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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 this paper, the relationship was investigated between objective psychoacoustic parameters, A-weighted sound pressure level (SPL) and the results of the subjective evaluation by using grey relational analysis (GRA). The sounds were recorded with eight different passenger cars at four different running conditions. The sound quality indices were calculated, including loudness, sharpness, roughness, fluctuation, and A-weighted SPL. Subjective evaluation was performed by thirty subjects using rating scale method. GRA was compared with traditional correlation analysis, and the comparison shows that some hidden information which could not be found in the traditional correlation analysis was revealed. In order to know the further relationship between fluctuation and subjective evaluation, another subjective evaluation was performed by the same 30 subjects. The result demonstrates that the relationship revealed from GRA is correct.

In this paper, the performance simulation model of a domestic self-dumping truck was established using AVL-Cruise software. Then its accuracy was checked by the power performance and fuel economy tests which were conducted on the proving ground. The power performance of the self-dumping truck was evaluated through standing start acceleration time from 0 to 70km/h, overtaking acceleration time from 60 to 70km/h, maximum speed and maximum gradeability, while the composite fuel consumption per hundred kilometers was taken as an evaluation index of fuel economy. A L9 orthogonal array was applied to investigate the effect of three matching factors including engine, transmission and final drive, which were considered at three levels, on the power performance and fuel economy of the self-dumping truck. Furthermore, the grey relational grade was proposed to assess the multiple performance responses according to the grey relational analysis.

Wind noise, an aeroacoustic phenomenon, is an important attribute that influences customer sensation of interior quietness in a moving vehicle. As a vehicle travels faster, occupants' sensation of wind noise becomes increasingly objectionable. The purpose of this work is to investigate the increase of wind noise level perceived by a driver in response to an increase in wind speed. Specifically, it explores how much the level of wind noise at the DOE (driver outboard ear) would vary in response to a change in wind speed based on the test data obtained in a wind tunnel from ten vehicles that belong to several different passenger vehicle segments. The first part of this work studies the change of the SPL (sound pressure level) in response to a change in wind speed U. It shows that the SPL(dBA) approximately scales to U5.7 at the DOE and to U6.3 in the far-field, which could be interpreted as the dominance of dipoles.

Gear whine noise impacts customer perception of vehicle interior quietness in general and sound quality in particular. It has been a frequently occurred annoying phenomenon during vehicle development and much discussed topic regarding transmission NVH refinement in automotive industry. This work pertains to a transmission gear whine issue encountered in prototype evaluations during a vehicle program development process. The effort centers itself on the optimization of transmission gear macroscopic and microscopic parameters to fix the issue which is deemed unacceptable for customers. Specifically, by using multi-body dynamics approach, this work carries out a transmission system whine noise simulation based on optimal gear macro parameter selection and micro tooth flank modification. The obtained results show that the proposed design changes could successfully resolve the issue, which is verified by subsequent test measurement and confirmed by subjective evaluations.

A hybrid electric vehicle (HEV) will start the engine which drives its motor to charge the battery even at idle whenever the battery power is detected to be insufficient. The activation of idle battery charging could lead to serious NVH problems if powertrain parameters are not designed or calibrated properly. This work is focused on a noise issue encountered during idle charging for a specific prototype vehicle, and investigates control strategies to contain the noise level. Based on basic principles of automobile vibration and noise control along with the specific characteristics of the hybrid vehicle architecture, this work analyzes and elucidates methods of the engine idle charging noise control from the perspectives of powertrain modal alignment, idle speed optimization, and electric motor control algorithm.

The primary noise sources of electric vehicles differ from that of traditional vehicles due to the fundamental differences in their powertrain architecture. In this work, some exterior noise test methods for electric vehicles are briefly introduced first, which include a pass-by noise measurement method during acceleration on the proving ground as well as a similar measurement in a semi-anechoic room. The obtained results based on those two methods from a production electric vehicle are compared and analyzed. Then the mechanism of the source, path, and contribution is illustrated, and a model of path-source-contribution for electric vehicles is established. The model validation is subsequently carried out by correlating the calculated outcomes with the measured results under real operating conditions. Finally, by using the model, contribution analyses are carried out to identify the primary exterior noise sources.

