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Feeling and hearing the results of engineering decisions immediately via a “virtual car” - simultaneous engineering - can significantly shorten vehicle development time. Sound quality and discrete vibration at the driver's position may be predicted and “driven” before the first prototype is built. Although sound cannot yet be predicted in an unknown chassis, the sound and vibration behavior resulting from a new engine, never previously installed in a given vehicle, may be predicted, heard binaurally and felt in an interactive “drivable” simulation based on transfer path analysis. Such a simulation, which includes the binaural sound field and discrete vibration of steering wheel and seat, can also include wind and tire noise to determine if certain engine contributions in sound and vibration may be masked.

Besides powertrain and aerodynamic noise, tire-road noise is an important aspect of the acoustic comfort inside a vehicle. For the subjective evaluation of different tires or vehicles in a benchmark, authentic sound examples are essential. They should be recorded on a real road rather than on a roller dynamometer (avoiding artificial and periodic sounds, especially in the case of a small roller circumference and a smooth surface). The challenge of on-road measurements is the need for separating the components of the interior noise generated by rolling tires, aerodynamic flow and powertrain. This allows for individual judgment of the noise shares. A common approach for eliminating the engine sound is shutting the engine off after acceleration to the desired maximum speed. Operational Transfer Path Analysis (OTPA) can then be used to auralize the tire-road noise at a certain receiver location, where an artificial head records the interior noise during this coast-down.

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.

For many years in vehicle and other product noise assessments, tonality measurement procedures such as the Tone-to-Noise Ratio, Prominence Ratio and DIN 45681 Tonality have been available to quantify the audibility of prominent tones. Especially through the recent past as product sound pressure levels have become lower, disagreements between perceptions and measurements have increased across a wide range of product categories including automotive, Information Technology and residential products. One factor is that tonality perceptions are caused by spectrally-elevated noise bands of various widths and slopes as well as by pure tones, and usually escape measure in extant tools. Near-superpositions of discrete tones and elevated narrow noise bands are increasingly found in low-level technical sounds. Existing pure-tone methodologies tend to misrecognize an elevated noise band as general masking lowering the audibility of a tone in the measured vicinity, whereas perceptually they add.

With the exact knowledge of the current positions of the microphones in an array and the potential noise sources, it is possible to compensate a relative motion between them. In the past, techniques exploiting this knowledge have been used successfully, e.g., for the measurement of wind turbines and airplane flyover measurement. In this paper, these ideas are applied and modified for the development of a traffic flow observation system. The main purpose of a vehicle pass by measurement is to extract the continuous noise levels of the dominant sources. With the use of advanced video processing or additional sensor information (radar, light barrier) it is possible to create a continuous tracking model of the vehicle. The scan grid in the beam forming algorithm is then recalculated to compensate the movement. In the resulting acoustic video, the vehicle is fixed and the evolution of the sound sources can be observed and auralized for psychoacoustic evaluations.

Predominant brake noise evaluation and rating was developed many years ago and no longer fulfills the need of modern development work. An extended description of a noisy brake event (European expert group guideline EKB 3006) and a standardized test data exchange format, allowing the comparison of different source test results (EKB 3008) are presented. Today's noise rating systems are described and compared by selected examples. The paper proposes an open 4 level noise rating system (EKB 3007). It starts with simple occurrence statistics, noise rating based on sound levels, situational noise rating including duration and finally based on the human perception, described by psychoacoustics.

Since currently a technological shift from automobiles with internal combustion engines now to electric vehicles occurs, new challenges in vehicle acoustics must be met. Although, one of the core duties of NVH engineers will still be the prevention and treatment of disturbing noises, the targeted creation of intended and designed sounds will gain in importance significantly. This sound design task is no longer a choice but a necessity. In the scope of hybrid and electric cars a new kind of acoustic feedback must be created. Surely, the simple electric motor sound, the “tram on wheels”, will not be the final solution accepted by customers. Besides the mandatory use of technical methods like transfer path analysis enabling the reliable identification of the reasons for acoustical problems by separation of sources and transfer paths or binaural panel contribution analysis, investigations of customer preferences on the basis of simulated and real test drives will become more important.

