Abstract:

In view of that the sound quality of vehicle door slamming noise directly affects the purchase intention of customer， the predictive models for the door slamming sound quality of an electric vehicle are studied in this paper. Firstly， a multi-condition door slamming test is carried out with several groups of noise samples near driver’s ear collected. Then， six objective sound quality evaluation indicators are proposed and measured， meanwhile the subjective evaluation on the degree of annoyance is conducted， with the correlation between objective sound quality evaluation indicators and subjective annoyance evaluation analyzed. Next， a subjective sound quality predictive model is created by using GA-BP neural network on one hand， while a multiple linear regression predictive model， reflecting the relationship between subjective annoyance and objective sound quality indicators is established based on the correlation analysis mentioned above on the other hand. Finally， by utilizing five random noise samples， a comparative verification on two predictive models is performed. The results show that the prediction accuracy of GA-BP neural network is higher than that of multiple linear regression model.

Key words: electric vehicles, door slamming noise, sound quality evaluation, predictive models