主办:陕西省汽车工程学会
ISSN 1671-7988  CN 61-1394/TH
创刊:1976年

Automobile Applied Technology ›› 2023, Vol. 48 ›› Issue (14): 19-23.DOI: 10.16638/j.cnki.1671-7988.2023.014.004

• New Energy Vehicle • Previous Articles    

Satisfaction Degree Prediction of Electric Vehicle Sound Quality Based on Principal Component Regressione

CHEN Ke, WANG Zhongyuan*   

  1. School of Automobiles and Transportation, Shenyang Ligong University
  • Online:2023-07-30 Published:2023-07-30
  • Contact: WANG Zhongyuan

基于主成分回归的电动汽车声品质满意度预测

陈 克,王钟缘*   

  1. 沈阳理工大学 汽车与交通学院
  • 通讯作者: 王钟缘
  • 作者简介:陈克(1965-),男,博士,教授,研究方向为汽车系统动力学与控制、汽车现代设计与制造技术,E-mail: chenke@sylu.edu.cn。 通信作者:王钟缘(1997-),男,硕士研究生,研究方向为车辆动力学与控制,E-mail:wangzhongyuan617@163.com。

Abstract: In order to study the interior noise characteristics of electric vehicle and the evaluation method of sound quality satisfaction, the overall interior noise is scored subjectively by the grade scoring method, and the A-weighting sound pressure level, loudness, roughness, sharpness and language articulation index (AI) of the overall noise are calculated, while the sound pressure level (SPL), loudness and sharpness of the interior motor electromagnetic noise are calculated.The objective parameters of electromagnetic noise and overall noise are taken as the original variables together.Three principal components are constructed by dimensionality reduction method, and the principal component regression model is built with these as the independent variables. The results show that the mean absolute error and root mean square error of the principal component regression model are 4.3% and 11.6% lower than those of the multiple linear regression model, which can be used as an effective means to study the sound quality of electric vehicle.

Key words: Electric vehicle; Sound quality satisfaction degree; Electromagnetic noise; Subjective and objective evaluation; Principle component egression

摘要: 为研究电动汽车车内噪声特性及声品质满意度评价方法,采用等级评分法对车内总体 噪声进行主观评分,计算总体噪声 A 计权声压级、响度、粗糙度、尖锐度、语言清晰度指数 (AI),同时对车内电机电磁噪声的声压级(SPL)、响度、尖锐度进行计算,与总体噪声客 观参量共同作为原始变量,通过降维方式构建了 3 个主成分,并以此为自变量构建了主成分 回归模型。结果表明,主成分回归模型对比多元线性回归模型平均绝对误差降低了 4.3%,均 方根误差降低了 11.6%,可作为电动汽车声品质研究一种有效手段。

关键词: 电动汽车;声品质满意度;电磁噪声;主客观评价;主成分回归