Prediction and Analysis of Vehicle Interior Road Noise Based on Mechanism and Data Series Modeling

  • Jian Pang State Key Laboratory of Vehicle Noise, Vibration and Harshness (NVH) and Safety Technology, Chongqing, 401120, China; Changan Auto Global R&D Center, Chongqing Changan Automobile Co., Ltd., Chongqing, 401120, China
  • Tingting Mao School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, China
  • Wenyu Jia Changan Auto Global R&D Center, Chongqing Changan Automobile Co., Ltd., Chongqing, 401120, China
  • Xiaoli Jia Changan Auto Global R&D Center, Chongqing Changan Automobile Co., Ltd., Chongqing, 401120, China
  • Peisong Dai School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, China
  • Haibo Huang State Key Laboratory of Vehicle Noise, Vibration and Harshness (NVH) and Safety Technology, Chongqing, 401120, China; School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, China
Article ID: 2647
Keywords: NVH; road noise; multi-body dynamics; data-driven; AE-LSTM

Abstract

Currently, the inexorable trend toward the electrification of automobiles has heightened the prominence of road noise within overall vehicle noise. Consequently, an in-depth investigation into automobile road noise holds substantial practical importance. Previous research endeavors have predominantly centered on the formulation of mechanism models and data-driven models. While mechanism models offer robust controllability, their application encounters challenges in intricate analyses of vehicle body acoustic-vibration coupling, and the effective utilization of accumulated data remains elusive. In contrast, data-driven models exhibit efficient modeling capabilities and can assimilate conceptual vehicle knowledge, but they impose stringent requirements on both data quality and quantity. In response to these considerations, this paper introduces an innovative approach for predicting vehicle road noise by integrating mechanism-driven and data-driven methodologies. Specifically, a series model is devised, amalgamating mechanism analysis with data-driven techniques to predict vehicle interior noise. The simulation results from dynamic models serve as inputs to the data-driven model, ultimately generating outputs through the utilization of the Long Short-Term Memory with Autoencoder (AE-LSTM) architecture. The study subsequently undertakes a comparative analysis between different dynamic models and data-driven models, thereby validating the efficacy of the proposed series vehicle road noise prediction model. This series model, encapsulating the rigid-flexible coupling dynamic model and AE-LSTM series model, not only demonstrates heightened computational efficiency but also attains superior prediction accuracy.

References

[1]Baker, D., Sturesson, P., Sale, C., Kulkarni, K., Boston, C. (2021). Development and optimization of vehicle systems for improved road noise and prediction using vehicle system model in an autonomous vehicle application. SAE International Journal of Advances and Current Practices in Mobility, 4, 299–308. https://doi.org/10.4271/2021-01-1109

[2]Huang, H., Huang, X., Ding, W., Zhang, S., Pang, J. (2023). Optimization of electric vehicle sound package based on LSTM with an adaptive learning rate forest and multiple-level multiple-object method. Mechanical Systems and Signal Processing, 187, 109932. https://doi.org/10.1016/j.ymssp.2022.109932

[3]Huang, H., Huang, X., Ding, W., Yang, M., Yu, X. (2023). Vehicle vibro-acoustical comfort optimization using a multi-objective interval analysis method. Expert Systems with Applications, 213, 119001. https://doi.org/10.1016/j.eswa.2022.119001

[4]Olley, M. (1934). Independent wheel suspension-its whys and wherefors. SAE Journal, 34(3), 73–81.

[5]Segel, L. (1956). Theoretical prediction and experimental substantiation of the response of the automobile to steering control. Proceedings of the Institution of Mechanical Engineers: Automobile Division, 10(1), 310–330.

[6]Matschinsky, W. (1987). Die Radführungen der Strassenfahrzeuge. Analyse. In: Synthese, Elasto-Kinematik. Köln: TÜV Rheinland.

[7]Wang, Y. (2017). Research on key technology of automobile vibration model establishment and verification based on real vehicle driving condition (Master Thesis). Chongqing University of Technology, China.

