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With electromobility, vehicles are becoming quieter due to the presence of electric motors that replace internal combustion engines. The interior cabin noise of electric vehicles is characterized by high-frequency components that can be annoying and unpleasant. Therefore, it is essential to analyse the NVH behaviour of e-powertrains early in the design-phase. However, this induces inherent uncertainties during the design process related to the operating conditions, geometrical parameters, measurement techniques, etc. that need to be quantified with fast and comprehensive stochastic models. In this work, we first present a deterministic framework to provide first-order estimations of the e-powertrain’s interior whining noises, combining both the airborne & structure-borne contribution with data-driven NVH transfers meta-models.

In this paper, a local method of structure-borne noise source characterization is presented. It is based on measurements of transverse displacement and local structural operator knowledge and allows to localize and quantify sources without any need of boundary condition information. To fix the instability caused by measurement noise, the regularization step inherent to inverse problem is realized with a probabilistic approach, within the Bayesian framework. When a priori distributions about noise and sources are considered as Gaussian, the Bayesian regularization is equivalent to the well-known Tikhonov regularization. The optimization of the regularization is then performed by the Gibbs Sampling (GS) algorithm, which is part of Markov Chain Monte Carlo (MCMC) techniques. The whole probability of the regularized solution is inferred, providing access to confidence intervals.