This study presents a hybrid optimization approach of TOPSIS-based Taguchi method and entropy measurement for the determination of the optimal suspension parameters to achieve an enhanced compromise among ride comfort, road friendliness which means the extent of damage exerted on the road by the vehicles, and handling stabilities of a self-dumping truck. Firstly, the full multi-body dynamic vehicle model is developed using software ADAMS/Car and the vehicle model is then validated through ride comfort road tests. The performance criterion for ride comfort evaluation is identified as root mean square (RMS) value of frequency weighted acceleration of cab floor, while the road damage coefficient is used for the evaluation of the road-friendliness of a whole vehicle. The lateral acceleration and roll angle of cab were defined as evaluation indices for handling stability performance.

For the purpose of predicting the interior noise of a passenger automobile at middle and high frequency, an energy finite element analysis (EFEA) model of the automobile was created using EFEA method. The excitations including engine mount excitation and road excitation were measured by road experiment at a speed of 120 km/h. The sound excitation was measured in a semi-anechoic chamber. And the wind excitation was calculated utilizing numeric computation method of computational fluid dynamics (CFD). The sound pressure level (SPL) and energy density contours of the interior acoustic cavity of the automobile were presented at 2000 Hz. Meanwhile, the flexural energy density and flexural velocity of body plates were calculated. The SPL of interior noise was predicted and compared with the corresponding value of experiment.

In contrast to functionality and reliability, which are more and more assumed to be a natural and necessary condition of any vehicle, the performance of Noise, Vibration and Harshness (NVH) now belongs to those features which play an essential role for the customer's purchasing decision. Sound design and vehicle interior noise control are essential parts of NVH. One tool of the NVH solution toolbox is Active Noise Control (ANC). ANC technology aims to cancel unwanted noise by generating an “anti-noise” with equal amplitude and opposite phase. Owing to the fact that human hearing has selective sensitivity for different critical bands, a new control strategy of ANC, which selectively controls the noise of specific bandwidths according to the result of specific loudness and retains the part of noise created by the normal running of facilities, trying to attenuate the unwanted and unacceptable noise, has been proposed in this paper.

In recent years, the interior noise of automobile has been becoming a significant problem. In order to reduce the noise, porous materials have been widely applied in automobile manufacturing. In this study, the simulation method and optimal analysis are used to determine the optimum sound absorption of polyurethane foam. The experimental simulation is processed based on the Johnson-Allard model. In the model, the foam adheres to a hard wall. The incident wave is plane wave. The function of the model is to calculate the noise reduction coefficient of polyurethane foam with different thickness, density and porosity. The back propagation neural network coupled with genetic optimization technique is utilized to predict the optimum sound absorption. A developed back propagation neural network model is trained and tested by the simulation data.

Excessive wind noise is one of the most complained problems by owners of new vehicles as evidenced by JD Power Initial Quality Study (IQS) in recent years. After the vehicle speed surpasses 100 km/h, wind noise is gradually becoming the dominant noise source. In an effort to reduce aeroacoustic noise level, Beamforming (BF) is a very effective noise source identification technique used during vehicle wind noise development phases. In this work, based on the planar BF methodology, a large semi-circle microphone array is designed in accordance with the desired resolution and dynamic range pertaining to actual noise source distribution on a typical passenger vehicle. Acoustic array calibration and mapping deformation correction are accomplished by multi-point source method, and the Doppler Effect due to wind is corrected by the location calibration method.

A low frequency vibration issue around 3.2 Hz occurs during a commercial heavy truck program development process, and it is linked to extremely uncomfortable driving and riding experiences. This work focuses on an analytical effort to resolve the issue by first building a full vehicle MBS (multi-body-system) model, and then carrying out vibration response analyses. The model validation is performed by using full vehicle testing in terms of structural modes and frequency response characteristics. In order to resolve the issue which is excited by tire non-uniformity, the influence of the cab suspension, frame modes, front leaf spring system and rear tandem suspension is analyzed. The root cause of the issue is found to be the poor isolation of the rear tandem suspension system. The analytical optimization effort establishes the resolution measure for the issue.

A detailed multi-body dynamic model of a passenger car was modeled using ADAMS/Car and then checked by the ride comfort and handling stability test results in this paper. The performance criterion for ride comfort evaluation was defined as the overall weighted acceleration root mean square (RMS) value of car body floor, while the roll angle and lateral acceleration of car body were considered as evaluation indicators for handling stability performance. Simultaneously, spring stiffness and shock absorber damping coefficients of the front and rear suspensions were taken as the design variables (also called factors), which were considered at three levels. On this basis, a L9 orthogonal array was employed to perform the ride and handling simulations.

