Browse Topic: Noise, Vibration, and Harshness (NVH)
This research addresses the issue of noise, vibration, and harshness (NVH) in electric buses, which can hinder their widespread adoption despite their environmental benefits. With the absence of traditional engines, NVH control in electric vehicles focuses on auxiliary components like the air compressor. In this study, the air compressor was identified as a major source of vibration, causing harsh contact between its oil sumps and mounting bracket. Analyzing the vibrations revealed that the sump and bracket were not moving freely, increasing noise. Modifying the bracket design to allow more movement between the components successfully reduced both noise and vibration. The paper details the experimental process, findings, and structural damping methods to mitigate NVH in electric buses.
The electric vehicle driveline generates less vibration and noise compared to a conventional internal combustion engine vehicle, making it harder for the driver to perceive the vehicle’s operating status through driveline sounds, thereby diminishing driving engagement and experience. To compensate for the absence of engine sound in EV drivelines, Active Sound Design (ASD) technology has become a crucial method for drivetrain sound enhancement, with sound synthesis algorithms playing a key role in this process. Although pitch-shifting algorithms based on frequency shift principles can synthesize engine sounds, they suffer from spectral leakage and stuttering caused by sound splicing. To address these issues, a pitch-shifting synthesis algorithm (QCPS, Quadratic interpolation-based Continuous audio sample indexing Pitch Shifting algorithm) is proposed in this paper, which combines a quadratic interpolation method with a continuous audio sample indexing strategy. First, the frequency
This ARP provides two methods for measuring the aircraft noise level reduction of building façades. Airports and their consultants can use either of the methods presented in this ARP to determine the eligibility of structures exposed to aircraft noise to participate in an FAA-funded Airport Noise Mitigation Project, to determine the treatments required to meet project objectives, and to verify that such objectives are satisfied.
Sound source identification based on beamforming is widely used today as a spatial sound field visualization technology in wind tunnel experiments for vehicle development. However, the conventional beamforming technique has its inherent limitation, such as bad spatial resolution at the low frequency range, and limited system dynamic range. To improve the performance, three deconvolution methods CLEAN, CLEAN-SC and DAMAS were investigated and applied to identify wind noise sources on a production car in this paper. After analysis of vehicle exterior wind noise sources distribution, correlation analysis between identified exterior noise sources and interior noise were conducted to study their energy contribution to vehicle interior. The results show that the algorithm CLEAN-SC based on spatial source coherence shows the best capability to remove the sidelobes for the uncorrelated wind noise sources, while CLEAN and DAMAS, which are based on point spread functions have definite
A proprietary metamaterial has been shown to reduce panel vibration. In this particular case, the metamaterial is designed to be attached to the edge of a glass panel and can reduce panel vibration and noise transmission due to wind or other sources into the vehicle interior. Acoustic transmission loss and panel vibration assessments show the benefit of this approach.
As the adoption of Electric Vehicles (EV) and Plug-in Hybrid Electric Vehicles (PHEV) continues to rise, more individuals are encountering these quieter vehicles in their daily lives. While topics such as propulsion sound via Active Sound Design (ASD) and bystander safety through Acoustic Vehicle Alerting Systems (AVAS) have been extensively discussed, charging noise remains relatively unexplored. Most EV/PHEV owners charge their vehicles at home, typically overnight, leading to a lack of awareness about charging noise. However, those who have charged their cars overnight often report a variety of sounds emanating from the vehicle and the electric vehicle supply equipment (EVSE). This paper presents data from several production EVs measured during their normal charging cycles. Binaural recordings made inside and outside the vehicles are analyzed using psychoacoustic metrics to identify sounds that may concern EV/PHEV owners or their neighbors.
