Vehicle Interior Noise Research Articles

Using Vehicle Interior Noise Classification for Monitoring Urban Rail Transit Infrastructure.

This study developed a multi-classification model for vehicle interior noise from the subway system, collected on smartphones. The proposed model has the potential to be used to analyze the causes of abnormal noise using statistical methods and evaluate the effect of rail maintenance work. To this end, first, we developed a multi-source data (audio, acceleration, and angle rate) collection framework via smartphone built-in sensors. Then, considering the Shannon entropy, a 1-second window was selected to segment the time-series signals. This study extracted 45 features from the time- and frequency-domains to establish the classifier. Next, we investigated the effects of balancing the training dataset with the Synthetic Minority Oversampling Technique (SMOTE). By comparing and analyzing the classification results of importance-based and mutual information-based feature selection methods, the study employed a feature set consisting of the top 10 features by importance score. Comparisons with other classifiers indicated that the proposed XGBoost-based classifier runs fast while maintaining good accuracy. Finally, case studies were provided to extend the applications of this classifier to the analysis of abnormal vehicle interior noise events and evaluate the effects of rail grinding.

Study on Radiated Noise of a Panel under Fluctuating Surface Pressure Due to an Idealized Side Mirror

As traditional automobiles develop towards new energy vehicles, the noise, vibration and harshness (NVH) performance of automobiles is facing new challenges. Without the cover of the traditional engine noise and inlet and exhaust noise, the high-speed wind noise becomes more prominent. Thus, research on the calculation method of vehicle interior noise in high-speed driving condition is needed. However, vehicle body structure is complex, and the external excitation components are complicated. In order to analyze the method of predicting the vehicle interior noise at high speed, an idealized side mirror model is taken as the research object in this paper and the radiated noise of a panel under the fluctuating surface pressure (FSP) due to the idealized side mirror is studied. The FSP of the panel is first studied by the numerical simulations of incompressible and compressible flow field. For the incompressible flow field, the Corcos turbulent boundary layer (TBL) model is established to simulate the convective component and the boundary element method (BEM) is used to extract the acoustic component. Subsequently, the Corcos model coupling BEM method, the random modal force coupling BEM method and the deterministic modal force coupling BEM method are used separately to calculate the noise of the panel under the FSP. For the compressible flow field, the convective and acoustic component in the fluctuating pressure are separated by the wavenumber-frequency spectrum (WFS) method. The radiated noise of the panel under the FSP is calculated again by using the WFS, the method of random modal force and the method of deterministic modal force, respectively. Then, the computational time of the six methods of incompressible and compressible calculation is compared. Finally, a fast and accurate method of calculating the panel radiated noise under FSP is obtained by comparing the computational accuracy with the experimental results and combining the computational time: the method of incompressible random modal force. This method can be used to quickly and accurately analyze the vehicle interior noise at high speed, and to optimize the exterior protrusions and the vehicle sound package for improving the vehicle NVH performance at high speed.

Effects of mounting positions and boundary conditions on the sound transmission loss of panels in a niche

The sound insulation performance of railway car body structures is critical for the control of rail vehicle interior noise. In sound transmission loss (STL) measurements, a niche with a large depth is necessary to allow for mounting the wide range of thicknesses of railway car body panels and for the mechanical isolation of the two rooms. In this study, two typical interior floor panels are tested in a series of mounting conditions and mechanical boundary conditions. The change of STL results during measurement is also predicted by an STL prediction model based on the finite element method. At lower frequencies, the STL results are influenced by both the mounting positions and the mechanical boundary conditions. At higher frequencies, the STL results are mainly influenced by the mechanical boundary conditions. Differences between the panel in the infinite baffle and niches at the resonance and off-resonance frequencies are different. Considering both the effects of mounting positions and mechanical boundary conditions, the existence of the cavity amplifies the STL difference caused by the mechanical boundary conditions.

