Evaluation Modeling of Electric Bus Interior Sound Quality Based on Two Improved XGBoost Algorithms Using GS and PSO
Abstract
There is no doubt that traffic noise has become one of the main sources of urban noise, and the electric bus, as an important means of transport frequently used by people in daily life, has a direct impact on the psychological and auditory health of passengers due to its interior noise characteristics. Consequently, studying electric bus sound quality is an important way to improve vehicle performance and comfort. In this paper, eight electric buses were selected and 64 noise samples were measured. Acoustic comfort was taken as an evaluation index, professionals were organized to complete the subjective evaluation tests for all noise samples based on rank score comparison (RSC). And nine psycho-acoustic objective parameters such as loudness, sharpness and roughness were calculated using Artemis software to establish the sound quality database of electric buses. Aiming at the practical application requirements of high-precision modeling of acoustic comfort in vehicles, this paper presented two improved extreme gradient boosting (XGBoost) algorithms based on grid search (GS) method and particle swarm optimization (PSO), respectively, with objective parameters and acoustic comfort as input and output variables, and established three regression models of standard XGBoost, GS-XGBoost, and PSO-XGBoost through data training. Finally, the calculation results of three indexes of average relative error, square root error and correlation coefficient indicate that the proposed PSO-XGBoost model is significantly better than GS-XGBoost and standard XGBoost, with its prediction accuracy as high as 97.6 %. This model is determined as the evaluation model of interior acoustic comfort for this case, providing a key technical support for future sound quality optimization of electric buses.Keywords:
electric bus, sound quality, acoustic comfort, GS-XGBoost, PSO-XGBoostReferences
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2. Chen T.Q., Guestrin C. (2016), XGBoost: A scalable tree boosting system, [in:] KDD ’16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794, https://doi.org/10.1145/2939672.2939785
3. Das G., Pattnaik P.K., Padhy S.K. (2014), Artificial neural network trained by particle swarm optimization for non-linear channel equalization, Expert Systems with Applications, 41(7): 3491–3496, https://doi.org/10.1016/j.eswa.2013.10.053
4. Ding F., Xie W.T., Xie X.P. (2023), Research on optimization of car door closing sound quality based on the integration of structural simulation and test, [in:] Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 237(6): 1378–1390, https://doi.org/10.1177/09544070221088370
5. Doleschal F., Verhey J.L. (2022), Pleasantness and magnitude of tonal content of electric vehicle interior sounds containing subharmonics, Applied Acoustics, 185: 108442, https://doi.org/10.1016/j.apacoust.2021.108442
6. Huang H.B., Huang X.R., Li R.X., Lim T.C., Ding W.P. (2016), Sound quality prediction of vehicle interior noise using deep belief networks, Applied Acoustics, 113: 149–161, https://doi.org/10.1016/j.apacoust.2016.06.021
7. Huang H.B., Wu J.H., Lim T.C., Yang M.L., Ding W.P. (2021), Pure electric vehicle nonstationary interior sound quality prediction based on deep CNNs with an adaptable learning rate tree, Mechanical System and Signal Processing, 148: 107170, https://doi.org/10.1016/j.ymssp.2020.107170
8. Huang X.R., Huang H.B., Wu J.H., Yang M.L., Ding W.P. (2020), Sound quality prediction and improving of vehicle interior noise based on deep convolutional neural networks, Expert Systems with Applications, 160: 113657, https://doi.org/10.1016/j.eswa.2020.113657
9. Kim D., Lee J. (2022), Predictive evaluation of spectrogram-based vehicle sound quality via data augmentation and explainable artificial Intelligence: Image color adjustment with brightness and contrast, Mechanical Systems and Signal Processing, 179: 109363, https://doi.org/10.1016/j.ymssp.2022.109363
