Modelling and intelligent prediction of sound quality for high-voltage fans on fuel cell vehicles. (May 2022)

• Record Type: Journal Article
• Authors: Rong, Guo; Mi, Tiantian; Ye, Shengwang; Jiang, Yanqing; Lv, Jiaming; Wang, Ziyi
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 236:Number 6(2022)
• Page Start: 1246
• Page End: 1258
• Publication Date: 2022-05
• Subjects: Sound quality -- high-voltage fan -- psychoacoustics prediction -- subjective evaluation experiment -- backpropagation neural network; Mechanical engineering -- Congresses; Transportation engineering -- Congresses; 629.2
• DOI: 10.1177/09544070211034313
• Languages: English
• ISSNs: 0954-4070

Abstract

Since fuel cell vehicles have much higher heat dissipation requirements, 350-V high-voltage fans are adopted instead of traditional 12-V cooling fans, generating more aerodynamic noise. The installed fans are required to possess not only low sound pressure level but also good psychoacoustic performance. This paper is aimed at solving the complex correlation between subjective sound quality evaluation results and objective psychoacoustics parameters and establishing a sound quality prediction model for high-voltage fans. The noise signals of two high-voltage fans operating on a fuel cell vehicle under different running conditions are collected by an artificial head and preprocessed to acquire seven objective parameters. Then the subjective evaluation experiment on the annoyance of the noise samples is carried out based on pair-wise comparison method. A sample group of 23 adults is selected and a graphical user interface is programmed for test guiding. The subjective annoyance scores of the noise signals are obtained after data processing and effectiveness verification. By analyzing the tested results, the correlations between the subjective score and each of the single psychoacoustic parameters are summarized. Two sound quality prediction models are established by multiple linear regression and backpropagation neural network respectively, and the training results of the two methods are verified and compared, proving the reliability of neural network training results. With the established models, the sound quality of the high-voltage fans can be estimated effectively without complex subjective tests, contributing to improving the acoustic performance of fan products.