Time-domain signal reconstruction of vehicle interior noise based on deep learning and compressed sensing techniques

During vehicle driving, the noise signal of passenger ear-sides is affected by many sound sources. Meanwhile, the composition and generation mechanism of these sound sources are complicated. The application of traditional data-driven technology in the noise signal reconstruction process of passenger ear-sides often requires complex signal processing and prior knowledge, thereby limiting its application in signal reconstruction. Thus, a multivariable-based vehicle interior noise time-domain signal reconstruction (MTSR) algorithm based on compressed sensing and deep learning is proposed to address such limitation. Raw data are compressed to acquire samples using the proposed algorithm to reduce the amount of data and realize the adaptive extraction of signal features. A deep neural network model for the noise signal reconstruction of passenger ear-sides is established on the basis of multisource signals of the compressed domain, which is pretrained by a restricted Boltzmann machine for improved reconstruction accuracy. The recovery compression signal method realizes the time-domain signal reconstruction of the passenger ear-sides. The effectiveness of the proposed MTSR algorithm is validated using noise signal sources collected from a vehicle. Compared with the different reconstruction models, the proposed algorithm is superior in reconstruction accuracy and time consumption.

A generalized inverse cascade method to identify and optimize vehicle interior noise sources

The noise, vibration and harshness (NVH) emitted by a vehicle are very important to a customer's perception of the vehicle quality. A vehicle's NVH can be improved by considering the three following facets: the noise source, transfer path, and receiver. The identification and optimization of vehicle interior noise sources is crucial when attempting to reduce noise levels and improve sound quality. Although traditional methods, such as those utilizing sound pressure levels, nearfield acoustic holography, and transfer path analysis, can provide the magnitudes and contributions of noise sources, they cannot present specific methods for optimizing those noise sources. This study proposes a new method, the generalized inverse cascade method (GICM), to solve this problem. The GICM combines systems engineering with the interval optimization technique to identify and optimize vehicle noise sources. Applying the GICM to a decision problem involves the following three steps: (1) constructing the decision problem as a cascade tree; (2) developing a numerical model to quantify the cascade tree; and (3) solving the numerical model using the interval optimization method. A Volkswagen sedan is used in this study as an example, and a vehicular road test and subjective evaluation are implemented to record and evaluate the interior noise. The GICM, identifies potential abnormal interior noise sources, and a modified method is presented to optimize the abnormal noise sources by calculating the feasible intervals of design variables. A verification experiment shows that the vehicle interior noise is successfully optimized, thereby validating the proposed GICM.

Active control for vehicle interior noise using the improved iterative variable step-size and variable tap-length LMS algorithms

Active noise control (ANC) is used to reduce undesirable noise, particularly at low frequencies. There are many algorithms based on the least mean square (LMS) algorithm, such as the filtered-x LMS (FxLMS) algorithm, which have been widely used for ANC systems. However, the LMS algorithm cannot balance convergence speed and steady-state error due to the fixed step size and tap length. Accordingly, in this article, two improved LMS algorithms, namely, the iterative variable step-size LMS (IVS-LMS) and the variable tap-length LMS (VT-LMS), are proposed for active vehicle interior noise control. The interior noises of a sample vehicle are measured and thereby their frequency characteristics. Results show that the sound energy of noise is concentrated within a low-frequency range below 1000 Hz. The classical LMS, IVS-LMS and VT-LMS algorithms are applied to the measured noise signals. Results further suggest that the IVS-LMS and VT-LMS algorithms can better improve algorithmic performance for convergence speed and steady-state error compared with the classical LMS. The proposed algorithms could potentially be incorporated into other LMS-based algorithms (like the FxLMS) used in ANC systems for improving the ride comfort of a vehicle.

EMobility – The silent revolution!?

Electric vehicle become more and more popular worldwide. A strong increase of new registered electric vehicles is expected. The interior and exterior sounds of electric vehicles will become an important aspect for sound engineering considering the psychoacoustics: what sounds do customers prefer, what kind of sounds are important for customers, how to consider the typical acoustical orientation known from combustion noises, should it be possible, that a customer can actively/individually influence the character of the vehicle interior noise, which new sounds arise and must be avoided or emphasized, how to design vehicle exterior noise (e.g., pedestrian protection)? Based on the ISO 12913 Soundscape standard the following topics will be discussed: concepts for the development of sounds, applying jury testing to predict human reactions towards the noise or sound? of new vehicle types, application of psychoacoustics analysis and simulation of warning signals in complex traffic scenarios to check their applicability and their annoyance.Electric vehicle become more and more popular worldwide. A strong increase of new registered electric vehicles is expected. The interior and exterior sounds of electric vehicles will become an important aspect for sound engineering considering the psychoacoustics: what sounds do customers prefer, what kind of sounds are important for customers, how to consider the typical acoustical orientation known from combustion noises, should it be possible, that a customer can actively/individually influence the character of the vehicle interior noise, which new sounds arise and must be avoided or emphasized, how to design vehicle exterior noise (e.g., pedestrian protection)? Based on the ISO 12913 Soundscape standard the following topics will be discussed: concepts for the development of sounds, applying jury testing to predict human reactions towards the noise or sound? of new vehicle types, application of psychoacoustics analysis and simulation of warning signals in complex traffic scenarios to check their applic...

