Archives of Acoustics, Online first
10.24425/aoa.2024.148794

Evaluation Modeling of Electric Bus Interior Sound Quality Based on Two Improved XGBoost Algorithms Using GS and PSO

Enlai ZHANG
School of Mechanical and Automotive Engineering, Xiamen University of Technology; Xiamen Key Laboratory of Robot Systems and Digital Manufacturing
China

Yi CHEN
School of Mechanical and Automotive Engineering, Xiamen University of Technology
China

Liang SU
Bus Engineering Research Institute, Xiamen King Long United Automotive Industry Co., Ltd
China

Ruoyu ZHONGLIAN
School of Mechanical and Automotive Engineering, Xiamen University of Technology
China

Xianyi CHEN
School of Mechanical and Automotive Engineering, Xiamen University of Technology
China

Shangfeng JIANG
School of Mechanical and Automotive Engineering, Xiamen University of Technology
China

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-XGBoost
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Copyright © 2024 The Author(s). This work is licensed under the Creative Commons Attribution 4.0 International CC BY 4.0.


DOI: 10.24425/aoa.2024.148794