Study on the sound quality of the electric vehicle powertrain under acceleration conditions

Abstract

The evaluation and prediction of the sound quality (SQ) of electric vehicle (EV) powertrains are critical to the overall SQ of EVs. Firstly, a grouping method for noise samples is proposed to achieve rational grouping when SQ evaluations are performed using the grouped paired comparison method and its improvements. Secondly, aiming at the limitations of psychoacoustic parameters in predicting SQ under nonstationary conditions, a SQ prediction model based on the energy features of intrinsic mode functions (IMF) of signals is proposed. Finally, SQ evaluations are conducted, comparing the prediction performance of two SQ models based on energy features and psychoacoustic parameters. The prediction results show that the mean absolute percentage error (MAPE) of the model with energy features is 4.18%, while the MAPE of the model with psychoacoustic parameters is 8.88%, which demonstrates that energy features are superior in predicting the SQ of EV powertrains under acceleration conditions.