电动汽车电驱动系统加速工况声品质评价及预测

杜进辅, 杨攀, 曲南飞

振动与冲击 ›› 2024, Vol. 43 ›› Issue (22) : 126-134.

PDF(2157 KB)
PDF(2157 KB)
振动与冲击 ›› 2024, Vol. 43 ›› Issue (22) : 126-134.
论文

电动汽车电驱动系统加速工况声品质评价及预测

  • 杜进辅*,杨攀,曲南飞
作者信息 +

Evaluation and prediction of the sound quality of the electric powertrain for electric vehicles under acceleration conditions

  • DU Jinfu*,YANG Pan,QU Nanfei
Author information +
文章历史 +

摘要

电动汽车电驱动系统高频加速噪声严重影响整车声品质。为此,通过电驱动系统振动噪声试验,采集多工况加速噪声信号,并进行主、客观评价。结合相关性分析以心理声学参数为输入,通过改进的灰狼算法(Improved Gray Wolf Optimizer,IGWO)优化支持向量回归(Support Vector Regression,SVR),建立IGWO-SVR模型用于电驱动系统声品质预测。引入互补集合经验模态分解(Complementary Ensemble Empirical Mode Decomposition,CEEMD)与信号的均方根值(Root Mean Square,RMS),提取电驱动系统加速噪声的CEEMD-RMS特征,并建立以CEEMD-RMS为输入IGWO-SVR声品质预测模型。检验结果表明:以CEEMD-RMS特征为输入的声品质预测模型,预测效果较以心理声学参数为输入的IGWO-SVR模型更优,测试集平均相对误差由8.88%减小为4.18%。

Abstract

The high-frequency acceleration noise of electric powertrains for electric vehicles seriously affects the sound quality of the whole vehicle. In this regard, multi-operating-condition acceleration noise signals are collected through vibration and noise tests of the electric powertrain. These signals were subsequently subjectively and objectively evaluated for sound quality. After reference correlation analysis, the improved gray wolf optimizer-support vector regression (IGWO-SVR) model was developed for sound quality prediction of electric powertrains using psychoacoustic parameters as inputs and optimized support vector regression (SVR) using the improved gray wolf optimizer (IGWO). Complementary ensemble empirical mode decomposition (CEEMD) and root mean square (RMS) of the signal are introduced to extract the CEEMD-RMS features of the acceleration noise of the electric powertrain and to establish the IGWO-SVR sound quality prediction model with CEEMD-RMS as input. The test results show that the sound quality prediction model with CEEMD-RMS features as inputs predicts better than the IGWO-SVR model with psychoacoustic parameters as inputs, and the mean relative error of the test set is reduced from 8.88% to 4.18%.

关键词

电驱动系统 / 加速工况 / 声品质 / 互补集合经验模态分解 / 预测模型

Key words

electric powertrain / acceleration condition / sound quality / complementary ensemble empirical mode decomposition (CEEMD) / predictive model

引用本文

导出引用
杜进辅, 杨攀, 曲南飞. 电动汽车电驱动系统加速工况声品质评价及预测[J]. 振动与冲击, 2024, 43(22): 126-134
DU Jinfu, YANG Pan, QU Nanfei. Evaluation and prediction of the sound quality of the electric powertrain for electric vehicles under acceleration conditions[J]. Journal of Vibration and Shock, 2024, 43(22): 126-134

