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This course is offered in China only and presented in Mandarin Chinese. Sound quality is one of the most important desired attributes for the customers, and road noise is one of the most vital factors influencing sound quality. Road noise is the major interior noise source, especially for EVs, and it is a common customer complaint. In order to improve the customers’ satisfaction and market share, almost all the OEMs have spent lots of sources on the road noise attenuation.

Abstract The high-frequency noise issue is one of the most significant noise, vibration, and harshness problems, particularly in battery electric vehicles (BEVs). The sound package treatment is one of the most important approaches toward solving this problem. Owing to the limitations imposed by manufacturing error, assembly error, and the operating conditions, there is often a big difference between the actual values and the design values of the sound package components. Therefore, the sound package parameters include greater uncertainties. In this article, an uncertainty analysis method for BEV interior noise was developed based on an interval model to investigate the effect of sound package uncertainty on the interior noise of a BEV. An interval perturbation method was formulated to compute the uncertainty of the BEV’s interior noise.

Abstract Correlation analysis of subjective and objective evaluation for automobile sound quality is an important topic in automobile technology fields. In view of the deficiency of multi-dimensional linear regression analysis and the theoretical merits of grey system method, grey comprehensive relational degree was calculated to analyze the contribution of objective evaluation data to subjective evaluation. The main objective variables affecting the subjective feeling were determined. The variables include loudness, sharpness and shaking degrees. Grey GM(0,4) model was established as a quantitative expression for describing the subjective and objective evaluation correlation. The results of residual test and posterior-variance-test show that the established model was accurate and the model can be used to analyze and predict subjective and objective evaluation data of automobile sound quality.

Abstract In the selection of automobile noise reduction schemes, the lack of comprehensive quantitative index system and objective evaluation method is a serious problem. In this article, the methods of analytic hierarchy process (AHP) and gray target decision were used to solve the problem. Firstly, AHP and gray target decision method were introduced respectively in detail. Secondly, three automobile noise reduction schemes were illustrated. Four types of data were selected as the decision indexes, and the weight coefficients of all the decision indexes were calculated using the AHP. Then multi-attribute mixed weighted gray target decision model was established. The optimum scheme was obtained by the calculation of the off-target distance and the sorting of the calculation results. The proposed method can quantify the evaluation process and overcome the disadvantages of the traditional analogy method. The example shows that the method is feasible.

NVH refinement is an important aspect of the powertrain development and vehicle integration process. The depletion of fossil-based fuels and increase in price of gasoline have prompted most vehicle manufacturers to embrace propulsion technologies with varying degrees and types of hybridization. Many different hybrid vehicle systems are either on the market, or under development, even up to all-electric vehicles. Each hybrid vehicle configuration brings unique NVH challenges that result from a variety of sources. This paper begins with an introductory discussion of hybrid propulsion technologies and associated unique vehicle NVH challenges inherent in the operation of such hybrid vehicles. Following this, the paper outlines a two-dimensional landscape of typical customer vehicle maneuvers mapped against hybrid electric vehicle (HEV) operational modes.

The high cost and competitive nature of automotive product development necessitates the search for less expensive and faster methods of predicting vehicle performance. Continual improvements in High Performance Computing (HPC) and new computational schemes allow for the digital evaluation of vehicle comfort parameters including wind noise. Recently, the commercially available Computational Fluid Dynamics (CFD) code PowerFlow, was evaluated for its accuracy in predicting wind noise generated by an external automotive tow mirror. This was accomplished by running simulations of several mirror configurations, choosing the quietest mirror based on the predicted performance, prototyping it, and finally, confirming the prediction with noise measurements taken in an aeroacoustic wind tunnel. Two testing methods, beam-forming and direct noise measurements, were employed to correlate the physical data with itself before correlating with simulation.

