Powertrain Noise Research Articles

LCV Vehicle in-cab noise prediction and reduction using SEA based simulation approach

With increase in consumer demand vehicle OEMs are developing quieter vehicles at a design stage using acoustic simulation tools to reduce development cycle times avoiding costly proto builds and test iterations. Present work describes application of statistical energy analysis (SEA) simulation approach for in cab noise prediction and noise reduction through sound package optimization for LCV vehicle. In first step complete vehicle in-cab SEA model is prepared with baseline sound package concept. It consists of comprehensive definition of structural panels, interior and exterior cavities. Noise control treatment is modelled as multi-layer poro-elastic materials. Airborne noise excitations for sources engine, transmission, intake and exhaust muffler are applied. Source strengths for above sources were derived from dyno-coupled powertrain noise tests in the anechoic chamber. Load case of rated speed was considered neglecting tire and wind noise during simulation. Contribution analysis was carried out to find out sensitivity of different panels on interior noise at various frequencies. It was observed that bottom middle floor panel and rear glass panel were major noise contributors. Noise mitigation was carried out by optimizing floor panel sound package and intake air filter within given space constraints. These modifications resulted in overall 2 dB in-cab noise reduction

Unfolding dynamics in the perception of interior vehicle acoustics via continuous evaluation procedure (CEP)

The evaluation of a soundscape is a challenging task as the object of study is not a stationary event of sensation but rather a dynamic and complex scene stretching over a specific period. To do justice to the time dimension in such acoustic scenes, we utilized the Continuous Evaluation Procedure (CEP). Extending common standard instruments asking participants for a singular integral at the end of the sound experience (e.g., on a rating scale), the participants in this study were enabled to continuously evaluate the evolving acoustic scene of accelerating electrified vehicles (EVs) using CEP. With the increasing electrification of powertrains in the automotive industry, acoustic engineers face the challenge of defining innovative sounds using the availabilities of now low-noise emission platforms of EVs that deviate in their noise profiles from familiar but technologically outdated internal combustion engine vehicles (ICEVs), which have defined the general sound schemes for more than a century. To capture dynamic aspects in the quality perception of powertrain noise in EVs, we asked 37 participants to evaluate acoustic recordings of different vehicles in varying acceleration modes in a high-quality three-dimensional (3D) acoustic simulator. Thereby, we revealed much more detailed and time-dependent quality aspects, which do not come forth in an integral singular measure (ISM) where all impressions experienced during the ongoing acoustic scene are blended together. We, therefore, propagate the systematic application of the CEP method when it comes to the qualitative evaluation of transient acoustic scenes. CEP opens the great opportunity to unfold, detect, and analyze dynamic effects in soundscapes and noise profiles, but of any kind of acoustic signal.

Implementation of supervised machine learning classification for detection and severity determination of electrified power train noise and vibration fault diagnosis

Vehicles equipped with electrified powertrains produce lower sound and vibration levels compared to those equipped with internal combustion engine powertrains. This makes noise and vibration (N&V) from other non-engine components more perceptible. Gear growl is one of the newly observed N&V that brings concerns by the passengers and manufacturers. The understanding of signal characteristics and the threshold for determining whether gear growl requires attention remains limited. To address this, supervised machine learning classification is employed. Root-mean-square (RMS) and spectral entropy values are sufficient for the classification of vibration data with test accuracy of 0.983. However, the acoustic signal required more features due to background noise, making data linearly inseparable. Features that describe the characteristics of acoustic data are studied, extracted, and selected. Utilizing a support vector machine (SVM) for classification, the study achieves an average test accuracy of 0.918. Further, a multi-class classification model is implemented based on preliminary subjective listening studies, classifying different severities of gear growl. Further listening studies are suggested for improving multi-class classification performance. Methods described in this study mainly focus on the analysis of gear growl, but they can be generalized for N&V signal-based fault diagnosis applications.

