Browse Topic: Noise
This study compares two international loudness standards—International Organization for Standardization (ISO) 532-1:2017 (Zwicker) and ISO 532-3:2023 (Moore–Glasberg–Schlittenlacher)—for assessing vehicle wind noise using wind tunnel measurement data. The two methods demonstrate good agreement in ranking wind noise quietness at typical highway speeds (120–140 km/h). However, discrepancies arise under specific conditions due to ISO 532-3’s binaural processing, which accounts for interaural inhibition effects. These differences are particularly pronounced in vehicles with asymmetric wind noise levels between the driver and passenger sides. Power-law regression analysis reveals that binaural loudness at the driver side (ISO 532-3) increases at a slower rate with wind speed compared to the monaural measurements [driver outboard ear (DOE)] of ISO 532-1. Under yaw angle conditions, the perceptual contribution of the driver inboard ear (DIE) introduces subtle metric-dependent variations. Contribution analyses further highlight method-specific sensitivities to noise source location and direction. Overall, this study provides a valuable reference for implementing loudness metrics in automotive wind noise engineering.
It was reported earlier that the wear differential between the inboard pad and the outboard pad leads to brake squeal generation. The (inboard/outboard) pads wear differential can occur due to hardware issues such as brake pad drag and/or two different wear rates of the (I/O) pads, which is caused by two different material properties of the pads although the pad formula may be the same. It is found that (I/O) pads compressibility differential/hardness differential/friction differential are all interrelated and that they contribute to brake squeal generation in addition to the inboard pad tangential/radial taper wear. A method has been found to separate the inboard pad friction and the outboard pad friction and to estimate each friction coefficient.
Moan noise is a low-frequency noise occurring in the 170–500 Hz frequency ranges. While it frequently appears in vehicles equipped with a rear Coupled Torsion Beam Axle (CTBA), the exact cause, generation mechanism and clear solutions remain unidentified. For those reasons, we have developed a moan noise analysis method capable of representing the moan noise phenomenon in vehicles with rear CTBA along with an automation tool. From these results, we can use moan analysis models to reduce real moan noise problems. Consequently, this not only enhances customer satisfaction and vehicle quality but also significantly increases the work efficiency of vehicle designers through design modification in the preliminary stages of vehicle development
Steady advancement is observed in global research on eco-friendly and sustainable transportation. Rapid technological evolution of hybrid electric vehicles (HEVs) is documented. Lower overall noise output and more compact structures are achieved in HEV engines relative to conventional internal combustion engines. The perceptibility of harmonic impulsive sounds is significantly enhanced by these design characteristics. A close correlation is observed between these acoustic phenomena and negative human auditory perceptions. These events are treated as a core focus for HEV noise, vibration, and harshness optimization. Accurate quantification of harmonic impulsive sounds is not achieved by conventional objective indicators. A favorable balance between reliability and accuracy is not established by existing subjective prediction models. Practical engineering applications of these methods are severely restricted. A novel objective quantification method for harmonic impulsive sounds is proposed in this study. The method is established based on time–frequency masking theory and tonal strength. Bench tests in a semi-anechoic chamber and subjective evaluation experiments with standardized rating scales are performed for data collection. Collected sound signals are decomposed through an integrated approach of wavelet transform and variational mode decomposition. Targeted feature extraction is completed for harmonic impulsive sounds. A quantitative index incorporating human auditory temporal and frequency masking effects is developed. The proposed index exhibits a significantly stronger correlation with subjective evaluation results than traditional objective metrics, confirming its superior ability to reflect actual perceived sound quality. An interval prediction model for sound quality evaluation is established based on support vector machines and kernel density estimation. Traditional objective metrics and the proposed index are introduced as key input parameters. Effective and reliable prediction of HEV engine noise subjective satisfaction is achieved by the model.