In response to the growing demand for fuel economy, we are developing a high-efficient variable displacement pump for hydraulic power steering systems. In order to develop a quiet variable displacement pump which generates lower noise for better vehicle interior sound quality, we have been developing a simulation tool which includes hydraulic analysis, vibration analysis, and vehicle interior noise analysis which combines simulation outputs and measured noise transfer functions of the targeted vehicle. This paper provides both validation results of the simulation tool and application examples to design improvement to conclude the effectiveness of the simulation tool developed.

For most car manufacturers, wind noise from the greenhouse region has become the dominant high frequency noise contributor at highway speeds. Addressing this wind noise issue using experimental procedures involves high cost prototypes, expensive wind tunnel sessions, and potentially late design changes. To reduce the associated costs as well as development times, there is strong motivation for the use of a reliable numerical prediction capability early in the vehicle design process. Previously, a computational approach that couples an unsteady computational fluid dynamics solver (based on a Lattice Boltzmann method) to a Statistical Energy Analysis (SEA) solver had been validated for predicting the noise contribution from the side mirrors. This paper presents the use of this computational approach to predict the vehicle interior noise from the windshield wipers, so that different wiper placement options can be evaluated early in the design process before the surface is frozen.

Wind noise is a significant source of interior noise in automobiles at cruising conditions, potentially creating dissatisfaction with vehicle quality. While wind noise contributions at higher frequencies usually originate with transmission through greenhouse panels and sealing, the contribution coming from the underbody area often dominates the interior noise spectrum at lower frequencies. Continued pressure to reduce fuel consumption in new designs is causing more emphasis on aerodynamic performance, to reduce drag by careful management of underbody airflow at cruise. Simulation of this airflow by Computational Fluid Dynamics (CFD) tools allows early optimization of underbody shapes before expensive hardware prototypes are feasible. By combining unsteady CFD-predicted loads on the underbody panels with a structural acoustic model of the vehicle, underbody wind noise transmission could be considered in the early design phases.

The turbulent boundary layer (TBL) that forms on the outside of a commercial airplane in flight is a significant source of noise. During cruise, the TBL can be the dominant source of noise. Because it is a significant contributor to the interior noise, it is desirable to predict the noise due to the TBL. One modeling approach for the acoustic prediction is statistical energy analysis (SEA). This technique has been adopted by North American commercial airplane manufacturers. The flow over the airplane is so complex that a fully resolved pressure field required for noise predictions is not currently analytically or numerically tractable. The current practice is to idealize the flows as regional and use empirical models for the pressure distribution. Even at this level of idealization, modelers do not agree on appropriate models for the pressure distributions. A description of the wall pressure is insufficient to predict the structural response. A structural model is also required.

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.

Traditional IC engine powered vehicles readily mask the lower amplitude transients like hiss, clicks and slight tones. However, due to the popularity and expected increase in number of hybrid and electrically driven automobiles all around the world, all the OEMs are concerned about the vehicle interior quietness and sound quality. Refrigerant system induced hiss/gurgle is quite normal phenomenon but its excessive levels and frequent occurrences can be objectionable to demanding customers. Introduction of new environmentally friendly refrigerants (HFO-1234yf), A/C compressors with built-in oil separation (reduced damping in evaporator), and use of IHX with coupled suction and liquid lines, may lead to increased refrigerant system induced hiss and gurgle. Operation of typical A/C system generates steady-state and transient noises that consist of audible tones and high frequency hiss.

System and component target setting for noise and vibration are important activities within automotive product development. New challenges arise when electric motors are introduced into cars traditionally powered by combustion engines. The emitted noise from an electric traction motor for hybrid electric vehicles is characterized by high frequency tonal components from the dominating magnetic harmonics which can be perceived as annoying. Sound power is frequently used for quantifying the airborne noise from rotating electrical machines. This paper describes the process of determining the radiated sound power from an automobile electric rear axle drive in-situ and its contribution to interior cabin noise for a prominent rotor order. The sound power was calculated by combining the average stator surface vibration velocity together with an estimate of the radiation efficiency.

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.