The challenges concerning noise, vibration, and harshness (NVH) performance in the vehicle cabin have been significantly changed by the powertrain shift from a conventional drive unit with an internal-combustion engine (ICE) to electric drive units (eAxles). However, there is few research regarding the impact of electrification on NVH considering the influence of the context such as multi-stimuli and traffic rules during a real-life driving. In this study, the authors conducted test drives using EVs and ICEVs on public roads in Europe and conducted a statistical analysis of the difference in driver impression of NVH performance based on interviews during actual driving. The impression data were categorized into clusters corresponding to related phenomena or features based on driver comments. Furthermore, the vehicles data (vehicle speed, acceleration, GPS information, etc.) were recorded to associate the driver impressions with the vehicle’s conditions when the comments were made.

Diesel impulsiveness (so called Diesel knocking) present in the cabin of diesel vehicles is perceived as unpleasant because of its impulsive time structure. JD Power data clearly show the customers preference of vehicles with little Diesel knocking over those with severe knocking. Corresponding objective descriptors that reflect the customers' perception are introduced. The occurrence of such noise patterns is influenced by the combustion process itself as well as by all excited mechanical components within the power train. Further the transfer characteristics of the engine structure and various vehicle noise paths do contribute to a poor Diesel Sound Quality. It is essential that all these factors have to be considered in combination. This paper provides an overview about suitable methods and technologies, including Binaural Transfer Path Analysis and Synthesis. The potential of the approach is demonstrated by an example.

Interior vehicle sound is an important factor for customer satisfaction. To achieve an optimized product sound at an early stage of development, subjective evaluation methods as well as analysis and prediction tools must be combined to provide reliable information relevant to product quality and comfort judgments. Binaural Transfer Path Synthesis (BTPS) is a well-known method to calculate interior noise and vibrations based on multi-channel input measurements. Recent enhancements of the BTPS method enable taking into account also simulated excitations, for example engine mount vibrations calculated using MBS and/or FEM simulations, allowing the prediction of interior noise even if the engine is not available in hardware. Interactive evaluation of the generated sounds in a vibro-acoustic driving simulator helps to increase understanding of customer responses and perception of target sounds.

The development of a new method to evaluate the NVH quality of diesel combustion noise bases upon following questions by regarding typical driving modes: Driving behavior with diesel vehicles Which driving situation causes an annoying diesel combustion noise Judgment of diesel combustion noise as good or bad A suitable test course was determined to regard typical driving situations as well as the European driving behavior. Vehicles of different segments were tested on that course. The recorded driving style and the simultaneously given comments on the diesel combustion noise results to a typical driving mode linked to acoustics sensation of diesel combustion noise. The next step was to simulate this driving mode on the chassis dynamometer for acoustical measurements. The recordings of several vehicles were evaluated in listening test to identify a metric. The base of metric was objective analyses evaluating diesel combustion noise in relevant driving situations.

Clearly, sound quality evaluation has become a central focus for assuring customer satisfaction. To achieve an optimized product sound at an early stage of development, subjective evaluation methods must be combined with analysis and prediction tools to provide reliable information relevant to product quality judgments. Some years ago, a “Hearing Model” was developed explaining and describing many psychoacoustic effects [1], [2], and allowing for roughness calculation in accordance with subjective listening tests [3]. Existing roughness models work well for synthetic signals such as modulated tones or noise signals, but it is challenging to predict roughness for engine sounds because of their more complex spectral and temporal noise patterns [4].

Vehicle interior noise can be regarded as the sum of all panel contributions which enclose the compartment. In order to experimentally investigate the sound contributions of individual panels, the so-called “window method” is often used. Due to some fundamental drawbacks, a new method has been invented which is considered a useful alternative. The theoretical background of the method is covered in this paper, as well as application examples illustrating the performance and advantages of this new technique.