参考文献

[1] WANG Y, ZHANG S, MENG D, et al. Nonlinear overall annoyance level modeling and interior sound quality prediction for pure electric vehicle with extreme gradient boosting algorithm [J]. Applied Acoustics, 2022, 195.
[2] HUANG H B, HUANG X R, DING W P, et al. Uncertainty optimization of pure electric vehicle interior tire/road noise comfort based on data-driven [J]. Mechanical Systems and Signal Processing, 2022, 165.
[3] HUANG B H, HUANG R X, WU H J, et al. Novel method for identifying and diagnosing electric vehicle shock absorber squeak noise based on a DNN [J].Mechanical Systems and Signal Processing, 2019, 124: 439-458.
[4] 许雪莹, 韩国华, 吴瑛, 等. 电动汽车的噪声特点及评价方法[J]. 汽车工程学报, 2011, 1(z1): 17-20.
XU Xue-ying, HAN Guo-hua, WU Ying, et al. Sound character and evaluation method of electric vehicle [J]. Chinese Journal of Automotive Engineering, 2011, 1(z1): 17-20.
[5] QIAN K, HOU Z. Intelligent evaluation of the interior sound quality of electric vehicles [J]. Applied Acoustics, 2021, 173.
[6] Huang H B, Wu J H, Lim C T, et al. Pure electric vehicle nonstationary interior sound quality prediction based on deep CNNs with an adaptable learning rate tree [J]. Mechanical Systems and Signal Processing, 2021, 148.
[7] 黄宇. 纯电动汽车车内噪声品质评价研究[D]. 天津: 天津大学, 2019.
HUANG Yu. Research on the Evaluation of interior sound quality of pure electric vehicle [D]. Tianjin: Tianjin University, 2019.
[8] 王海文. 永磁同步电机声品质主观评价研究[D]. 武汉: 武汉科技大学, 2021.
WANG Hai-wen. Subjective evaluation of sound quality of permanent magnet synchronous motor [D]. Wuhan: Wuhan University of Science and Technology, 2021.
[9] 郭栋, 代文翔, 刘骄, 等. 变速器敲击噪声心理声学评价研究[J]. 噪声与振动控制, 2019, 39(01): 67-72+115.
GUO Dong, DAI Wen-xiang, LIU Jiao, et al. Research on evaluation of transmission gear rattle based on psychoacoustics [J]. Nosie and Vibration Control, 2019, 39(01): 67-72+115.
[10] Fang Y, Zhang T. Sound quality investigation and improvement of an electric powertrain for electric vehicles [J]. IEEE Transactions on Industrial Electronics, 2018, 65(2): 1149-1157.
[11] Fang Y, Chen H, Zhang T. Contribution of acoustic harmonics to sound quality of pure electric powertrains [J]. IET Electric Power Applications, 2018, 12(6): 808-814.
[12] 曹志强. 电驱动总成NVH性能分析与优化[D]. 重庆: 重庆理工大学, 2020.
CAO Zhi-qiang. Analysis and optimization of NVH performance of electric power train [D]. Chongqing: Chongqing University of Technology, 2020
[13] 刘泽宇. 电驱动动力总成系统声品质评价维度及综合评价预测研究[D]. 天津: 河北工业大学, 2021.
LIU Ze-yu. Electric powertrain system sound quality evaluation dimensions and comprehensive evaluation prediction research [D]. Tianjin: Hebei University of Technology, 2021.
[14] 刁坤, 汪晓虎, 王伟东. 电动汽车电驱动噪声声品质主客观评价模型[J]. 噪声与振动控制, 2021, 41(03): 187-191+203.
DIAO Kun, WANG Xiao-hu, WANG Wei-dong. Subjective and objective evaluation model of sound quality of drive unit tonal noise of electric vehicles [J]. Nosie and Vibration Control, 2021, 41(03): 187-191+203.
[15] Liu H, Zhang H, Huang X, et al. Research on noise source separation and sound quality prediction for electric powertrain [J]. Applied Acoustics, 2022, 199.
[16] 黄煜, 陈克安, 闫靓, 等. 自适应分组成对比较法:原理及种子的选择[J]. 声学学报, 2008, 33(05): 443-449.
HUANG Yu, CHEN Ke-an, YAN Lian, et al. Adaptive grouped paired comparison: theory and selection of seeds [J]. Acta Acustica, 2008, 33(05): 443-449.
[17] 王泽俊, 林志斌, 陶建成. 基于多种子自适应分组成对比较的音质评价[J]. 南京大学学报(自然科学), 2021, 57(2): 327-333.
WANG Ze-jun, LIN Zhi-bin, TAO Jian-cheng. Evaluation of sound quality based on multi-seed adaptive grouped paired comparison [J]. Journal of Nanjing University (Natural Sciences), 2021, 57(2): 327–333.
[18] 毛东兴, 高亚丽, 俞悟周, 等. 声品质主观评价的分组成对比较法研究[J]. 声学学报, 2005, 30(06): 37-42.
MAO Dong-xing, GAO Ya-li, YU Wu-zhou, et al. Grouped pair-wise comparison for subjective sound quality evaluation [J]. Acta Acustica, 2005, 30(06): 37-42.
[19] 邱森. 加速工况车内声品质评价及优化研究[D]. 长春: 吉林大学, 2020.
QIU Sen. Research on vehicle interior sound quality evaluation and optimization under accelerated operating conditions [D]. Changchun: Jilin University, 2020.
[20] 毛东兴, 俞悟周, 王佐民. 声品质成对比较主观评价的数据检验及判据[J]. 声学学报, 2005, 30(05): 468-472.
MAO Dong-xing, YU Wu-zhou, WANG Zuo-min. Statistical validation and criterion for paired comparison data in sound quality evaluation [J]. Acta Acustica, 2005, 30(05): 468-472.
[21] 曾发林, 孙苏民. 基于RNR-WVD与GA-小波的非稳态排气噪声声品质研究[J]. 振动与冲击, 2019, 38(12): 74-80+104.
ZENG Fa-ling, SUN Su-ming. Study on the sound quality of unsteady exhaust noise based on RNR-WVD calculation and GA-wavelet network [J]. Journal of Vibration and Shock, 2019, 38(12): 74-80+104.
[22] 黄海波, 黄晓蓉, 苏瑞强, 等. 基于EEMD与GA-小波神经网络的传动系声品质预测[J]. 振动与冲击, 2017, 36(09): 130-137.
HUANG Hai-bo, CHENG Xiao-rong, SU Rui-qiang, et al. Sound metric prediction of a power train system based on EEMD and GA-wavelet neural network [J]. Journal of Vibration and Shock, 2017, 36(09): 130-137.
[23] LIU Q, ZHU J Z, WEN F L. Sound quality control based on CEEMD blind source separation and FELMS algorithm [J]. Electronics, 2022, 11(10): 1641-1641.
[24] 毕凤荣, 李琳, 张剑, 等. 基于EEMD-HT与LSSVM的柴油机辐射噪声品质预测技术[J]. 天津大学学报(自然科学与工程技术版), 2017, 50(01): 28-34.
BI Feng-rong, LI Lin, ZHANG Jian, et al. Sound quality prediction for diesel engine radiated noise based on EEMD-HT and LSSVM [J]. Journal of Tianjin University (Science and Technology), 2017, 50(01): 28-34.
[25] Lu Y, Zuo Y Y, HUI W, et al. Sound quality prediction for power coupling mechanism of HEV based on CEEMD-HT and RVM [J]. Neural Computing and Applications, 2020, 33(14): 1-16.
[26] Chen P S, Xu L Y, Tang Q S, et al. Research on prediction model of tractor sound quality based on genetic algorithm [J]. Applied Acoustics, 2022, 185: 108411.

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