Tire exterior noise has become increasingly critical in vehicle acoustics due to two key developments: updated pass-by noise regulations, which amplify the relative contribution of tire noise, and the rise of Battery Electric Vehicles (BEVs), which lack traditional powertrain noise. Design trends in BEVs—such as increased vehicle mass from battery packs and the widespread use of large-diameter, wide, low-profile tires—further intensify tire noise due to stiffer constructions and altered contact dynamics. A common method for predicting tire noise is the source-transfer-receiver model, where the tire is represented by a set of monopoles with volume velocity Q derived from near-field measurements. Acoustic propagation is modeled via p/Q transfer functions. Despite its simplifications, this approach is practical for vehicle development, enabling clear separation between source and transfer mechanisms and facilitating targeted noise control strategies. In previous work, we proposed a rigorous framework to optimize both the spatial distribution and strength of the monopole sources. Positions were identified using an L1-norm regularization via the Lasso algorithm, promoting sparsity and physical interpretability. Strengths were estimated using an L2-norm Tikhonov regularization, which stabilizes the solution against measurement noise. While the Tikhonov regularization parameter was previously tuned manually through trial and error, we now enhance predictive accuracy by selecting it via a cross-validation technique, ensuring a more robust and data-driven optimization. Besides this, compared to the previous work the approach here is validated for the prediction of both indoor and outdoor pass-by noise, as well as for multiple tire types providing different noise levels. Results demonstrate the method’s robustness, accuracy, and applicability for acoustic development in modern vehicle platforms.
Electric vehicle subsystems, including powertrains, electric motors, and gearboxes, pose new challenges in achieving stringent acoustic performance targets for both interior and exterior noise. These challenges are intensified by increasingly demanding customer expectations regarding interior acoustic comfort, which encompasses the reduction of intrusive noise sources and the enhancement of overall sound quality across a broad frequency spectrum. A primary concern associated with electric vehicles subsystems is the generation of high-frequency tonal noise, commonly referred to as whine noise, which can significantly impact acoustic performance and passenger comfort. High-frequency whine noise propagates through multiple transmission paths and can be effectively attenuated at the source through encapsulation strategies, which also contribute to broadband noise reduction across a wide frequency spectrum. To predict the acoustic performance of encapsulation, a coupled simulation approach combining the Boundary Element Method (BEM), the Finite Element Method (FEM) and the Poroelastic Finite Element Method (PEM) has been developed. This methodology has been already presented and validated through experimental measurements, demonstrating its acoustic effectiveness in the encapsulation of a generic electric motor housing. While BEM is well-suited for modeling exterior acoustic propagation, standard implementations encounter limitations at high frequencies due to mesh density requirements and computational cost. This work presents hybrid parallelization strategies that integrate frequency-domain decomposition with multi-threading to accelerate BEM H-matrix computations. Frequency decomposition enables parallel processing by distributing independent frequency tasks across multiple processes, while multi-threading enhances performance for fine-grained operations such as matrix assembly and H-matrix compression within each frequency. The processes and improvements enabled by these strategies are discussed and presented within an adapted high-performance computing (HPC) environment.
In the automotive industry, controlling noise transmission through vehicle components is essential for passenger comfort and regulatory compliance. Traditionally, Transmission Loss (TL) is estimated using simplified CAD-based metrics, which lack accuracy at high frequencies and for complex assemblies. Modeling complex vehicle components introduces challenges, such as representing fluid-structure and trim interactions, with spatially varying trim thicknesses. This study presents an industrial application implementing the Virtual SEA (Statistical Energy Analysis) method to evaluate TL for a firewall. The study discusses strategies for subsystem adaptation and analytical trim modeling, highlighting the importance of managing spatial averaging effects. The proposed workflow integrates laboratory measurements of trim materials, advanced subsystem definition, diffuse sound field (DSF) excitation and radiation in free-field condition. Virtual SEA results are systematically validated against Finite Element Method (FEM) simulations (where the frequency range allows) and experimental data. Virtual SEA demonstrates strong agreement with FEM, especially at mid and high frequencies where FE starts to be cumbersome, confirming its suitability for industrial Noise, Vibration, and Harshness (NVH) applications. While some limitations remain—such as the inability to fully model mixed-component subsystems—ongoing research and practical workarounds are proposed. In conclusion, the Virtual SEA approach enables accurate and efficient TL prediction for vehicle components up to higher frequencies that FEM can achieve, supporting NVH targets and facilitating knowledge transfer to engineering teams. This work advances simulation-based acoustic transparency analysis for modern automotive design.