[8]Li, F., Li, X., Wo, X., Wen, B. (2021). Analysis and optimization of damping performance of suspension system of hybrid connected vehicle. Journal of Northeastern University (Natural Science Edition), 42(8), 1098–1104 (In Chinese). https://doi.org/10.12068/j.issn.1005-3026.2021.08.006

[9]Hurel, J., Mandow, A., García-Cerezo, A. (2013). Kinematic and dynamic analysis of the MacPherson suspension with a planar quarter-car model. Vehicle System Dynamics, 51(9), 1422–1437. https://doi.org/10.1080/00423114.2013.804937

[10]Fan, D., Dai, P., Yang, M., Jia, W., Jia, X. (2022). Research on maglev vibration isolation technology for vehicle road noise control. SAE International Journal of Vehicle Dynamics, Stability, and NVH, 6, 233–245. https://doi.org/10.4271/10-06-03-0016

[11]Chen, S., Wang, D., Zan, J. (2012). Brake judder analysis using a car rigid-flexible coupling model. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 226, 348–361. https://doi.org/10.1177/0954407011417760

[12]Chen, S., Shi, T., Wang, D., Chen, J. (2015). Multi-objective optimization of the vehicle ride comfort based on Kriging approximate model and NSGA-II. Journal of Mechanical Science and Technology, 29, 1007–1018. https://doi.org/10.1007/s12206-015-0215-x

[13]Chang, H., Gao, Y., Zhang, S. (2023). Load spectrum extraction of double-wishbone independent suspension bracket based on virtual iteration (No. 2023-01-0774). SAE Technical Paper. https://doi.org/10.4271/2023-01-0774

[14]Wang, L., Zhao, Y., Li, L., Ding, Z. (2016). Research on the vibration characteristics of the commercial-vehicle cabin based on experimental design and genetic algorithm. Journal of Vibroengineering, 18(7), 4664–4677. https://doi.org/10.21595/jve.2016.17161

[15]Zeng, Q., Wang, H., Ji, L., Li, S., Huang, Y. (2020). Road noise analysis and optimization research of vehicles based on wheel core loads. Noise and Vibration Control, 40(4), 183–189. https://doi.org/10.3969/j.issn.1006-1355.2020.04.033

[16]Yang, G., Hu, H., Guo, Z., Li, X., Ding, Z. (2015). Load in the NVHD environmen noise performance based on spindle analysis and optimization of vehicle road. 2015 Altair Technology Conference Proceedings, pp. 685–692. Shanghai, China.

[17]Zou, J., Huss, M., Abid, A., Mohammadi, P., Torkamani, A. et al. (2019). A primer on deep learning in genomics. Nature Genetics, 51(1), 12–18. https://doi.org/10.1038/s41588-018-0295-5

[18]Jayasree, T., Shia, S. (2021). Combined signal processing based techniques and feed forward neural networks for pathological voice detection and classification. Sound & Vibration, 55(2), 141–161. https://doi.org/10.32604/sv.2021.011734

[19]Huang, H., Huang, X., Ding, W., Yang, M., Fan, D. et al. (2022). Uncertainty optimization of pure electric vehicle interior tire/road noise comfort based on data-driven. Mechanical Systems and Signal Processing, 165, 108300. https://doi.org/10.1016/j.ymssp.2021.108300

[20]Lino, M., Fotiadis, S., Bharath, A. A., Cantwell, C. D. (2023). Current and emerging deep-learning methods for the simulation of fluid dynamics. Proceedings of the Royal Society A, 479. https://doi.org/10.1098/rspa.2023.0058

[21]Huang, H., Lim, T. C., Wu, J., Ding, W., Pang, J. (2023). Multitarget prediction and optimization of pure electric vehicle tire/road airborne noise sound quality based on a knowledge- and data-driven method. Mechanical Systems and Signal Processing. https://doi.org/10.1016/j.ymssp.2023.110361

[22]Liu, W., Huang, H., Fan, D., Wang, D., Ding, W. (2023). Vehicle’ s road noise prediction method based on LSTM. Noise and Vibration Control, 61(2), 205–212. https://doi.org/10.3969/j.issn.1006-1355.2023.03.023

