Journal of South China University of Technology(Natural Science Edition) ›› 2022, Vol. 50 ›› Issue (3): 65-72.doi: 10.12141/j.issn.1000-565X.210445

Special Issue: 2022年机械工程

• Mechanical Engineering • Previous Articles     Next Articles

Analysis of the Powertrain Mount Systems of Electric Vehicles by Considering the Correlation of Probabilistic Parameters

LÜ Hui1,2 ZHAO Jiawei1 MAO Haikuan1 HUANG Xiaoting3   

  1. 1. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, Guangdong, China； 2. Key Laboratory of Advanced Manufacture Technology for Automobile Parts of Ministry of Education, Chongqing University of Technology, Chongqing 400054, China；  3. School of Automobile and Traffic Engineering, Guangzhou City University of Technology, Guangzhou 510800, Guangdong, China
  • Received:2021-07-09 Revised:2021-11-01 Online:2022-03-25 Published:2022-03-01
  • Contact: 黄晓婷(1988-)，女，硕士，讲师，主要从事汽车NVH研究。 E-mail:huangxiaoting1@gcu.edu.cn
  • About author:吕辉(1986-)，男，博士，副教授，主要从事汽车振动噪声和可靠性设计研究。E-mail：melvhui@scut.edu.cn
  • Supported by:
    Supported by tle National Natural Science Foundation of China(51975217)and the Natural Science Foundation of Guangdong Province(2020A1515010352)

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Abstract: Aiming at the complex situation where the parameters of powertrain mounting system (PMS) of electric vehicle are both uncertain and correlated, this paper investigated the inherent characteristics of electric vehicle PMS considering the correlation of probabilistic parameters. Firstly, the correlation matrix and probabilistic parameters were employed to describe the correlation and uncertainty of PMS parameters. Then, the Monte-Carlo method (MCM) was developed to calculate the PMS inherent characteristics with correlated probabilistic parametersbased on Monte-Carlo sampling. Then, an efficient method for calculating the statistical moments of PMS inherent characteristics was put forward based on sparse grid numerical integration (SGNI). Finally, the effectiveness of the proposed approach was demonstrated by the numerical example of an electric vehicle PMS. The analysis results show that, the SGNI method has good accuracy and efficiency in solving statistical moments and bounds of PMS natural frequencies and decoupling ratios, compared with MCM. The correlation of probabilistic parameters has a great influence on the upper and lower bounds of decoupling ratios. More reasonable analysis results can be obtained by taking the correlation of the probabilistic parameters into consideration. 

Key words: electric vehicle, powertrain mounting system, probabilistic parameter, correlation, inherent 

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