Vehicle electrification and accelerated development cycles create a need for virtual Noise, Vibration and Harshness (NVH) development tools which are fast, precise and, seamlessly interchangeable between development sites, suppliers and OEMs. Component-based Transfer Path Analysis (C-TPA), standardized in ISO 20270:2019, enables independent component characterization and integration with virtual models to predict sound and vibration in new assemblies, referred to as Virtual Prototype Assemblies (VPA). However, conventional measurements are labor-intensive, typically restricted to a small number of samples, and overlook production variability. This paper introduces a fully automated, ISO 20270-compliant C-TPA system for non-rigid test benches, featuring a pre-instrumented test fixture with multiple vibration shakers and sensors automatically linked to a data acquisition system for immediate processing. Components can be characterized within minutes, with blocked forces directly integrated into a VPA workflow, replacing time-intensive in-vehicle testing with a repeatable, operator-independent bench procedure. A case study on an automotive steering system demonstrates the method’s accuracy, repeatability, and efficiency, along with its ability to predict realistic interior sound pressure levels and capture production variability, enabling robust virtual NVH evaluation early in the development cycle.
The deployment of high-power DC charging infrastructure for electric vehicles introduces new challenges in managing noise, particularly in public environments where acoustic comfort and regulatory compliance are essential. Noise emissions from both charging stations and vehicles during charging are a concern for operators of charging parks regarding customer experience and noise immission regulations. AVL employed a structured three-step approach to develop a non-expert tool for assessing the noise radiation of charging stations and vehicles during the charging phase. In a first step, AVL characterized the noise emissions with sound power measurements. Secondly, the measurement results were transferred to the virtual domain. To achieve this, the vehicles and charging station were characterized in the simulation with multiple monopole sources supported by transfer function measurements. This simulation model was validated against the sound power measurement results. After successful correlation of the simulation model, AVL implemented a user-friendly noise mapping tool for predictive planning of charging parks moving from a 3D FEM simulation approach to a simplified noise radiation model according to ISO 9613. Measurements were conducted on a representative high-power DC charging station and two electric vehicles. The vehicle cooling fan was identified as the dominant noise source on the vehicle side, while internal cooling fans of the charging station were the primary contributors to the noise radiated by the charger. Noise emissions were found to increase with higher charging currents, indicating a strong dependency on thermal load. The project established a reusable workflow for acoustic source identification enabling the creation of a scalable database. The noise mapping tool allows easy prediction of noise radiation from multiple charging stations and vehicles during the planning phase of charging parks and supports the design of countermeasures to meet regulatory requirements.
The virtual development of Electric Drive Modules (EDMs) for Battery Electric Vehicles (BEVs) requires proven and predictive methodologies. One part of the development investigates the vibro-acoustic assessment for the low- and high-frequency ranges within the targeted operating range. The efficient use of such a methodology requires an understanding of the accuracy and validity of the achievable results, as well as the derivation of suitable improvement measures for goals that have not been achieved. The use of reference data from experimental investigations and a detailed root cause analysis (RCA), to directly link a specific response and behavior to the excitations, modal content, and transfer functions, is an essential and non-trivial part of the methodology development. This paper describes the development of such a methodology using the example of a new EDM virtual model for Noise, Vibration and Harshness (NVH) analysis, including the simulation approach, validation, and evaluation procedure. It discusses how RCA can be applied to different observed phenomena in EDM NVH behavior and detected deviations between the initial model and the measurements, the main influencing parameters, and the identified improvement potential for simulation models.