The use of an artificial neural network for assessing tone perception in electric powertrain noise, vibration and harshness

The transition from internal combustion engines to electric powertrains brings new challenges for the Noise, Vibration, and Harshness (NVH) analysis of these vehicles. The tonal nature of the electromagnetic excitations and of the gear meshing mechanism are reflected in the radiated noise of electric powertrains, often leading drivers and passengers to rate the noise from electric vehicles with an increased nuisance even if they are quieter than internal combustion driven powertrains. In this paper, a flexible multi-body dynamics model is developed to calculate the vibration and forces transmitted from the bearings to the housing of an electric powertrain. Acceleration, force and sound spectra data are used to train an artificial neural network to assess the prominence of tones in the noise based on the results of the structural simulation. The results show it is possible to identify psychoacoustic metrics from the multibody dynamics simulation alone. With this new approach, it is feasible to quickly investigate how changes in the powertrain will affect the tonal perception of the noise without the need of new acoustic simulations and experiments. For the tonal perception analysis, the Prominence Ratio is used as a metric. This framework of combining multibody dynamics simulation with initial acoustic data and neural networks can be also applied to different NVH metrics as appropriate.

Torque dependent behavior of the vehicle background noise and its influence on masking thresholds in electric vehicles

The quality and comfort perception in vehicles is strongly influenced by the overall sound quality of the vehicle. Besides aerodynamic and tire noise, tonal powertrain noise plays an important role and can have a strong negative impact on sound quality. An assessment of the perceptibility of tonal noise with respect to masking noise is therefore essential for the vehicle development process. Due to the absence of masking by the internal combustion engine in electric vehicles and the wider frequency range of the powertrain noise, new methods are required for this purpose. In order to evaluate state of the art masking models and a newly developed method regarding the audibility of tonal components, masking thresholds for tonal noise in the vehicle interior are experimentally determined in listening tests. A currently developed extension of the method will also allow roller dynamometer measurements to be used for the evaluation. Currently, the masking signals are mostly generated with torqueless coast down measurements to consider the worst-case scenario. However, a torque dependency has to be taken into consideration. In this paper the torque dependency of the masking noise and the resulting influence on masking threshold is shown for road and roller dynamometer measurements.

The Sound Characteristic of Armored Electric and Internal Combustion Engine SUV

This study aims to classify and characterize the sounds produced by an armored SUV with an internal combustion engine and an electric motor in various driving situations. Based on our study, we found that electric vehicles (EVs) produce lower sound pressure levels (SPLs) than internal combustion engine vehicles (ICEVs) under static conditions. The SPL levels indicates a significant difference of 4.64 dB(A) on average and 6.04 dB(A) on peak of the SPL levels of EVs and ICEVs. Our study also identified differences in the sounds produced by EVs and ICEVs under various scenarios. The powertrain components of EVs and ICEVs have different characteristics and produce different sounds. Specifically, during acceleration, EVs had significantly less powertrain noise compared to ICEVs due to the absence of an ICE. In contrast, during deceleration, the EVs had more road bump noise in the frequency range of 100-20000 Hz compared to ICEVs, possibly due to the ICE powertrain noises being more dominant compared to the suspension systems. In general, two distinct vehicles can be distinguished by their distinct sounds in various settings In addition, the electric vehicle emits a number of sounds that are often absent from internal combustion engine vehicles.

Experimental Determination of the Masking Threshold for Tonal Powertrain Noise in Electric Vehicles

Tonal powertrain noise can have a strong negative impact on vehicle sound quality. Therefore, an assessment of the perceptibility of tonal noise with respect to masking noise is essential for the vehicle development process. In electric vehicles, due to the missing masking by the combustion engine, new methods are required for this purpose. In this study, listening tests were conducted to determine the masking threshold in the electric vehicle interior for various driving speeds (30 km/h, 60 km/h, and 90 km/h) with an Adaptive-Forced-Choice method. The novelty of this study is that it used vehicle interior noise as a masker, compared to broadband or narrowband white and pink noises. It could be shown that the masking threshold in electric vehicles strongly depends on the driving speed, and the investigated interior noise mainly affects frequencies up to 6400 Hz in this speed range. For frequencies greater than 6400 Hz, the masking noise has no significant effect on perceptibility of tonal noise in the investigated vehicle, and only the subjects’ individual absolute threshold of hearing is relevant. Additionally, a strong variation in the masking threshold between the subjects was found for high frequencies. With these results, methods that estimate masking thresholds in electric vehicles can be improved. Furthermore, threshold targets can be adjusted for different customer groups.

