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Case study on noise identification of an electric vehicle using psychoacoustic metrics

 
: Guimaraes, G.P.; Schmidt, Matthias; Bartel, Torsten; Matthias, Michael

Desmet, W. ; Katholieke Universiteit Leuven, Departement Werktuigkunde:
ISMA 2020, International Conference on Noise and Vibration Engineering. Proceedings : In conjunction with the 8th International Conference on Uncertainty in Structural Dynamics (USD2020), 7 to 9 September 2020, CD-ROM, virtually
Leuven: KU Leuven, 2020
ISBN: 978-90-828931-1-3
S.243-244
International Conference on Noise and Vibration Engineering <29, 2020, Online>
International Conference on Uncertainty in Structural Dynamics (USD) <8, 2020, Online>
Englisch
Konferenzbeitrag
Fraunhofer LBF ()
case study; Noise Identification; electric vehicle

Abstract
The electrification of vehicles brings new challenges to noise control, as higher frequency components and a lack of masking noise (originally from the combustion engine). This work focus on the experimental identification of an electric vehicle (BMWi3) noise and on a perception analysis. The measurements are made during a run-up manoeuvre and a psychoacoustic analysis (based on loudness and sharpness) is performed afterwards. As results, strong tonal high-frequency noise related to the electrical motor orders and harmonic components (fan effect) from the power electronics, not only around the switching frequency but also around its multiples. By extracting (bandpass filter) and filtering (removing) orders and bands, it is possible to rank the components regarding its annoyance. Such results can contribute to the development of packaging of the noise sources, based on new materials (as metamaterials) and active solutions (as smart structures), as well as a combination of both (active metamaterials).

: http://publica.fraunhofer.de/dokumente/N-615489.html