The sound quality (SQ) and sound perception assessments of electric vehicles (EVs) clearly differ from those of conventional internal combustion engine vehicles (ICEVs). Therefore, it is essential to describe and evaluate the SQ of EVs. To evaluate the SQ in EVs, it is necessary to organize evaluators for conducting subjective jury tests, which are time-consuming and labor-intensive. In addition, the evaluation results are subject to the evaluators themselves and other external interferences. With the advancement of machine learning and artificial neural networks (ANNs), this problem can be well solved. This paper outlines a model for SQ estimation in EVs based on a genetic algorithm-optimized back propagation artificial neural network (GA-BP ANN). Moreover, the correlation between the physical-psychoacoustical parameters and the subjective SQ estimations obtained from the jury tests was investigated in this study. It was found that the GA-BP ANN SQ model has many advantages in comparison with the multiple linear regression (MLR) model in terms of precision and generalization. In addition, this method is ready to be applied for rapidly evaluating the SQ in EVs without jury tests, and it can also be of high significance in dealing with the acoustical designs and improvements of EVs in the future.

Electric vehicles (EV) are considerably quieter than internal combustion engine (ICE) powered vehicles, because noise of ICE is eliminated. However, the interior noise of an EV usually contains significant high-frequency noise components caused by electrical motor, which can be subjectively perceived as annoying and unpleasant. This paper describes a numerical model to predict interior acoustic noise caused by electromagnetic forces in permanent magnet synchronous motor (PMSM) for electric vehicles. Firstly, the principle of the multiphysics method is to establish a complete 3Dstructural finite element model (FEM) of motor. Based on FEM, natural frequency and modal shape were calculated by modal analysis. Secondly, using an electromagnetic finite element solver, the excitation due to electromagnetic phenomena is obtained. This excitation is then projected onto the structure mesh of motor in order to calculate the dynamic response. Thirdly, radiated electromagnetic vibration acceleration on the surface of the motor is calculated with modal superposition method. Compared with experimental test results, the creditability of motor electromagnetic vibration simulation is proved. Finally, by combining with transfer path analysis (TPA) techniques, interior electromagnetic noise of electric vehicles is accurately predicted. According to contribution analysis, motor surface zones and transfer paths contributing largely to the interior motor electromagnetic noise are identified. The results play a significant guiding role in both electric vehicle and permanent magnet synchronous motor for noise control and analysis.

The interior sound quality (SQ) of pure electric vehicles (PEVs) has become an important consideration for users purchasing vehicles. At present, it is insufficient to take the sound pressure level as the interior acoustics design index of PEVs. Transfer path analysis (TPA) and transfer path synthesis (TPS) that take the SQ of interior noise as the improvement target remains in the preliminary exploration stage. In this paper, objective psychoacoustic parameters of SQ were taken as evaluation indexes of interior PEV noise. A virtual interior SQ synthesis model was designed on the basis of TPA and TPS, which combines experimentation and simulation. The SQ synthesis model demonstrates each noise component contribution in a PEV by new SQ separation technology. First, the interior noise transfer path and noise source of the PEV were determined in a synthesis analysis method of the interior PEV noise. Second, on the basis of the composition mechanism of interior noise and the basic principle of TPA, the excitation signal and transfer function of each interior noise path in the PEV were tested. On the basis of TPS, the interior SQ synthesis model of PEV was then established. Finally, the accuracy of the prediction model was verified in simulation and experimental comparison studies on the psychoacoustic objective parameters of SQ. The SQ objective parameter value of each transfer path was quantified by using contribution analysis. The results are expected to improve the comfort of the interior acoustic environment and enhance the competitiveness of vehicle products. They also provide an effective reference and new ideas for the development of interior SQ in PEVs.

The interior noise of vehicles directly affects the comfort of the occupants, necessitating precise evaluation and control. Existing research has focused on constructing mappings between objective parameters and subjective perceptions of noise, where back propagation neural networks (BPNNs) are widely used due to their strong nonlinear mapping capabilities. However, the selection of initial weights and thresholds can affect the predictive accuracy of BPNN. This study developed a BPNN model optimized by an intelligent algorithm for predicting the level of subjective annoyance of passengers during the movement. Initially, objective parameters of interior noise were obtained through acoustic signal processing techniques, and five parameters were selected for studying subjective annoyance through correlation analysis and two-tailed tests. Meanwhile, the actual subjective ratings of passengers on interior noise were captured for subsequent training of the model and testing of the results. Finally, the established sparrow search algorithm (SSA) and genetic algorithm (GA) optimized BPNN were used to predict subjective evaluations. The predictive accuracy and efficiency of the model were significantly improved upon validation, providing a viable alternative to traditional passenger vehicle noise assessment experiments and valuable references for future noise control and optimization efforts. The experimental results are consistent with the view that the neural network model optimized with a mixture of intelligent algorithms is closer to the passenger’s subjective annoyance level having higher accuracy and efficiency.

With the increasing demand of users for the acoustical comfort of commercial vehicles, the sound quality has become one of the important indicators of comfort evaluation. The research focuses on the objective evaluation method of the subjective perception of the sound quality in commercial vehicle. The interior noises of commercial vehicle with an inline six diesel engine are measured. The five psychoacoustic parameters (loudness, roughness, sharpness, fluctuation strength, tonality and articulation index) are applied to the evaluation and analysis of the interior noises of the commercial vehicle. Using psychoacoustic parameters to evaluate the noises in commercial vehicle, it is of great significance for the analysis and control of the noises in commercial vehicle. The research results provide a theoretical basis for guiding the sound quality design and development of commercial vehicles.