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.

由于燃料电池汽车对散热的要求更高，因此采用350-V高压风扇代替传统的12-V冷却风扇，产生更大的气动噪声。安装的风扇不仅需要具有低声压级，还需要具有良好的心理声学性能。本文旨在解决主观音质评价结果与客观心理声学参数之间的复杂相关性，建立高压风机音质预测模型。燃料电池汽车上运行的两个高压风扇在不同运行条件下的噪声信号由人工头采集并预处理，得到七个客观参数。然后基于成对比较方法对噪声样本的烦扰度进行主观评价实验。选择了 23 名成年人的样本组，并为测试指导编写了图形用户界面。噪声信号的主观烦扰度得分是经过​​数据处理和有效性验证后得出的。通过对测试结果的分析，总结了主观评分与每个单一心理声学参数之间的相关性。分别通过多元线性回归和反向传播神经网络建立了两种音质预测模型，并对两种方法的训练结果进行了验证和比较，证明了神经网络训练结果的可靠性。有了既定的模型，