Sound quality of vehicle is more and more an important product feature which significantly influences the perceived product quality. Over recent years, the broad variety of new models, which resulted in increased competition, has lead to rising customer demands with regard to NVH (Noise, Vibration and Harshness) aspects. Apart from the indispensable troubleshooting, the acoustic engineer's scope of work is extended to NVH design engineering. Thus, innovative, ambitious measurement technologies were developed to meet these new, challenging tasks and to maintain a competitive advantage.

Pass-by measurements on a test track are a standard test procedure for every new vehicle. Since there are only a few test tracks and the measurements are depending on the environmental conditions two indoor test procedures have been developed using a chassis dynamometer in a semi anechoic chamber. The first procedure delivers the standard pass-by analyses as well as monaural and binaural time signals using a far field array measurement. The second procedure delivers more detailed information about the different noise sources at the vehicle. Near field measurements of the main noise sources of the vehicle are combined with the airborne transfer functions between these sources and a far field observer position to get a simulated far field microphone signal of the whole vehicle or any set of components

Quantifying tonalities in technical sounds according to human perception is a task of growing importance. The psychoacoustic tonality method, published in the 15th edition of the ECMA-74 standard, is a new method that is capable of calculating the perceived tonality of a signal. Other methods, such as Prominence Ratio or Tone-to-Noise Ratio do not consider several essential psychoacoustic effects. The psychoacoustic tonality is based on a model of human hearing and thus is able to model human perception better than other methods. The algorithm described in ECMA-74 calculates tonality over time and frequency. In practice, tonalities often originate from rotating components, for example, parts of an electric motor. In these cases, quantification of the tonality of orders is often more interesting than the tonality over frequency. In this paper, an extension of the psychoacoustic tonality according to ECMA-74 is presented.

Vehicle interior noise consists of a superposition of broadband contributions from powertrain, wind, and tire-road noise. Tire-road noise has become increasingly important referring to overall acoustic comfort, especially for (luxury) sedans with pleasant low-noise engine sounds. An interior noise recording during a coast-down (engine switched off) contains different components: a mixture of wind along with airborne and structure-borne tire-road noise shares. Separating the mixture into these components requires appropriate algorithms and additional measurements. Therefore, structure-borne excitation signals as well as the airborne noise radiation of all four tires are measured simultaneously to an artificial head recording in the vehicle interior during a coast-down test from maximum vehicle speed to standstill.

To empirically estimate the radiation of sound sources, a measurement with microphone arrays is required. These are used to solve an inverse problem that provides the radiation characteristics of the source. The resolution of this estimation is a function of the number of microphones used and their position due to spatial aliasing. To improve the radiation resolution for the same number of microphones compared to standard methods (Ridge and Lasso), a method based on normalizing flows is proposed that uses neural networks to learn empirical priors from the radiation data. The method then uses these learned priors to regularize the inverse source identification problem. The effects of different microphone arrays on the accuracy of the method is simulated in order to verify how much additional resolution can be obtained with the additional prior information.

Today several international brake acceptance tests exist, like the Los Angeles City Traffic test (LACT) or the Mojacar Noise Route in Spain. During these tests noise evaluation is done subjectively by test drivers, which can cause discrepancies. Sometimes noise data are recorded and evaluated by different, mostly company-specific methods but a procedure that considers the human perception of brake squeal is missing. To fill this gap, the procedure BONI (Brake Objective Noise Index) detailed in this paper is developed based on subjective ratings acquired in hearing tests. It provides reliable prediction of squeal annoyance with high correlation to human perception.

Since the brake colloquium in 2004 the role of climatic conditions and their relations to noise occurrence, sound pressure level and friction coefficient level is widely discussed in the US and European working groups on brake noise. A systematic study has been started to investigate the influence of relative humidity, absolute humidity and temperature on brake noise and the corresponding friction coefficient level. In this study an enormous effort was taken to keep the influences of the brake parameters, e.g. lining material, Eigenfrequencies and dimensions of the different components as small as possible to investigate the climatic influence only. Strategic humidity and temperature levels were tested according to the Mollier-Entropy-Enthalpy-Diagram which are corresponding to the seasons in the various international regions. A regression analysis evaluates the correlation and the influence of each parameter to noise and friction coefficient level.