The Vehicle Noise Control Engineering Academy covers a variety of vehicle noise control engineering principles and practices. There are two concurrent, specialty tracks (with some common sessions): Powertrain Noise and Vehicle Interior Noise. Participants should choose and register for the appropriate Academy they wish to attend. The Powertrain Noise track focuses on noise and vibration control issues associated with internal combustion, hybrid and electric powered vehicles. The vehicle in this case includes passenger cars, SUVs, light trucks, off-highway vehicles, and heavy trucks.
The Vehicle Noise Control Engineering Academy covers a variety of vehicle noise control engineering principles and practices. There are two concurrent, specialty tracks (with some common sessions): Vehicle Interior Noise and Powertrain Noise. Participants should choose and register for the appropriate track they wish to attend. The Vehicle Interior Noise track focuses on understanding the characteristics of noise produced by different propulsion systems, including internal combustion, hybrid and electric powered vehicles and how these noises affect the sound quality of a vehicle’s interior.
In this paper, we propose a novel Split Ring Resonator (SRR) metamaterial capable of achieving a total bandgap in the material’s band structure, thereby reflecting air-borne and structure-borne noise in a targeted frequency range. Electric Vehicles (EVs) experience tonal excitation arising from the switching frequencies associated with motors and inverters, which affects occupant perception of vehicle quality. Recently proposed metamaterial designs isolate either air-borne noise or structure-borne noise, but not both. To achieve isolation of both air-borne and structure-borne acoustic energy associated with these tonal frequencies, we propose a metamaterial supercell with transverse and longitudinal resonant frequencies falling in the desired bandwidth of the total bandgap. We calculate the resonant frequencies and corresponding mode shapes using Finite Element (FE) modal analysis.
The China Automotive Technology and Research Center (CATARC) has completed two new wind tunnels at its test center in Tianjin, China: an aerodynamic/aeroacoustic wind tunnel (AAWT), and a climatic wind tunnel (CWT). The AAWT incorporates design features to provide both a very low fan power requirement, 3.1 MW at 250 km/hr with a 28 m2 test section, and a very low background noise, 58.2 dB(A) at 150 km/h, putting it amongst the quietest in the automotive world. These features are also combined with high flow quality, a full boundary layer control system and 5-belt rolling road (producing a 5 mm block height boundary layer profile), an automated traversing system, and a complete acoustic measurement system including a 3-sided microphone array. The CWT, located in the same building as the AAWT, has a flexible nozzle to deliver 250 km/h with an 8.25 m2 nozzle, and 130 km/h with a 13.2 m2 nozzle.
As a key component of in-vehicle intelligent voice technology, speech enhancement can extract clean speech signals contaminated by environmental noise to improve the quality and intelligibility of speech perception. It has broad applications in human-vehicle interaction, in-car communication, and in-car cinema. Although some end-to-end time-domain-based speech enhancement methods have been proposed, few models are designed based on the characteristics of speech signals. In this paper, we propose a new U-Net based speech enhancement method that utilizes the temporal correlation of speech signals to reconstruct higher-quality and more intelligible clean speech.
As the mobility becomes complex, the coopertion of test and simulation is more and more important. For several years, various technologies for hybrid methods of test and simulation, such as VPT, FBS decoupling, modal model, SEMM and auralization have been studied and developed to help virtual development activities. As a result, the model based roadnoise development process and dedicated softwares have been developed. In this study, the system models such as tire blocked force, suspension FRFs and body FRFs were made by experiment and simulation and assembled to predict vehicle’s roadnoise. As a objective evaluation, roadnoise was analysed and the systematic TPA and sensitivity analysis process and program were development to set reasonable system targets to meet vehicle target. Then, as a subjective evaluation, roadnoise were auralized for various driving conditions such as speeds and road types according to driver’s input.
In vehicle development, reducing noise is a major concern to ensure passenger comfort. As electric vehicles become more common and engine and vibration noises improve, the aerodynamic noise generated around the vehicle becomes relatively more noticeable. In particular, the fluctuating wind noise, which is affected by turbulence in the atmosphere, gusts of wind, and wake caused by the vehicle in front, can make passengers feel uncomfortable. However, the cause of the fluctuating wind noise has not been fully understood, and a solution has not yet been found. The reason for this is that fluctuating wind noise cannot be quantitatively evaluated using common noise evaluation methods such as FFT and STFT. In addition, previous studies have relied on road tests, which do not provide reproducible conditions due to changing atmospheric conditions. To address this issue, automobile manufacturers are developing devices to generate turbulence in wind tunnels.
In the process of automobile industrialization, integrated electric drive systems turn to be the mainstream transmission system of electric vehicles gradually. The main sources of noise and vibration in the chassis are from the gear reducer and motor system, as a replacement of engine. For improving the electric vehicles NVH performance, effective identification and quantitative analysis of the main noise sources are a significant basis. Based on the rotating hub test platform in the semi-anechoic chamber, in this experiment, an electric vehicle equipped with a three-in-one electric drive system is taken as the research object. As well the noise and vibration signals in the interior vehicle and the near field of the electric drive system are collected under the operating conditions of uniform speed, acceleration speed, and coasting with gears under different loads, and the test results are processed and analyzed by using the spectral analysis and order analysis theories.
