Optimization of Automobile Firewall Acoustic Package for Multi-level Goals

HUANG Haibo; ZHENG Zhiwei; ZHANG Siwen; WU Yudong; YANG Mingliang; DING Weiping

doi:10.3969/j.issn.0258-2724.20211086

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ZHANG Wenbai, LIN Guobin, KANG Jinsong, ZHAO Yuanzhe, LIAO Zhiming. Sensorless Control Method of High-Frequency Injection for Long-Stator Synchronous Motor of Maglev Trains Considering Phase Shift Compensation[J]. Journal of Southwest Jiaotong University, 2025, 60(4): 1032-1041. doi: 10.3969/j.issn.0258-2724.20240310

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HUANG Haibo, ZHENG Zhiwei, ZHANG Siwen, WU Yudong, YANG Mingliang, DING Weiping. Optimization of Automobile Firewall Acoustic Package for Multi-level Goals[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20211086

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Optimization of Automobile Firewall Acoustic Package for Multi-level Goals

doi: 10.3969/j.issn.0258-2724.20211086

HUANG Haibo^{1, 2
,},
ZHENG Zhiwei¹,
ZHANG Siwen³,
WU Yudong¹,
YANG Mingliang^{1, 2},
DING Weiping^{1, 2
,
,}

1.
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
2.
Engineering Research Center of Advanced Driving Energy-Saving Technology, Ministry of Education , Southwest Jiaotong University, Chengdu 610031, China
3.
Changan Auto Globe R & D Center, Chongqing 401120, China

Received Date: 06 Jan 2022
Rev Recd Date: 16 Jun 2022

Available Online: 18 Jan 2023

Abstract

Abstract

To study the influence of automotive acoustic package design parameters on its multi-performance objectives, firstly, the traditional DBNs (deep belief networks) method was modified, and the SVR-DBNs (support vector regression-deep belief networks) model was proposed to improve the accuracy of model mapping. Secondly, from the perspective of vehicle noise transfer relationship and hierarchical target decomposition, a multi-level target prediction and analysis method was proposed. Finally, the proposed method was applied to the multi-objective prediction and optimization analysis of the MTL (mean transmission loss), weight and cost of the acoustic package for a real vehicle.The results show that the accuracy of SVR-DBNs method for the MTL, weight and cost target prediction of the acoustic package is higher than 0.975, which is better than that of the traditional BPNN（back propagation neural network）, SVR and DBNs models. The optimization results based on the SVR-DBNs model are appropriate to the measured results, the comprehensive relative error of the predicted and tested targets is 1.09% (the absolute values of the relative errors of MTL, weight and cost are 1.44%, 1.04% and 0.71%, respectively). Compared with the original status, the MTL, weight and cost of the acoustic package have increased by 5.51%, 9.01% and 4.40%, respectively.
- NVH (noise, vibration and harshness),
- acoustic package,
- sound absorption and insulation,
- SVR-DBNs,
- optimal design

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References

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