Modelling of a lined tyre for predicting cavity noise mitigation

Tyre cavity resonance can significantly affect vehicle interior noise at frequencies around 200 Hz. The insertion of a sound absorbing liner inside the tyre is known to be an efficient countermeasure, in fact nowadays tyre manufacturers are already producing tyres implementing this kind of solution. The present work, through analytical and numerical models, supported by material testing, provides a methodology for predicting the dag performance of a lined tyre, taking into account tyre/lining geometry and sound absorbing material properties. The results reported in the paper show that for fixed material characteristics, the attenuation of the cavity resonance is mostly influenced by the volume of the lining treatment. Moreover, the numerical model developed for discontinuous treatments, suggests that for fixed volume and material properties, the cavity resonance attenuation can be increased by choosing a proper layout.

Identification of perceived sound discomfort contributed from partially correlated vibration and noise sources in vehicles

The interior noise sources are often complex in vehicles, including not only structure-borne and air-borne noise sources but also vibration sources. These sources may be partially correlated and cannot be calculated using traditional methods, e.g., transfer path analysis and operational path analysis. On the other hand, it is necessary to study the sound quality of the vehicle interior noise to improve the comfort of drivers and passengers in vehicles. An operational partial singular value decomposing method together with sound quality analyses was employed in this study to determine the influence of various partially correlated sources with the perceived discomfort of the subject. The vibration and noise in vehicles were measured in a car when it was running on asphalt, concrete, gravel and bumpy roads. Thirty subjects used the absolute magnitude estimation method to rate the discomfort produced by noise stimuli. A discomfort model was proposed based on the relations between subjective magnitudes and the objective parameters of noise (i.e., the SPL, loudness, roughness, sharpness, and articulation index). The contributions of various vibration and noise sources to the vehicle discomfort were predicted well by the operational partial singular value decomposing method based on the discomfort model.

Vehicle interior noise active control based on piezoelectric ceramic materials and improved fuzzy control algorithm

An active control approach for interior noise of vehicle based on the piezoelectric ceramic materials and fuzzy control algorithm is presented in this paper. According to the electromechanical coupling characteristics and the positive/inverse mechanism of a piezoelectric material, a theoretical model of piezoelectric ceramic plate with a sandwich structure is deduced by using impedance analysis method. The size of a piezoelectric ceramic transducer (or actuator) and its sticking position on the plate frame are determined. The structural modes and dynamic responses of a vehicle cavity model are calculated by finite element analysis for selecting optimal positions of actuator on the front wall. Furthermore, a fuzzy controller is designed for active vibration control. The fuzzy algorithm is improved by adjusting quantization factors, such as the deviation and its changing rate. Following the derived piezoelectric equations and the improved fuzzy algorithm, an active structural acoustic control (ASAC) system is developed and a functional prototype is set up by using the dSPACE platform. Experimental results show that the developed ASAC system can significantly reduce not only the vibrations of the front wall, but also the interior noises of the cavity. This implies that the proposed vibration-based ASAC method is effective and feasible, which might be a potential approach for interior noise control of vehicles.

Do drivers of cars hear the ambulance siren sound?: Comparison of vehicle interior noise and ambulance siren volume

Do drivers of cars hear the ambulance siren sound?: Comparison of vehicle interior noise and ambulance siren volume

Influence of Vehicle Interior Noise and Steering Vibration on the Uncomfortableness at Vehicle Cruising

Influence of Vehicle Interior Noise and Steering Vibration on the Uncomfortableness at Vehicle Cruising

Loudness calculation procedure to study electronic steering column lock noise measurement

In the automotive field, the customer requirements for low interior noise and pleasant sound quality inside a vehicle are getting higher and higher. Various national and international regulatory authorities established and reviewed vehicle interior noise for the past years. Besides, lots of studies have shown that vehicle noise can influence the driver’s perceptions and also his or her driving capabilities. To succeed in this scenario, all manufacturers are investing in technology and research in order to improve their component performance. Working on the noise source so as to reduce the seriousness of these noise problems can be really effective. However, various engineering techniques are available to deal with noise and sound-measuring instruments, and systems can help to identify the nature of the problem and they can also be helpful in determining the right procedure to analyse the noise problem. This work proposes a procedure to evaluate the noise originated from an electronic steering column lock device which is used to lock and unlock the steering wheel according to European safety requirements. In particular, different standards and requirements for loudness evaluation have been discussed and the formulation of a straightforward procedure, which can be used to evaluate the loudness according to costumer’s requirements, is defined.