This paper analyzes the mechanism of vibrational energy propagation and panel vibration generation at the point joints between frame and panel which can be applied to reduce the vehicle interior noise. In this study, we focused on the traveling wave in the early stage of propagation before the mode is formed, and investigated the mechanism of panel vibration generation due to wave energy propagation and its reduction method. First, we show theoretically that the out-of-plane component of the transmitted power at the point junction between frame and panel that contributes to panel vibration is associated with frame deformation. Then, we show through numerical verification that panel vibration can be reduced by reducing the transferred power of the out-of-plane component, and explain the effectiveness of the frame-to-panel joint design guidelines based on energy propagation analysis. Next, This analysis technique is applied to the vehicle body model.
During the pure electric vehicle high speed cruise driving condition, the unsteady air flow in the chassis cavity is susceptible to self-sustaining oscillations phenomenon. And the aerodynamic oscillation excitation could be coupled with the cabin interior acoustic mode through the body pressure relief valve, the low frequency booming noise may occur and seriously reduces the driving comfort. This paper systematically introduces the characteristics identification and the troubleshooting process of the low frequency aerodynamic noise case. Firstly, combined with the characteristics of the subjective jury evaluation and objective measurement, the acoustic wind tunnel test restores the cabin booming phenomenon. The specific test procedure is proposed to separate the noise excitation source.
The noise tests of electric motor systems serve as the foundation for studying their acoustic issues. However, there is currently a lack of corresponding method for identifying key noise characteristics, such as the noise frequency range that needs to be considered, the significant noise orders, and the resonance bands worth paying attention to, based on experimental test data under actual operating conditions. This article proposes a method for identifying the noise characteristics of an electric motor system based on tests conducted under distinct operating conditions, which consists of three parts: identifying the primary frequency range, the significant orders, and the important resonance bands. Firstly, in order to extract noise with the primary energy, the concept of noise contribution is introduced. The primary frequency range and the significant orders under a specific operating condition can be obtained after extraction.
LiDAR sensors play an important role in the perception stack of modern autonomous driving systems. Adverse weather conditions such as rain, fog and dust, as well as some (occasional) LiDAR hardware fault may cause the LiDAR to produce pointcloud with abnormal patterns such as scattered noise points and uncommon intensity values. In this paper, we propose a novel approach to detect whether a LiDAR is generating anomalous pointcloud by analyzing the pointcloud characteristics. Specifically, we develop a pointcloud quality metric based on the LiDAR points' spatial and intensity distribution to characterize the noise level of the pointcloud, which relies on pure mathematical analysis and does not require any labeling or training as learning-based methods do. Therefore, the method is scalable and can be quickly deployed either online to improve the autonomy safety by monitoring anomalies in the LiDAR data or offline to perform in-depth study of the LiDAR behavior over large amount of data.
The challenges concerning noise, vibration, and harshness (NVH) performance in the vehicle cabin have been significantly changed by the powertrain shift to the electric drive unit (eAxle) from the conventional drive unit with an internal combustion engine (ICE). In general, there is a common perception that electric vehicles (EVs) without ICE, which is a large source of noise and vibration, are quieter but exhibit more prominent high-frequency noise than conventional ICE vehicles. Still, past research has not sufficiently clarified the difference in driver evaluation of NVH performance between electric vehicles and conventional ICE vehicles in actual operation. In this study, the authors conducted test drives on public roads in Europe using four EVs and two conventional ICE vehicles, and statistically investigated the difference in driver impression of NVH performance based on interviews during actual driving.
Speech enhancement can extract target speech contaminated by noise and improve its perception quality and intelligibility. This technology has significant potential in intelligent voice interaction for automotive applications. However, the noise environment in vehicles is highly complex, especially due to prominent human voice interference, which poses substantial challenges for automotive voice interaction systems. To address this issue, this paper proposes a module called target-speech-feature-aware for U-net-based speech enhancement that effectively extracts clean speech in environments with human voice interference, enhancing its perceptual quality and intelligibility. In order to extract the features of the target speech, this paper proposes a method designed for the intermediate layer of the U-Net network based on LSTM. Firstly, bidirectional LSTM is used to capture temporal characteristics of encoding compression features.
Methanol emerges as a compelling renewable fuel for decarbonizing engine applications due to a mature industry with high production capacity, existing distribution infrastructure, low carbon intensity and favorable cost. Methanol’s high flame speed and high autoignition resistance render it particularly well-suited for spark-ignition (SI) engines. Previous research [REF paper to be submitted] showed a distinct phenomenon, known deflagration-based knock in methanol combustion, whereby knocking combustion was observed albeit without end-gas autoignition. This work studies the implications of deflagration-based knock on noise emissions, by investigating the knock intensity and combustion noise at knock-limited operation of methanol in a single-cylinder direct-injection SI engine operated, at both stoichiometric and lean (λ = 2.0) conditions. Results are compared against observations from a premium-grade gasoline.