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E-vehicles can generate strong tonal components that may disturb people inside the vehicle. However, such components, deliberately generated, may be necessary to meet audibility standards that ensure the safety of pedestrians outside the vehicle. A tradeoff must be made between pedestrian audibility and internal sound quality, but any iteration that requires additional measurements is costly. One solution to this problem is to modify the recorded signals to find the variant with the best sound quality that complies with regulations. This is only possible if there is a good separation of the tonal components of the signal. In this work, a method is proposed that uses the High-resolution Spectral Analysis (HSA) to extract the tonal components of the signal, which can then be recombined to optimize any sound quality metric, such as the tonality using the Sottek Hearing Model (standardized in ECMA 418-2).

The powertrain noise of cars has been reduced in the last decades. Therefore in many cases, rolling tires have increasingly become the dominant sources of vehicles' interior noise. For sound design or a reduction of tire-road noise it is important to know the individual noise shares of the tires and their transfer paths. Authentic tire-road noise can only be measured on a real road, not on a roller dynamometer. So far measurements have been performed during a coast-down on the road with the engine switched off, avoiding the influence of engine noise. Operational Transfer Path Analysis (OTPA) can be used to remove the uncorrelated wind noise, and to synthesize structure-borne and airborne tire-road noise based on input signals measured with microphones at the tires and a triaxial accelerometer at each wheel carrier. Simultaneously, the interior noise is recorded by an artificial head.

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.

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.

Sound quality of vehicles has become very important for car manufacturers. This feature is interpreted as among the most relevant factors regarding perceived product quality. Since the development cycles in the automotive industry are constantly reduced to meet the customers' demands and to react quickly to market needs, ensuring product sound quality is becoming increasingly difficult. Moreover, new drive and fuel concepts, tightened ecological specifications, increase of vehicle classes and increasing diversification, etc., challenge the acoustic engineers trying to create and preserve a pleasant, adequate, harmonious passenger cabin sound. Another aspect concerns the general pressure for reducing emission and fuel consumption, which lead to vehicle weight reductions through material changes also resulting in new noise and vibration conflicts.

The sound sources of modern road vehicle can be classified into three components, driving sound (sound generated through normal driving patterns and events), operating sound (sound generated through actuated components not related to driving), and generated synthetic sound (electronic warning / interactive feedback). The characteristic features of these sounds are dependent upon customer expectation and usage requirements. Additional development complexities are introduced due to each market's cultural and regional differences. These differences in preference must be considered for the establishment of the target sound quality in the early vehicle development process. In this paper, a sound quality goal setting procedure based on user preference is introduced. The sound targets are created as a result of the user preference investigation and validated by intercultural comparison.

Since NVH is always a property of the whole system, one must have a deep understanding of the dependencies and all the components that interact. The well known in-situ Transfer Path Analysis (TPA) provides methods to separate different components of an acoustical system such as source and receiver. The source including excitation and structural dynamics of the exciting subsystem can be described independently of the structural dynamics of the receiving structure by means of the in-situ blocked forces. The Experimental Modal Analysis (EMA) is a common method as well and aims to identify the structural dynamics of a structure. This paper addresses the combination of both methods using the example of an e-drive of an electric car, which has been analyzed on a test rig. The combination of modal analysis and TPA yields a better understanding of the system and its dependencies.

In the engine development process, the ability to judge NVH comfort as early as possible is a great benefit. The prediction of engine noise on the basis of a prototype engine without the need to install it in a real car significantly speeds up the development process and leads to a cost reduction, as prototype modifications can be evaluated faster. Meaningful predictions of the perceived NVH comfort cannot be achieved just by comparing order levels, but require listening to an auralization of the engine noise at the driver's position. With the methods of Transfer Path Analysis and Synthesis (TPA/TPS) a prototype engine can be virtually installed in a car using test-bench data. The interior noise can be estimated by combining source signals containing near-field airborne noise radiation and mount forces with transfer functions describing the transmission to the target position in the cabin.

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.

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.

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.

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

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.

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.

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].

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.

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.

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.

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.

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.