A multi-source fusion algorithm for high-accuracy signal reconstruction of vehicle interior noise on passenger ear-sides

The use of reconstructed noise signal as a primary reference signal is critical toactive noise control in passenger ear-sides under high-speed conditions. A signal decomposition optimisation-based BP neural network for ear-side noise reconstruction (DBENR) algorithm is proposed. This algorithm contains the processes of signal decomposition optimisation (SDO), component fitness calculation (CFC) and ear-side noise reconstruction (ENR). The SDO method is divided into two steps. Firstly, multi-source noise signals are decomposed into a finite number of intrinsic mode function (IMF) components by empirical mode decomposition. Secondly, according to a proposed energy-extreme division method, the IMFs are reconstructed into three signal components, namely, high-, intermediate- and low-frequency components. CFC calculates the fitness of a component in each forward training process of a signal reconstruction BP network to obtain the optimal fitness value. The ENR model is obtained by regarding the optimal fitness values as the initial weights and the thresholds of the signal reconstruction BP network and training. The effectiveness of the proposed DBENR algorithm is validated using five noise signal sources collected from a vehicle. Compared with the signal reconstruction BP algorithm, the proposed algorithm is superior in reconstruction accuracy.

The effect of door structural stiffness and flexural components to the interior wind noise at elevated vehicle speeds

Vehicle interior noise level is the sum of the structure borne and air borne noises created on the road. Structure borne noises become less important at higher speeds and the airflow around the vehicle create the airborne wind noise. When the airflow comes across with the side mirrors, eddy currents create turbulent flow at the back of the mirror and near the A-pillar. Since the aerodynamic flow conditions frequently changes on the road (weather conditions, road obstacles, etc.), aerodynamic forces arise on the vehicle door. This aerodynamic door pressure distributions leads to complicated vibrations and increases interior noise level. The level of the wind noise conveyed from side door window sealing to the vehicle interior has a very significant effect on the customer’s perception of vehicle overall quality. In the present study, aspiration noise induced by sealing gap between the door and the sealing was investigated. Aerodynamic forces were calculated by CFD simulations at different speeds of vehicle and then door deformations caused by these aerodynamic forces were calculated. On the other hand, physical door deformations were measured on the road to digitize the perceived interior noise. The correlation between the sealing gap and the perceived interior noise level was expressed by examining the door stiffness and sealing pressure on the door.

The identification of minimum-weight sound packages

Generally, heavier noise control treatments are favored over lighter treatments since heavier acoustical materials generally insulate (block) noise sources more effectively than do lighter materials. In automotive applications, however, heavier materials cannot always be adopted because of concerns about the total weight of the vehicle. Thus, it would be useful to identify lightweight acoustical treatments that can mitigate vehicle interior noise. Automotive sound packages have both absorption and transmission characteristics, and there is inevitably a trade-off between these two. Therefore, it is important to study the exchange between the absorption and transmission of acoustical materials particularly as it pertains to weight. Here, we describe a procedure, based on plane wave analysis, that can be used to identify weight reduction opportunities by adjusting the acoustical properties of a generic sound package, consisting of a fibrous layer and a flexible microperforated panel surface treatment, so that it meets a target sound pressure level in a downstream interior space. It has been found for the configuration studied here that there are lightweight sound package configurations that can maintain acoustical performance equivalent to that of heavier noise treatments, and further, it has been found that the lightest treatments tend to favor barrier performance rather than absorption. Further, the impact of acoustical leaks is considered, and it has been found that even very small leaks can result in a very substantial weight penalty if a specified level of acoustical performance is to be ensured.