As acoustic requirements for NVH trim components become increasingly constrained by mass, cost, and sustainability targets, traditional approaches to inner dash design based on spatially averaged Transmission Loss (TL) metrics are reaching their practical limits. In fully built vehicles, the acoustic performance of the inner dash is governed by its global insulation capability but also by strong spatial heterogeneity and its interaction with spatially distributed noise sources such as the power unit, gearbox, and tyre-road excitation. This paper presents a test-based methodology for the spatial optimisation of inner dash acoustic performance using reciprocal holography. By applying a calibrated sound power source within the vehicle cabin and measuring the reciprocal response in the engine bay and wheel-arch regions, a high-resolution spatial Transmission Loss “hologram” of the inner dash is obtained under in-situ conditions. The resulting spatial data enables the identification of localised acoustic weak points that are not observable using conventional testing methods. To bridge the gap between passive component characterisation and real-world vehicle operation, the spatial TL hologram is subsequently evaluated using representative operational source sound power data to prioritise acoustically relevant regions. This enables the transmitted acoustic energy to be evaluated under realistic driving conditions. The holographic data is then coupled with a parametric acoustic model of the inner dash system, allowing localised mass redistribution to be optimised using a genetic algorithm while respecting packaging and manufacturing constraints.
Noise, Vibration, and Harshness (NVH) performance is critical in the automotive development process, yet identifying the true root causes of unwanted dynamic behavior remains a challenge in full vehicle or system-level finite element (FEM) models. This work demonstrates how Frequency Based Substructuring (FBS) provides an efficient framework for understanding NVH phenomena and facilitates new root cause analysis (RCA) types and processes. To begin, we prove the numerical accuracy of the FBS algorithm deployed in the presented investigation by comparing its results with those obtained with superelements and without substructuring. We point out that because the used FBS process starts with a modal representation of the components rather than their frequency response functions (FRF) a different class of RCA type becomes available. Then we introduce new RCA types starting with an analysis named Modal Influence (MI) that reveals the effect of the modes of any component on a certain response. Its key characteristic is that MI analysis is not restricted to the response component opposite to the well-known modal participation factors. Finally, a second novel analysis type is introduced, an advanced variant of Transfer Path Analysis (TPA). While standard TPA assesses the paths between only two system components, the new Expanding TPA is a multi-step process that identifies the most critical path across all components in a fully automated way.
The increasing electrification of vehicles means that heating, ventilation and air conditioning systems have a broader range of tasks and a different priority assessment. In electric cars, air conditioning systems are not only responsible for cooling the passenger compartment, but also for controlling the battery temperature, particularly during rapid charging, which represents a high-load operating point. Furthermore, achieving high thermodynamic efficiency is desirable, as this directly impacts the range of electric cars. The elimination of the combustion engine as a major source of noise prioritizes the noise, vibration and harshness behavior of the refrigerant compressor for product selection. To investigate the vibration and acoustic behavior, as well as the fluid dynamic forces resulting from the cyclic compression principle of an electric refrigerant compressor, a test rig was developed that allows compressors to be operated and measured in isolation in an anechoic chamber under various defined operating conditions. This test rig has been expanded in two ways within the scope of this work. Firstly, the compressor can be either rigidly attached to a dead mass using a VDA mount or measured while suspended freely. Secondly, a new R744-compatible refrigeration circuit has been added to the test rig, enabling compressors operating with the environmentally friendly refrigerant CO₂, which has so far only been used by a few manufacturers in selected models, to be tested. Measurement results obtained using this test rig provide valuable insight into the vibration behavior and sound spectra of the refrigerant compressor's fluid, structural, and airborne noise when operating at different points.
Recent studies indicate that the door system plays a significant role in the interior noise levels of newly developed vehicles. This research investigates the noise transmission paths through the door system and identifies effective strategies for improvement through a combination of door buck testing and simulation. Specifically, in this study, the finite element method (FEM) was employed for door buck simulation, and the model was validated against vibration test results. Subsequently, acoustic analysis tools were utilized to correlate with noise testing, thereby establishing a process to ensure simulation accuracy. The sound insulation performance for the main areas of the door was experimentally evaluated, and a simulation model with good correlation to these test results was developed. By utilizing both experimental and simulation results, the principal transmission paths were identified, and appropriate improvement strategies for these paths were investigated. The validated improvement strategies are intended to be applied in the development of next-generation vehicles.