[23]Lin, J., Dou, C., Wang, Q. (2018). Comparisons of MFDFA, EMD and WT by neural network, mahalanobis distance and SVM in fault diagnosis of gearboxes. Sound & Vibration, 52(2), 11–15. https://doi.org/10.32604/sv.2018.03653

[24]Wu, Y., Liu, X., Huang, H., Wu, Y., Ding, W. et al. (2023). Multi-objective prediction and optimization of vehicle acoustic package based on ResNet neural network. Sound & Vibration, 57(1), 73–95. https://doi.org/10.32604/sv.2023.044601

[25]Tan, X. (2020). Learn NVH: Noise, vibration, and modal analysis from here. Beijing, China: China Machine Press.

[26]Koppenaal, J., van Oosten, J., Porsche, I., Chavan, A. (2010). General modeling of nonlinear isolators for vehicle ride studies. SAE International Journal of Materials and Manufacturing, 3(1), 585–591. https://doi.org/10.4271/2010-01-0950

[27]Tian, J. (2020). Simulation analysis and optimization of ride comfort on rigid-flexible coupling vehicle model of heavy tractor, China (Master Thesis). Hefei University of Technology, China.

[28]Wang, H., Yuan, X., Yang, M. (2019). Network traffic prediction and application based on LSTM and traditional neural networks. Mobile Communications, 43(8), 37–44. https://doi.org/10.3969/j.issn.1006-1010.2019.08.007

[29]Hochreiter, S., Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

[30]Mi, Y., Xu, D., Gao, T. (2023). Application of PCA-LSTM algorithm for financial market stock return prediction and optimization model. International Journal for Simulation and Multidisciplinary Design Optimization, 14, 8. https://doi.org/10.1051/smdo/2023009

[31]Kong, W., Zhang, Y., Jia, Y., Hill, D., Xu, Y. (2017). Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Transactions on Smart Grid, 10(1), 841–851. https://doi.org/10.1109/TSG.2017.2753802

[32]Cheng, J., Dong, L., Lapata, M. (2016). Long short-term memory-networks for machine reading. https://doi.org/10.18653/v1/D16-1053

[33]Ling, Z., Zhang, Y., Cao, G., Chen, J., Li, L. (2022). AE-CNN-based multisource data fusion for gait motion step length estimation. IEEE Sensors Journal, 22(21), 20805–20815. https://doi.org/10.1109/JSEN.2022.3206883

[34]Huang, H., Wu, J., Huang, X., Yang, M., Ding, W. (2020). A generalized inverse cascade method to identify and optimize vehicle interior noise sources. Journal of Sound and Vibration, 467, 115062. https://doi.org/10.1016/j.jsv.2019.115062

[35]Wei, W., Wu, H., Ma, H. (2019). An autoencoder and LSTM-based traffic flow prediction method. Sensors, 19(13), 2946. https://doi.org/10.3390/s19132946

[36]Bates, S., Sienz, J., Langley, D. (2003). Formulation of the Audze-Eglais uniform Latin hypercube design of experiments. Advances in Engineering Software, 34(8), 493–506. https://doi.org/10.1016/S0965-9978(03)00042-5

[37]Syarif, I., Prugel-Bennett, A., Wills, G. (2016). SVM parameter optimization using grid search and genetic algorithm to improve classification performance. TELKOMNIKA (Telecommunication Computing Electronics and Control), 14(4), 1502–1509. https://doi.org/10.12928/telkomnika.v14i4.3956

[38]

Published
2024-02-27
How to Cite
Pang, J., Mao, T., Jia, W., Jia, X., Dai, P., & Huang, H. (2024). Prediction and Analysis of Vehicle Interior Road Noise Based on Mechanism and Data Series Modeling. Sound & Vibration, 58(1), 046247. Retrieved from https://ojs.acad-pub.com/index.php/SV/article/view/2647
Issue
Vol. 58 No. 1 (2024)
Section
Article