A normalized frequency-domain block filtered-x LMS algorithm for active vehicle interior noise control

Abstract A novel normalized frequency-domain block FxLMS (NFB-FxLMS) algorithm is proposed for active vehicle interior noise control. Different from the time-domain FxLMS (TD-FxLMS) algorithm that processes noise data point by point, the NFB-FxLMS algorithm processes noise data block by block based on the fast Fourier transform (FFT) and the overlap–save method, such that the calculation amount is considerably reduced. In the process, the input signal of a control filter and the filtered input signal are transferred to frequency domain, respectively. Meantime, its convergence performance is improved by normalizing each frequency-domain bin. The interior noise of a high-speed vehicle is recorded and analyzed. The simulations of ANC based on the TD-FxLMS, normalized TD-FxLMS (TD-NFxLMS), basic FB-FxLMS, and NFB-FxLMS algorithms are performed. The results show that the interior noises are suppressed by these four algorithms. But the NFB-FxLMS with greater stability has faster convergence rate and lower steady-state error in the entire frequency band compared with other three algorithms. The higher the vehicle speed, the more obvious the advantages of the NFB-FxLMS. Furthermore, the NFB-FxLMS shows very good effect on nonstationary noise. The NFB-FxLMS algorithm can be directly used in an active interior noise control system of the sample vehicle, which can be extended to other types of vehicle for interior noise control.

Influence of rail fastener stiffness on railway vehicle interior noise

More attention has been paid in recent years to the interior noise of railway vehicles. It has been observed that the interior noise can increase in some locations where vibration-isolation measures are used in the track structures. In order to assess the influence of vibration isolation measures on the noise levels inside railway vehicles, a field measurement campaign has been carried out. The vehicle interior noise has been measured when a train is running at different speeds over the same non-ballasted track section fitted with two types of rail fastener of different stiffnesses. Additional measurements of axlebox vibration, train floor vibration, exterior noise and rail vibration are used to investigate the influence of the fasteners further. The experimental results are compared with simulations performed using the TWINS model, considering the wheel/rail interaction, by focusing only on the relative differences between the two fastener systems. The axlebox vibration and rail vibration are predicted for a unit roughness input and the differences in rolling noise are also obtained. The predicted differences in axlebox vibration, rail vibration and rolling noise are in broad agreement with the measurement results. The results show that the fasteners with a lower stiffness cause a noisier interior environment. Around 125 Hz and in the frequency range 315–1000 Hz, the noise levels are higher for the more elastic fastener, with an average level difference of 3 dB in the latter frequency range. It appears from the shape of the level difference spectra that airborne noise has most influence between 100 and 400 Hz and structure-borne noise has more influence between 500 and 1000 Hz.

Optimal Design for the Rear-Glass Joint of an Automobile for Squeak and Rattle Noise Reduction

Ever since vehicle noise, vibration, and harshness (NVH) reduction technology made dramatic improvements, vehicle interior noises represented by Squeak and Rattle (S/R) becomes an ever more important factor to improve the emotional quality of vehicles. Generally, people detect S/R noises on automotive interior parts, brake system, suspension, Body in White (BIW), etc. Among them, the rear-glass joint is a major source for vehicle interior noise, and can cause S/R noises under a variety of environmental and driving conditions. This study uses, two approaches, experimental and numerical approaches, to define the cause of S/R noise at the rear-glass section. Based on these two approaches, this study confirms that S/R noises generate through the contact between bottom side of molding and BIW. The sealant penetration length, panelmolding distance, and sealant width are the parameters affecting noise generation. In addition, this study created an optimal design with Design of Experiments (DOE) of the rear-glass joint. The design maximized the sealant penetration length, which is a parameter that majorly affects noise. The optimal design comprises of two steps: sealant injections shape optimization and rear-glass joint parameter optimization. Each step is carried out with FEA and validated by sealant penetration experiments. Through these optimizations, this study obtained an optimum combination of design parameters and fignificantly reduced the noise generated by rear-glass section.

Experimental and numerical techniques for lightweight vehicle sound package development

The use of lightweight structural materials poses great challenge to noise control of a vehicle. A new approach to sound package design in lightweight vehicles was developed to reduce vehicle interior noise level without addition of weight. This new approach relies on lightweight acoustical materials that provide superior sound absorption performance to reduce noise level at the source, e.g., inside engine compartment and near the tires. Vehicle NVH finite element analysis (FEA) models were used to develop the lightweight vehicle’s structural design to bring the lightweight vehicle’s low and mid frequency responses closer the baseline vehicle’s performance. Full vehicle SEA (statistical energy analysis) model was developed to evaluate the high frequency NVH (noise, vibration, and harshness) performance of a vehicle. This correlated SEA model was used for the vehicle sound package optimization studies to develop sound package design to improve lightweight vehicle’s acoustic performance. In this paper, the use of NVH CAE (computer aided engineering) simulations for lightweight vehicle design is presented to highlight the limitation and use of FEA and SEA in lightweight vehicle development.