustic pressures. This database is used to perform the experimental identification of the probability model parameters and to validate the stochastic computational model.

We present a methodology to perform the identification and validation of complex uncertain dynamical systems using experimental data, for which uncertainties are taken into account by using the nonparametric probabilistic approach. Such a probabilistic model of uncertainties allows both model uncertainties and parameter uncertainties to be addressed by using only a small number of unknown identification parameters. Consequently, the optimization problem which has to be solved in order to identify the unknown identification parameters from experiments is feasible. Two formulations are proposed. The first one is the mean-square method for which a usual differentiable objective function and an unusual non-differentiable objective function are proposed. The second one is the maximum likelihood method coupling with a statistical reduction which leads us to a considerable improvement of the method. Three applications with experimental validations are presented in the area of structural vibrations and vibroacoustics.

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A comparison between transfer path analysis and operational path analysis methods using an electric vehicle is presented in this study. Structure-borne noise paths to the cabin from different engine and suspension points have been considered. To realise these methods, two types of test have been performed; operational tests on a rolling road and hammer tests in static conditions. The main aim of this work is assessing the critical paths which are transmitting the structure-borne vibrations from the electric vehicle's vibration sources to the driver's ear. This assessment includes the analysis of the noise contribution of each path depending on the frequency and vehicle speed range and moreover, the assessment of the path noise impact for harmonic orders which arise due to the physical components of the electric vehicle. Furthermore, the applicability of these methods to electric vehicles is assessed as these techniques have been extensively used for vehicles powered with internal combustion engines