Research on noise source separation and sound quality prediction for electric powertrain

Abstract

The overall noise of the electric powertrain (EP) features the characteristics of high-frequency discreteness, abundant orders of harmonics, and serious howling. The consequent poor sound quality problems in the EP are frequently inevitable. In this paper, a complete set of sound quality evaluation system for EP noise signals is established, which closely combines the time-domain separation and sound quality evaluation. The EP noise source separation method in the time domain based on computational auditory scene analysis (CASA) and Robust-independent component analysis (ICA) is proposed to separate and identify EP independent noise sources. Five independent noise source signals in the time domain are then obtained, which could be played back for further psychoacoustic evaluation. The EP sound quality prediction method based on kernel principal component analysis (KPCA) and Lasso regression is proposed to develop the relationship between the subjective and objective quality for EP noise signals. Besides, the dimensionality of the objective evaluation parameters can be significantly reduced from nine to five. The experimental results indicate the superiority of the proposed prediction method to the traditional multiple linear regression approach on predicting the sound quality. A further deduction is made that the harmonic noise (HN) and switching noise (SN) can contribute more to sound quality of the EP overall noise and optimization on them can be a good choice.