Research on Conditions and Influence Factors of an Acoustic Wave Acting as a Plane Wave in Tire Acoustic Cavity

Tire acoustic cavity resonance noise (TACRN) contributes significantly to the interior noise of electric cars and passenger cars with lower powertrain noise, which affects the comfort of the ride. To suppress TACRN effectively, it is crucial to clarify the characteristics of TACRN. In previous studies, the acoustic wave in the tire acoustic cavity is straightforwardly assumed to be the plane wave for convenience. In fact, there exist strict conditions for the acoustic wave propagating in the pipeline to act as a plane wave. The aim of this paper is to make the characteristics and evolution of acoustic waves in tire acoustic cavities clear. To do so, a simplified model of the tire cavity is established, and the sound field distribution and the acoustic wave propagation characteristics in the tire cavity are analyzed based on the theory of acoustic waveguide. Then, the existence ranges of higher-order waves (non-plane waves), the conditions of an acoustic wave evolving into a plane wave, and the frequency range of a plane wave are investigated. Finally, the characteristics and evolution law of an acoustic wave in a tire acoustic cavity are obtained. The work in this paper may deepen the understanding of the characteristics and mechanism of acoustic waves in the tire cavity and be helpful and meaningful for analyzing and suppressing TACRN. Therefore, it is of practical significance to reduce TACRN transmitted to the vehicle and improve the sound quality inside the vehicle.

Force amplification at the wheel hub due to the split in the air-cavity mode for a rolling tire

Reduction of tire/road noise has become an important issue for EV's since powertrain noise has largely been eliminated. A tire's air-cavity mode is known to be a significant contributor to increased forces at the wheel hub, which can result in significant interior noise levels near 200 Hz. Moreover, the single natural frequency of a static, undeformed tire can separate into two neighboring frequencies for a rolling, deformed tire due to the Doppler effect. In this study, the evolution of the frequency split at different speeds was observed in Tire Pavement Test Apparatus (TPTA) tests by measuring the dynamic force at the hub under rotation and load. Similar results were obtained using FE simulations to generate Campbell diagrams. Through the FE simulation, it has been shown that there can be force amplification at the hub when the split frequencies couple with adjacent treadband structural modes near 200 Hz. In addition, both material and geometrical modifications were applied to the base model to find values that would reduce force levels at the split frequencies. Finally, it is suggested that a target speed needs to be determined when evaluating a tire since the two natural frequencies are strongly influenced by rotation speed.

Simulating electrified powertrain noise and vibration measurement signals for machine learning based fault diagnosis applications

Machine learning classification is a common method for vehicle noise and vibration (N&V) fault diagnosis which helps improve vehicle safety, comfort, and reduce maintenance cost. To improve the accuracy of classification, the model requires sufficient training data which is often expensive and time-consuming to acquire. A possible solution to resolve this limitation is to extract representative features from measurement signals and generate realistic simulatied signals based on a smaller set of high-quality N&V measurement signals. A method to simulate N&V measurement signals of rotating machineries such as electrified powertrains is proposed. In its feature extraction process, tonal and broadband components of measurement data are separated. After the separation, time-varying tonal amplitudes, broadband spectra, and various related statistical features are extracted. In the simulation process, tonal and broadband signals were simulated using the extracted features with random variations added to each feature. In the current work N&V measurement signals of rotating machinery are simulated with relatively low speed fluctuation and results are presented.

Factors influencing tyre/road noise under torque

High levels of road traffic noise negatively impact public health in many parts of Europe, especially in cities. The introduction of electric mobility is often seen as one of the best measures to reduce noise exposition in urban environments. Compared to internal combustion engine vehicles (ICEV), there is an increased importance of tyre/road noise for electric vehicles (EV) because of the reduced masking by the powertrain noise. This effect increases further under acceleration. Firstly, it is known that in most cases tyre/road noise is higher under torque than for free rolling. Secondly, in situations which are characterized by increased driving torque, the lack of masking from powertrain noise for EVs is especially evident when compared to ICEVs. The aim of the LIFE E-VIA project is to reduce road traffic noise in cities by providing noise optimized road surfaces and tyres for EVs. Because of the mentioned effects, not only constant speed driving needs to be considered but also accelerated driving. Consequently, within E-VIA noise measurements from an indoor drum and a test track have been used to investigate the impact of different tyre parameters and operating conditions on the change of tyre/road noise under acceleration when compared to free rolling.

