Multi-Objective Prediction of the Sound Insulation Performance of a Vehicle Body System Using Multiple Kernel Learning–Support Vector Regression
The sound insulation performance of an electric vehicle’s body system serves as a critical metric for evaluating the noise, vibration, and harshness (NVH) quality of the vehicle. The accurate and efficient prediction of sound insulation performance is foundational for undertaking noise reduction des...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/13/3/538 |
| _version_ | 1797318863721857024 |
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| author | Ping Sun Ruxue Dai Haiqing Li Zhiwei Zheng Yudong Wu Haibo Huang |
| author_facet | Ping Sun Ruxue Dai Haiqing Li Zhiwei Zheng Yudong Wu Haibo Huang |
| author_sort | Ping Sun |
| collection | DOAJ |
| description | The sound insulation performance of an electric vehicle’s body system serves as a critical metric for evaluating the noise, vibration, and harshness (NVH) quality of the vehicle. The accurate and efficient prediction of sound insulation performance is foundational for undertaking noise reduction design and optimization. Current engineering practices predominantly rely on Computer-Aided Engineering (CAE) methodologies to address this challenge. However, inherent shortcomings such as low modeling efficiency and difficulty in ensuring prediction accuracy often characterize these approaches. In an effort to overcome these limitations, we propose a decomposition framework for predicting the sound insulation performance of the electric vehicle body system. This framework is established based on a comprehensive analysis of the noise transmission paths within the system. Subsequently, the support vector regression (SVR) method is introduced to construct a machine learning model specifically designed for predicting the sound insulation performance of the body system. This approach aims to mitigate the inherent weaknesses associated with the conventional CAE processes using a ‘data-driven’ paradigm. Furthermore, the Multiple Kernel Learning (MKL) method is used to enhance the processing efficacy of the SVR model. The proposed method is validated using practical application and testing on a specific electric vehicle. The results demonstrate commendable performance in terms of prediction accuracy and robustness. This research contributes to advancing the field by presenting a more effective and reliable approach to predicting the sound insulation performance of electric vehicle body systems, offering valuable insights for noise reduction strategies and optimization efforts in the automotive industry. |
| first_indexed | 2024-03-08T03:58:34Z |
| format | Article |
| id | doaj.art-57bf3bb380c04038801e81d6c646a06c |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-08T03:58:34Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-57bf3bb380c04038801e81d6c646a06c2024-02-09T15:10:34ZengMDPI AGElectronics2079-92922024-01-0113353810.3390/electronics13030538Multi-Objective Prediction of the Sound Insulation Performance of a Vehicle Body System Using Multiple Kernel Learning–Support Vector RegressionPing Sun0Ruxue Dai1Haiqing Li2Zhiwei Zheng3Yudong Wu4Haibo Huang5Liuzhou Vocational and Technical College, Liuzhou 545006, ChinaSchool of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaLiuzhou Vocational and Technical College, Liuzhou 545006, ChinaThe 30th Research Institute of China Electronics Technology Group Corporation, Chengdu 610093, ChinaSchool of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaSchool of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaThe sound insulation performance of an electric vehicle’s body system serves as a critical metric for evaluating the noise, vibration, and harshness (NVH) quality of the vehicle. The accurate and efficient prediction of sound insulation performance is foundational for undertaking noise reduction design and optimization. Current engineering practices predominantly rely on Computer-Aided Engineering (CAE) methodologies to address this challenge. However, inherent shortcomings such as low modeling efficiency and difficulty in ensuring prediction accuracy often characterize these approaches. In an effort to overcome these limitations, we propose a decomposition framework for predicting the sound insulation performance of the electric vehicle body system. This framework is established based on a comprehensive analysis of the noise transmission paths within the system. Subsequently, the support vector regression (SVR) method is introduced to construct a machine learning model specifically designed for predicting the sound insulation performance of the body system. This approach aims to mitigate the inherent weaknesses associated with the conventional CAE processes using a ‘data-driven’ paradigm. Furthermore, the Multiple Kernel Learning (MKL) method is used to enhance the processing efficacy of the SVR model. The proposed method is validated using practical application and testing on a specific electric vehicle. The results demonstrate commendable performance in terms of prediction accuracy and robustness. This research contributes to advancing the field by presenting a more effective and reliable approach to predicting the sound insulation performance of electric vehicle body systems, offering valuable insights for noise reduction strategies and optimization efforts in the automotive industry.https://www.mdpi.com/2079-9292/13/3/538body systemsound insulation performancedecompositionMKL-SVRsound insulation performance prediction |
| spellingShingle | Ping Sun Ruxue Dai Haiqing Li Zhiwei Zheng Yudong Wu Haibo Huang Multi-Objective Prediction of the Sound Insulation Performance of a Vehicle Body System Using Multiple Kernel Learning–Support Vector Regression Electronics body system sound insulation performance decomposition MKL-SVR sound insulation performance prediction |
| title | Multi-Objective Prediction of the Sound Insulation Performance of a Vehicle Body System Using Multiple Kernel Learning–Support Vector Regression |
| title_full | Multi-Objective Prediction of the Sound Insulation Performance of a Vehicle Body System Using Multiple Kernel Learning–Support Vector Regression |
| title_fullStr | Multi-Objective Prediction of the Sound Insulation Performance of a Vehicle Body System Using Multiple Kernel Learning–Support Vector Regression |
| title_full_unstemmed | Multi-Objective Prediction of the Sound Insulation Performance of a Vehicle Body System Using Multiple Kernel Learning–Support Vector Regression |
| title_short | Multi-Objective Prediction of the Sound Insulation Performance of a Vehicle Body System Using Multiple Kernel Learning–Support Vector Regression |
| title_sort | multi objective prediction of the sound insulation performance of a vehicle body system using multiple kernel learning support vector regression |
| topic | body system sound insulation performance decomposition MKL-SVR sound insulation performance prediction |
| url | https://www.mdpi.com/2079-9292/13/3/538 |
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