Noise phenomena in automobiles caused by the stick-slip effect are increasingly among the most frequent reasons for customer complaints and therefore represent a critical vehicle quality attribute. To proactively address such issues, stick-slip testing of contacting material pairs is commonly applied during development. However, the predictive capability of current stick-slip test methods remains limited, particularly when highly flexible materials and realistic, stochastic excitation conditions are involved. The flexibility of sealing systems often allows the actual relative motion at the contact interface to be accommodated through adhesion and elastic deformation, thereby delaying or even preventing sliding. To date, this effect has not been represented by any characteristic parameter in conventional stick-slip testing. Instead, existing evaluations focus exclusively on the analysis of occurring stick-slip oscillations. For the initiation of stick-slip phenomena, however, not only the mean displacement between two stick-slip oscillations during the sliding phase is relevant, but also the relative displacement required to initiate the first slip event of the sealing contact. With the algorithm developed in this work, which reproducibly determines the distance to first slip based on changes in the friction force slope, this methodological gap is now closed. The displacement to first slip depends on numerous influencing factors, including profile geometry, normal load, sliding velocity, excitation profile, and environmental conditions, and was previously inaccessible by both experimental and numerical approaches. In particular, the onset of slip in sealing contacts can now be determined under stochastic excitation of the friction pairing, thereby closely reflecting real operating conditions. As a result, the prevention of noise phenomena can be significantly strengthened at an early stage of vehicle development.
In vehicles with electrified powertrains, high-frequency tonal noise components have become increasingly prominent and can be perceived as particularly annoying by the driver. While recent advancements in international standardization — such as ECMA-74 [1] and ECMA-418 [2] — have led to powerful new algorithms for tonal noise visualization and analysis, including Tonality-Heatmaps, the measurement side still lacks sensor setups that adequately reflect the spatial sensitivity of noise, especially for tonal components. This challenge is amplified in enclosed vehicle cabins, where room modes create local minima and maxima that become increasingly dense at higher frequencies. As a result, even small head movements can lead to noticeable differences in perceived tonal noise. Current measurement approaches do not sufficiently account for this spatial variability. This contribution addresses the absence of tailored solutions for the driver’s position by introducing an improved microphone arrangement that significantly reduces the uncertainty of measured noise levels. The proposed setup considers spatial variability without compromising comfort or crash safety requirements. By enhancing the precision of tonal noise quantification, this approach provides noise-vibration-harshness (NVH) engineers with a valuable complement to modern software-based tonal analysis methods. The paper discusses the technical implementation constraints and demonstrates the comparability of the new measurement technique with conventional setups.
Recent advancements in system-level NVH (Noise, Vibration, and Harshness) development methodologies have improved target cascading and enabled more efficient system-level optimization. Dynamic substructuring facilitates the virtual integration and modification of multiple subsystems and the prediction of changes in overall transfer functions. In practical automotive applications, advanced frequency-based substructuring has been applied to virtually modify system parameters, such as mass and stiffness, at multiple points in a target system, allowing prediction of the resulting effects and optimization of parameter changes without physical intervention. This study extends the methodology by introducing an enhanced substructuring approach capable of addressing not only basic parameter modifications but also large-scale structural changes. The proposed process involves identifying the characteristics of a base system assembly and a target subsystem, decoupling the subsystem from the assembly, incorporating structural modifications, and predicting the resulting transfer function changes. The method was validated through two complementary workflows: a fully experimental test-based workflow and a hybrid workflow. The test-based workflow demonstrated the reliability of substructuring operations, decoupling and coupling, by experimentally evaluating the base assembly, the original subsystem, and the structurally modified subsystem. The hybrid workflow replaced the experimental subsystem models with finite element models, thereby demonstrating the feasibility of substructuring numerical subsystem models with a physical system assembly. Together, these workflows are applied to one of automotive suspension subsystems, cross-member, which can establish the accuracy, flexibility, and practical applicability of the proposed method in supporting system-level NVH development and structural optimization.
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