Sound Design of Background Noise in the Electric Vehicle Interior Based on the Perception of Electric Vehicle Powertrain Noise

Sound Design of Background Noise in the Electric Vehicle Interior Based on the Perception of Electric Vehicle Powertrain Noise

Research on the resonance frequency splitting mechanism and novel modal characteristics of a rotating tire acoustic cavity

Tire acoustic cavity resonance (TACR) noise contributes significantly to interior noise for lower powertrain noise passenger cars and electric cars, which affects the ride comfort obviously. To design sound absorption structures effectively, it is crucial to clarify the evolution mechanism and influence factors of the resonance frequencies and acoustic modal shapes with the running speed. Aiming at these problems, in this paper, a theoretical model of sound wave propagation in a tire acoustic cavity is constructed based on the superposition principle of traveling waves, and the sound field distributions under different rotating speeds are investigated. The formation conditions of TACR are summarized from the perspective of wavenumber. Especially for a rotating tire acoustic cavity, some novel modal characteristics, such as the novel deflective modal shapes and the continuously changing phase, are found. And the theoretical calculation results are verified by the experiment and simulation. The significance of this work is that the evolution mechanisms of TACR frequency and modal shape with the tire rotating speed are theoretically clarified and revealed, which are helpful to obtain effective solutions to suppress TACR noise.

Identification of prominent noise components of an electric powertrain using a psychoacoustic model

Because of the electric power transmission system has no sound masking effect compared with the traditional internal combustion power transmission system, electric powertrain noise has become the prominent noise of electric vehicles, adversely affecting the sound quality of the vehicle interior. Because of the strong coupling of motor and transmission noise, it is difficult to separate and identify the compositions of the electric powertrain by experiments. A psychoacoustic model is used to separate and identify the noise sources of the electric powertrain of a vehicle, considering the masking effect of the human ear. The electric powertrain noise is tested in a semi-anechoic chamber and recorded by a high-precision noise sensor. The noise source compositions of the electric powertrain are analyzed by the computational auditory scene analysis and robust independent component analysis. Five independent noise sources are obtained, i.e., the fundamental frequency of the first gear mesh noise, fundamental frequency of the second gear mesh noise, double frequency of the second gear mesh noise, radial electromagnetic force noise and stator slot harmonic noise. The results provide a guide for the optimization of the sound quality of the electric powertrain and for the improvement of the sound quality of the vehicle interior.

Howl, whirr, and whistle: The perception of electric powertrain noise and its importance for perceived quality in electrified vehicles

The technical specifications of electrified vehicles (xEVs) will drastically change the way future vehicles might sound compared to conventional vehicles with internal combustion engines (ICEVs). The electrified powertrain is responsible for a profoundly different profile in vibro-acoustical characteristics (NVH: noise, vibration, and harshness) and offers the opportunity to create specific sounds related to certain requirements and endorsing associations for increasing the user experience and aesthetic appreciation. The present study’s main aim was to evaluate the perceived emergence of the electric powertrain noise and its implications for perceived quality conveyed by it. Utilizing a sophisticated acoustic simulator presenting ambisonic 3D stimuli from eleven different electrified cars in four different driving scenarios, N = 65 participants evaluated the perceptibility of e-powertrain noise and perceived quality of the vehicle’s interior soundscape. We revealed the auditory modality as an integral part of assessing the product’s quality and gathered qualitative reference points for further investigations.

Preliminary design of a fuel cell/battery hybrid powertrain for a heavy-duty yard truck for port logistics

The maritime transport and the port-logistic industry are key drivers of economic growth, although, they represent major contributors to climate change. In particular, maritime port facilities are typically located near cities or residential areas, thus having a significant direct environmental impact, in terms of air and water quality, as well as noise. The majority of the pollutant emissions in ports comes from cargo ships, and from all the related ports activities carried out by road vehicles. Therefore, a progressive reduction of the use of fossil fuels as a primary energy source for these vehicles and the promotion of cleaner powertrain alternatives is in order.The present study deals with the design of a new propulsion system for a heavy-duty vehicle for port applications. Specifically, this work aims at laying the foundations for the development of a benchmark industrial cargo–handling hydrogen-fueled vehicle to be used in real port operations. To this purpose, an on-field measurement campaign has been conducted to analyze the duty cycle of a commercial Diesel-engine yard truck currently used for terminal ports operations. The vehicle dynamics has been numerically modeled and validated against the acquired data, and the energy and power requirements for a plug-in fuel cell/battery hybrid powertrain replacing the Diesel powertrain on the same vehicle have been evaluated. Finally, a preliminary design of the new powertrain and a rule-based energy management strategy have been proposed, and the electric energy and hydrogen consumptions required to achieve the target driving range for roll-on and roll-off operations have been estimated.The results are promising, showing that the hybrid electric vehicle is capable of achieving excellent energy performances, by means of an efficient use of the fuel cell. An overall amount of roughly 12 kg of hydrogen is estimated to be required to accomplish the most demanding port operation, and meet the target of 6 h of continuous operation. Also, the vehicle powertrain ensures an adequate all-electric range, which is between approximately 1 and 2 h depending on the specific port operation.Potentially, the hydrogen-fueled yard truck is expected to lead to several benefits, such as local zero emissions, powertrain noise elimination, reduction of the vehicle maintenance costs, improving of the energy management, and increasing of operational efficiency.