In this study, with the aim of reducing fuel consumption and improving power performance, the optimization for the driveline parameters of a self-dumping truck was performed by using a vehicle performance simulation model. The accuracy of this model was checked by the power performance and fuel economy tests. Then the transmission ratios and final drive ratio were taken as design variables. Meanwhile, the power performance of the self-dumping truck was evaluated through standing start acceleration time from 0 to 70km/h, maximum speed and maximum gradeability, while the combined fuel consumption of C-WTVC drive cycle was taken as an evaluation index of fuel economy. The multi-objective optimization for the power performance and fuel economy was then performed based on particle swarm optimization algorithm, and the Pareto optimal set was obtained. Furthermore, the entropy method was proposed to determine the weight of fuel consumption and acceleration time.

An issue of engine squealing in low temperature range (around −25°C) right after start-up emerged for a significant number of vehicles in a vehicle program. The earlier effort had focused on typical common culprits such as the pulley-belt systems etc. However, much effort, by subjective listening and guestimating hardware replacement, yielded no fruitful results for more than a year. The application of near-field acoustic holography on top of the engine in a climate chamber quickly identified several noise source locations. Further noise source identification effort on top, front and left side of the engine pinpointed the top three noise source locations: the idler pulley and water pump on front, the air compressor on right side, and the air intake throttle on top. Then a series of experiments were subsequently conducted, leading to a conclusion that the idler pulley was the source of the issue.

When a sunroof opens to let in fresh air while driving, there might be several noise issues associated with it. The most common and painful one is the wind throb issue, which is nevertheless largely resolved by implementing a sufficiently high wind deflector along the front edge of the sunroof. However, with the wind throb suppressed, other sound quality issues might emerge. The most notable one is the hissing noise issue, which becomes increasingly objectionable with the increase of vehicle speed. This work looks into the impact of sunroof deflector on interior sound quality with the consideration of wind throb, hissing noise and booming noise in terms of psychoacoustic attributes that could be felt subjectively. The goal is to achieve a better understanding of the sound quality associated with the sunroof deflector design, and inspire a balanced design, potentially targeting the most NVH demanding customers in the premium vehicle segment.

Wind noise is one of the most influential NVH attributes that impact customer sensation of vehicle interior quietness. Among many factors that influence wind noise performance, the amount of dynamic door deflection under the pressure load due to fast movement of a vehicle plays a key roll. Excessive deflection could potentially lead to loss of sealing contact, causing aspiration leakage, which creates an effectual path through which the exterior aerodynamically induced noise propagates into the vehicle cabin. The dynamic door deflection can be predicted using CFD and CAE approaches which, in addition to modeling the structure correctly, require a correct pressure loading composed of external and internal pressure distributions. The determination of external pressure distributions can be fulfilled fairly straightforward by using commercial CFD codes such as Fluent, Star CCM+, Powerflow and others.

Noise excitation sources are different between electric vehicles and conventional vehicles due to their distinct propulsion system architecture. This work focuses on an interior noise contribution analysis by experimental measurements and synthesis approach using a methodology established based on the principle of noise path analysis. The obtained results show that the structure-borne noise from the tire-road excitation acts as a major contributor to the overall interior noise level, and the structure-borne noise from the power plant system contributes noticeably as well, whereas contributions from the electric motor and tire are relatively insignificant.

With significant improvement in overall vehicle NVH performance in recent years, sound quality has increasingly become an important customer concern. In search of excellence in customer satisfaction with BEVs (Battery Electric Vehicles), optimizing e-motor noise is one of the most effective ways to improve the vehicle sound quality. This paper aims to resolve a whining noise issue from a PMSM (Permanent Magnet Synchronous Motor) during the development of a BEV program. The most critical order of the whining noise from an e-motor on market today is most likely the 48th order, which could be mitigated by implementing sound package material on the motor casing. In this work, however, the 8th order instead is found to be the most critical issue, and it is perceived as the whining noise in the frequency range under 600Hz. With such a low frequency content, sound package treatment has little effect.