Sound quality modeling and active control for electric buses based on complete ensemble empirical mode decomposition with adaptive noise

Sound quality research is an important development direction for electric bus interior noise control, and this paper proposes a coherent method of sound quality evaluation, modeling, prediction and active control. Firstly, an objective evaluation parameter is constructed based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and energy characterization; The structural parameters of the extreme gradient boosting (XGBoost) algorithm is adaptively optimized by particle swarm optimization (PSO) to establish the sound quality prediction model. Secondly, an active sound quality control method based on CEEMDAN and filtered-x least mean square (FxLMS) is presented. Finally, through the training and testing of sound quality data of 64 electric bus noise samples under different working conditions, the acoustic comfort evaluation model based on PSO-XGBoost algorithm is validated to have high prediction and fitting accuracy. In addition, the active controls based on CEEMDAN-FxLMS algorithm are implemented for four signal samples in a semi-anechoic room, and the results indicate that their acoustic comforts are effectively improved. The feasibility of the proposed method for sound quality modeling and active control is thus verified.