10. Liang L.Y., Chen S.M., Li P.R. (2020), The evaluation of vehicle interior impact noise inducing by speed bumps based on multi-features combination and support vector machine, Applied Acoustics, 163: 107212, https://doi.org/10.1016/j.apacoust.2020.107212
11. Madvari R.F., Sharak M.N., Tehrani M.J., Abbasi M. (2022), Estimation of metal foam microstructure parameters for maximum sound absorption coefficient in specified frequency band using particle swarm optimisation, Archives of Acoustics, 47(1): 33–42, https://doi.org/10.24425/aoa.2022.140730
12. Pourseiedrezaei M., Loghmani A., Keshmiri M. (2021), Development of a sound quality evaluation model based on an optimal analytic wavelet transform and an artificial neural network, Archives of Acoustics, 46(1): 55–65, https://doi.org/10.24425/aoa.2021.136560
13. Shi W.K., Liu G.Z., Song H.S., Chen Z.Y., Zhang B. (2018), Vibration and noise characteristics of electric bus [in Chinese], Jilin Daxue Xuebao (Gongxueban), 48(2): 373–379, https://doi.org/10.13229/j.cnki.jdxbgxb20170110
14. Steinbach L., Altinsoy M.E. (2019), Prediction of annoyance evaluations of electric vehicle noise by using artificial neural networks, Applied Acoustics, 145: 149–158, https://doi.org/10.1016/j.apacoust.2018.09.024
15. Wang C., Peng J.X., Zhang X.W. (2020), A classification method related to respiratory disorder events based on acoustical analysis of snoring, Archives of Acoustics, 45(1): 141–151, https://doi.org/10.24425/aoa.2020.132490
16. Wang Y., Zhang S., Meng D.J., Zhang L.J. (2022), Nonlinear overall annoyance level modeling and interior sound quality prediction for pure electric vehicle with extreme gradient boosting algorithm, Applied Acoustics, 195: 108857, https://doi.org/10.1016/j.apacoust.2022.108857
17. Wang Y.S. (2009), Sound quality estimation for nonstationary vehicle noises based on discrete wavelet transform, Journal of Sound and Vibration, 324(3–5): 1124–1140, https://doi.org/10.1016/j.jsv.2009.02.034
18. Zhang E.L., Chen X.Y., Li S.Y., W Q.Q., Zhuo J.M. (2022a), A comprehensive evaluation model of electric bus interior acoustic comfort and its application, International Journal of Acoustics and Vibration, 27(4): 361–366, https://doi.org/10.20855/ijav.2022.27.41887
19. Zhang E.L., Chen Y., Chen X.Y., Zhang J.B., Xun P.W., Zhuo J.M. (2023a), High-precision modeling and prediction of acoustic comfort for electric bus based on BPNN and XGBoost, International Journal of Acoustics and Vibration, 28(2): 158–164, https://doi.org/10.20855/ijav.2023.28.21922
20. Zhang E.L., Hou L., Shen C. (2016), Sound quality prediction of vehicle interior noise and mathematical modeling using a back propagation neural network (BPNN) based on particle swarm optimization (PSO), Measurement Science and Technology, 27(1): 015801, https://doi.org/10.1088/0957-0233/27/1/015801
21. Zhang E.L., Zhang Q.M., Xiao J.J., Hou L., Guo T. (2018), Acoustic comfort evaluation modeling and improvement test of a forklift based on rank score comparison and multiple linear regression, Applied Acoustics, 135: 29–36, https://doi.org/10.1016/j.apacoust.2018.01.026
22. Zhang E.L., Zhuo J.M., Hou L., Fu C.H., Guo T. (2021), Comprehensive annoyance modeling of forklift sound quality based on rank score comparison and multi-fuzzy analytic hierarchy process, Applied Acoustics, 173: 107705, https://doi.org/10.1016/j.apacoust.2020.107705
23. Zhang X.C., Cheng J., Lu J.W., Yuan B., Jiang P., Sha W. (2022b), Sound quality evaluation of pure electric vehicle with subjective and objective unified evaluation method, International Journal of Vehicle Design, 88(2–4): 283–303, https://doi.org/10.1504/IJVD.2022.127024
24. Zhang Y., Meng T., Wang K. (2020), Subjective evaluation model for interior sound quality and optimization of medium-frequency noise, Automotive Engineering, 42(5): 651–657+664, https://doi.org/10.19562/j.chinasae.qcgc.2020.05.013
25. Zhang Y., Peng B.T., Yang E.C., Ren K.L., OU J. (2023b), Prediction and analysis of vehicle interior sound quality based on XGBoost algorithm [in Chinese], Noise and Vibration Control, 43(3): 161–166+211, https://doi.org/10.3969/j.issn.1006-1355.2023.03.025
