Evaluation of Objective Sound Quality Feature Extraction with Kernel Principal Component Method in Electric Drive System

This paper takes the electric drive system used in the electric vehicle as the research object, in which the objective sound quality of noise samples is extracted and evaluated based on the kernel principal component (KPCA) analysis method. Seven different power-level prototypes and their related parameters are firstly presented, while the sample library under different operational conditions has been established. Secondly, the KPCA method is employed to extract the contributions of eight objective psychological features. The results show that the KPCA method can effectively achieve multi-dimensional feature extraction. The cumulative contribution of sharpness and tonality is meeting 98.18%, which can fully represent the objective sound quality. Moreover, the sharpness and tonality are more sensitive to the speeds under different load conditions. Especially, tonality obtains a different pattern with SPL-A above 10000 r/min. This work can provide a theoretical and practical basis for predicting and optimizing the objective and subjective sound quality in electric vehicle applications.