Identification of prominent noise components of an electric powertrain using a psychoacoustic model

Because of the electric power transmission system has no sound masking effect compared with the traditional internal combustion power transmission system, electric powertrain noise has become the prominent noise of electric vehicles, adversely affecting the sound quality of the vehicle interior. Because of the strong coupling of motor and transmission noise, it is difficult to separate and identify the compositions of the electric powertrain by experiments. A psychoacoustic model is used to separate and identify the noise sources of the electric powertrain of a vehicle, considering the masking effect of the human ear. The electric powertrain noise is tested in a semi-anechoic chamber and recorded by a high-precision noise sensor. The noise source compositions of the electric powertrain are analyzed by the computational auditory scene analysis and robust independent component analysis. Five independent noise sources are obtained, i.e., the fundamental frequency of the first gear mesh noise, fundamental frequency of the second gear mesh noise, double frequency of the second gear mesh noise, radial electromagnetic force noise and stator slot harmonic noise. The results provide a guide for the optimization of the sound quality of the electric powertrain and for the improvement of the sound quality of the vehicle interior.