Low interior noise levels in combination with a comfortable sound is an important task for passenger cars. Due to the reduction of many noise sources over the last decades, nowadays tire-road noise has become one of the dominant sources for the interior noise. Especially for manufactures of luxury cars, the reduction of tire-road noise is a big challenge and therefore a central part of NVH development. The knowledge of the noise transmission behavior based on the characteristics of the relevant sources is a fundamental of a modern NVH - development process. For tire-road noise the source characteristics can be described by wheel forces and radiated airborne noise. In combination with the related vehicle transfer functions it is possible to describe the noise transmission behavior in detail. A method for estimating wheel forces and radiated airborne noise is presented.

The NVH study of trimmed vehicle body is essential in improving the passenger comfort and optimizing the vehicle weight. Efficient modal finite-element approaches are widely used in the automotive industry for investigating the frequency response of large vibro-acoustic systems involving a body structure coupled to an acoustic cavity. In order to accurately account for the localized and frequency-dependant damping mechanism of the trim components, a direct physical approach is however preferred. Thus, a hybrid modal-physical approach combines both efficiency and accuracy for large trimmed body analysis. Dynamic loads and exterior acoustic loads can then be applied on the trimmed body model in order to evaluate the transfer functions between these loads and the acoustic response in the car compartment.

In the vehicle development process, targets are defined to fulfill customers' expectations on acoustic comfort. The interior complete vehicle acoustic targets can be cascaded down to system and component targets, e.g. insulation properties and source strengths. The acoustic transfer functions (ATFs) from components radiating airborne noise play a central role for the interior sound pressure levels. For hybrid vehicles fitted with an electric traction motor, the contribution of high frequency tonal components radiated from the motor housing needs to be controlled. The interior sound pressure due to an airborne motor order can be estimated by surface velocities and ATFs. This study addresses the ATFs measured from a large number of positions located around an electric rear axle drive (ERAD) and their influence on estimated interior noise. First, the magnitude variation between the individual ATFs and how it clearly can be visualized was presented.

Taguchi method is a technology to prevent quality problems at early stages of product development and product design. Parameter design method is an important part in Taguchi method which selects the best control factor level combination for the optimization of the robustness of product function against noise factors. The air induction system (AIS) provides clean air to the engine for combustion. The noise radiated from the inlet of the AIS can be of significant importance in reducing vehicle interior noise and tuning the interior sound quality. The porous duct has been introduced into the AIS to reduce the snorkel noise. It helps with both the system layout and isolation by reducing transmitted vibration. A CAE simulation procedure has been developed and validated to predict the snorkel noise of the porous ducted AIS. In this paper, Taguchi's parameter design method was utilized to optimize a porous duct design in an AIS to achieve the best snorkel noise performance.

The contribution of wind noise through the glasses into the vehicle cabin is a large source of customer concern. The wind noise sources generated by turbulent flow incident on the vehicle surfaces and the transmission mechanisms by which the noise is transmitted to the interior of the vehicle are complex and difficult to predict using conventional analysis techniques including Computational Fluid Dynamics (CFD) and acoustic analyses are complicated by the large differences between turbulent pressures and acoustic pressures. Testing in dedicated acoustic wind tunnel (AWT) facilities is often performed to evaluate the contribution of wind noise to the vehicle interior noise in the absence of any other noise sources. However, this testing is time-consuming and expensive and test hardware for the vehicle being developed is often not yet available at early stages of vehicle design.

In a vehicle's development process, Transfer Path Analysis (TPA) is commonly used for identifying sound sources and their transmission to a receiver. Forces acting on the structure are the reason for the structure-borne sound share of the vehicle interior noise. In practice it is not possible, or too extensive, to measure operational forces directly. Instead, they are often calculated indirectly from accelerations and from additionally measured inertances. As the car body is a strongly coupled system, a force acting at one position results in accelerations throughout the structure. This crosstalk must be considered by using a dense inertance matrix consisting of the ratios between each force excitation and the accelerations at every sensor position. Then a matrix inversion is performed to solve the system of equations describing the coupling of the structure.