沈阳理工大学学报2024,Vol.43Issue(4) :79-85,91.DOI:10.3969/j.issn.1003-1251.2024.04.013

基于电机电磁噪声的电动汽车声品质MNLR预测方法

MNLR Prediction Method of Electric Vehicle Sound Quality Based on Motor Electromagnetic Noise

白镇熇 魏佳帅 冯胜涛 王钟缘 陈克 王楷焱
沈阳理工大学学报2024,Vol.43Issue(4) :79-85,91.DOI:10.3969/j.issn.1003-1251.2024.04.013

基于电机电磁噪声的电动汽车声品质MNLR预测方法

MNLR Prediction Method of Electric Vehicle Sound Quality Based on Motor Electromagnetic Noise

白镇熇 1魏佳帅 1冯胜涛 1王钟缘 2陈克 1王楷焱1
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作者信息

  • 1. 沈阳理工大学汽车与交通学院,沈阳 110159
  • 2. 潍柴雷沃重工股份有限公司,山东潍坊 261206
  • 折叠

摘要

为研究电动汽车电机电磁噪声对车内声品质的影响,建立声品质预测模型.采用等级评分法对车内噪声进行主观评价,作为预测模型因变量,计算总体噪声A计权声压级、粗糙度、AI指数等客观参量,作为预测模型自变量,此外通过阶次分析和滤波法得到电磁噪声并计算其尖锐度等客观参量,亦作为一组自变量,构建多元非线性回归(multivariate nonlinear regression,MNLR)模型.仿真实验结果表明,MNLR模型的预测结果与主观评价结果具有较高一致性,电磁噪声尖锐度提升会恶化车内噪声环境,同时还会增强AI指数对人主观感受的影响.本文预测模型对声品质优化具有一定的指导意义.

Abstract

To study the influence of electromagnetic noise of electric vehicle motor on the interior sound quality,a prediction model of sound quality was established.The objective parameters of the overall noise,such as SPL(A),roughness and Al index,were calculated as the independent varia-bles of the prediction model.In addition,the electromagnetic noise was obtained by order analysis and filtering method,and the objective parameters such as sharpness were calculated as a group of independent variables.A multivariate nonlinear regression(MNLR)model was constructed.The simulation results show that the prediction results of the MNLR model have high consistency with the subjective evaluation results.The increase of electromagnetic noise sharpness will worsen the noise environment inside the car,and at the same time will enhance the influence of AI index on people's subjective feelings.The prediction model is significant for the optimization of sound quality.

关键词

电动汽车/声品质/电磁噪声/主观评价/客观评价/多元非线性回归

Key words

electric vehicle/sound quality/electromagnetic noise/subjective evaluation/objective evaluation/multivariate nonlinear regression

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基金项目

辽宁省教育厅高等学校基本科研项目(LJKMZ20220603)

出版年

2024
沈阳理工大学学报
沈阳理工大学

沈阳理工大学学报

影响因子:0.223
ISSN:1003-1251
参考文献量13
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