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In this research, the interior noise of a passenger car was measured, and the sound quality metrics including sound pressure level, loudness, sharpness, and roughness were calculated. An artificial neural network was designed to successfully apply on automotive interior noise as well as numerous different fields of technology which aim to overcome difficulties of experimentations and save cost, time and workforce. Sound pressure level, loudness, sharpness, and roughness were estimated by using the artificial neural network designed by using the experiment values. The predicted values and experiment results are compared. The comparison results show that the realized artificial intelligence model is an appropriate model to estimate the sound quality of the automotive interior noise. The reliability value is calculated as 0.9995 by using statistical analysis.

In order to predict the interior noise of a commercial vehicle cab, a finite element model of a heavy commercial vehicle cab was established. An acoustic-structure coupling model of the cab was built based on experimentally validated structure model and acoustic model of a commercial vehicle cab. Moreover, based on the platform of Virtual. Lab, the acoustic field modes of the acoustic model of the commercial vehicle cab and the coupled modes of the acoustic-structure coupling model were analyzed by using the acoustic-structure coupling analysis technique. The excitation of the vehicle cab was tested at an average speed on an asphalt road. Then, the interior noise of the heavy commercial vehicle cab was predicted based on FEM-FEM method and FEM-BEM method with all the parameters and excitation. Furthermore, the predicted interior noise of the commercial vehicle cab was compared with the tested interior noise.

Intake system is an important noise source for commercial vehicles, which has a significant impact on their NVH performance. To predict the intake noise more accurately, a new one-dimensional prediction model is proposed in this paper. An air compressor model is introduced into the traditional model, and the acoustic properties of the intake system are simulated by GT-power. The simulation data of the inlet noise is obtained to make a comparison with the inlet noise data acquired from a test. The result shows that the proposed model can make a more precise prediction of the inlet noise. Compared with the traditional model, the proposed model can identify the noise coming from the air compressor, and achieve a more accurate prediction of the total sound pressure level of the inlet noise.

With the prevalent trend of the pure electric vehicle, vehicle interior noise has been reduced significantly. However, other noises become prominent in the cabin. Especially, the BSR noise generated by friction between parts and the clearance between components become the elements of complaints directly affect the quality of vehicles. Currently, the BSR noises are subjectively evaluated by experts, and the noise samples are simply labeled as ‘qualified’ or ‘unqualified’. Therefore, it is necessary to develop an evaluation model to assess the BSR noise objectively. In this paper, we study the vehicle rattle noise intensively. Several types of rattle noise were recorded in a semi-anechoic room. The recorded signals were then processed in the LMS test lab. to extract the single impact segments. A pool of simulated signals with different impact rates (number of impacts per second) and various loudness was synthesized for analyzation.