Case study: Separating source contributions of vehicle interior noise by operational transfer path analysis

The perception of vehicle interior noise is a key quality index to customers and automakers alike. By tracing noise back to key noise sources and paths, one can focus their refinement efforts. Aiming at the most efficient way to identify the primary noise sources in a vehicle cabin, this article establishes a framework of operational transfer path analysis (OTPA) for separating contributions of noise sources by operational measurements only. OTPA model design, measuring essentials and synthesis method used for separating vehicle interior noise contributions from the powertrain, tires andwindwere described in detail. To comprehend the implementation of OTPA on noise source separation, this article also addresses an exemplification study on an electric vehicle. In the case study illustrated, both spectral map and order extractions were used to validate if the OTPA synthesized results of the powertrain noise contribution agreed with the measured results. Tire noise contribution was validated using the tires driven by the dynamometer along with all other systems switched off. With well-validated OTPA model for the powertrain and tires, further individual path breakdown of the powertrain and tire noise then was investigated to identify key contributors to the interior noise. After clearly separating interior noise contributions, one therefore could design effective countermeasures to mitigate the dominant noise sources. With appropriate scheme of measurement and synthesis, the OTPA technique could therefore effectively serve target setting and refinement focus at foremost noise contributors.

Simulation and Optimization of Acoustic Package of Dash Panel Based on SEA

The instrument panel assembly is an important structure to shield powertrain noise and front wheel noise, and the instrument panel sound insulation system plays a key role in it. Therefore, the sound insulation characteristics of the instrument panel sound insulation components appear particularly important. In the existing technology, the acoustic design of instrument panel mostly adopts the reverse design method and pays little attention to the forward design of acoustic system, which tends to lead to the shortcomings such as inaccurate acoustic design and poor acoustic design quality. The reverse design also restricts the development cycle, so the control and design of acoustic performance cannot be realized in the initial stage of design. Based on the above problems, the statistical energy model of the dashboard model was established by combining the statistical energy flow method and the proxy model method, and the influences of different acoustic cladding layers, different thickness, leakage, and new material microfibers on the sound insulation parameters of the front coaming were simulated. The general rules that affect the sound insulation performance of the structure are obtained. On this basis, multiobjective genetic algorithm and proxy model method are used to optimize the insertion loss of the front panel acoustic pack and the weight of the dashboard by introducing multiobjective variable and experimental design method, so as to obtain the best solution to meet the requirements of insertion loss and lightweight acoustic pack of the dashboard. It is of great engineering significance for the development of acoustic components for forward design instrument panel.

Study on 3D quantization and optimization of the acoustic package of vehicle powertrain by energy boundary element method

Automobile noise has become the first source of environmental noise pollution, and its radiated power reaches 75% of the total environmental noise, while powertrain noise is the main source of automobile noise. With the problem of noise reduction of automobile powertrain, a 3D grid positioning technology, based on energy boundary element method, was proposed to quantify the noise source of automobile powertrain, and the generalized sound holography was applied to obtain the noise cloud map and analyze the noise propagation path, so as to determine the acoustic package material and the optimal location. Through the optimization experiment and simulation of 4 schemes, the acoustic package of automobile powertrain with the best effect was obtained, and the insertion loss of 6.7db could be obtained at 7m of the powertrain. Therefore, the results have important engineering application values for automobile noise reduction.

Optimization of the Powertrain Noise for the Electric Vehicle Mercedes-Benz EQC

Optimization of the Powertrain Noise for the Electric Vehicle Mercedes-Benz EQC