Influence of Model Detail Level on the Prognosis Quality of the Perceived Sound of an Electrified Passenger Car Powertrain

Abstract

Complex and resource intensive multi-physical models which integrate the domains of electrotechnical, structural dynamics and acoustics are needed to predict the noise, vibration and harshness (NVH) behavior of an electric passenger car drive train. Because of the highly limited resources in product development process of cars, engineers request efficient models with a good balance between accuracy and model creation as well as calculation effort. Knowing about which components and effects influence the prognosis is essential to adjust this balance between accuracy and effort. Therefore, in this paper a method is presented to determine the influence of the model fidelity level on the prognosis quality of the perceived drive train-related airborne sound under the use of hearing tests to consider the human noise perception. The work is based on a high fidelity multi-physical NVH of a small, series-produced electric vehicle drivetrain which is validated in structure-and air-borne-noise against test bench measurements. The influence of model detail level on the prognosis quality is analyzed using the aforementioned method taking into account four exemplary variant calculations of the proposed model: (1) Power electronics harmonics vs. ideal sine electric machine current, (2) electric machine with vs. without production deviations, (3) modal damping parameters derived from component measurements vs. modal damping taken from literature, and (4) numeric vs. analytical sound calculation method.

A literature review [2000–2022] on vehicle acoustics: Investigations on perceptual parameters of interior soundscapes in electrified vehicles

“…Moreover, psychoacoustic models and their ability to predict changes in perceived MOTC are referenced (Doleschal et al, 2021). Drichel et al (2021) suggested an efficient prediction model for EV-NVH behavior and investigated the influence of its model fidelity level on the predictive quality of the perceived drivetrain-related airborne noise while considering human perception. The model development itself included the analyses of the psychoacoustic metrics loudness, sharpness, tonality, roughness, and psychoacoustic annoyance, which led Drichel et al (2021) to the following observations: additional sidebands and friction-related excitations made the stimuli sound less tonal; more broad-band noise led to higher 1 Please note that these findings are conducted from the article's English abstract only, whereas the article of Fang et al (2015) itself was written in Chinese and not available to us in English language in an authorized version.…”

“…Frontiers in Mechanical Engineering frontiersin.org roughness; noise components in higher frequency ranges led to higher sharpness scores as the simulation might have underestimated the SPL in these ranges. For the validation of the model N = 30 participants were asked to evaluate multiple noise stimuli concerning a reference in regards to their similarity (from 0 = completely dissimilar to 100 = absolutely identical) in a MUSHRA test (Multi-Stimulus Test with Hidden Reference and Anchor) (Drichel et al, 2021). Table 1 gives an overview of the summarized literature assigned to this cluster and its analyzed parameters: a total of 16 articles, seven of them also including a comparison with subjective data.…”