Every vehicle has to be certified by the concerned governing authority that it matches certain specified criteria laid out by the government for all vehicles made or imported into that country. Horn is one of the components that is tested for its function and sound level before a vehicle is approved for production and sale. Horn, which is an audible warning device, is used to warn others about the vehicle’s approach or presence or to call attention to some hazard. The vehicle horn must comply with the ECE-R28 regulation [1] in the European market. Digital simulation of the horn is performed to validate the ECE-R28 regulation. In order to perform this, a finite element model of a cut model of a vehicle, which includes the horns and other components, is created. Fluid-structure coupled numerical estimation of the sound pressure level of the horn, with the appropriate boundary conditions, is performed at the desired location as per the ECE-R28 regulation. The simulation results thus
A test and signal processing strategy was developed to allow a tire manufacturer to predict vehicle-level interior response based on component-level testing of a single tire. The approach leveraged time-domain Source-Path-Contribution (SPC) techniques to build an experimental model of an existing single tire tested on a dynamometer and substitute into a simulator vehicle to predict vehicle-level performance. The component-level single tire was characterized by its acoustic source strength and structural forces estimated by means of virtual point transformation and a matrix inversion approach. These source strengths and forces were then inserted into a simulator vehicle model to predict the acoustic signature, in time-domain, at the passenger’s ears. This approach was validated by comparing the vehicle-level prediction to vehicle-level measured response. The experimental model building procedure can then be adopted as a standard procedure to aid in vehicle development programs.
This study presents a novel methodology for optimizing the acoustic performance of rotating machinery by combining scattered 3D sound intensity data with numerical simulations. The method is demonstrated on the rear axle of a truck. Using Scan&Paint 3D, sound intensity data is rapidly acquired over a large spatial area with the assistance of a 3D sound intensity probe and infrared stereo camera. The experimental data is then integrated into far-field radiation simulations, enabling detailed analysis of the acoustic behavior and accurate predictions of far-field sound radiation. This hybrid approach offers a significant advantage for assessing complex acoustic sources, allowing for quick and reliable evaluation of noise mitigation solutions.
This study focuses on the numerical analysis of weather-strip contact sealing performance with a variable cross-sectional design, addressing both static and dynamic behaviors, including the critical issue of stick-slip phenomena. By employing finite element modeling (FEM), the research simulates contact pressures and deformations under varying compression loads, DCE (Door Closing Efforts) requirements, typical in automotive applications. The analysis evaluates how changes in the cross-sectional shape of the weather-strip affect its ability to maintain a consistent sealing performance, especially under dynamic vehicle operations. The study also delves into stick-slip behavior, a known cause of noise and vibration issues, particularly improper/ loosened door-seal contact during dynamic driving condition. This study identifies key parameters influencing stick-slip events, such as friction coefficients, material stiffness, surface interactions, sliding velocity, wet/dry condition
Noise transmission through the vehicle dash panel plays a critical role in isolating passengers from noise sources within the motor bay of the vehicle. Grommets that contain electrical harness routing as well as HVAC lines are examples of dash panel pass-throughs that should be selected with care. Acoustic performance of these components is generally characterized in terms of measured quantities such as noise reduction (NR), sound transmission loss (STL), and insertion loss (IL). These measurements need to be carried out per SAE or ASTM standards in appropriate anechoic or reverberant chambers as this is important for consistency. This work explores an in-situ measurement of the grommet STL performance in the vehicle environment. It utilizes a repurposed vehicle with its cabin retrofitted to serve as an anechoic chamber and its frunk acting as a reverberant chamber. Results of this in-situ measurement are then compared to measurements following industry standards to discuss the
Electric vehicles (EVs) present a distinct set of challenges in noise, vibration, and harshness (NVH) compared to traditional internal combustion engine (ICE) vehicles. As EVs operate with significantly reduced engine noise, other sources of noise, such as motor whine, power electronics, and road and wind noise, become more noticeable. This review paper explores the key NVH issues faced by EVs, including high-frequency tonal noise from electric motors, gear meshing, and vibrations. Additionally, it examines recent advancements and trends in NVH mitigation techniques, such as active noise control, improved material insulation, and advanced vibration isolation systems. Furthermore, this paper discusses the role of computational tools, simulation technologies, and testing methodologies in predicting and addressing NVH concerns in EVs. By providing an in-depth analysis of the challenges and the latest innovations, this review aims to contribute to the ongoing development of quieter and
Gear whine has emerged as a significant challenge for electric vehicles (EVs) in the absence of engine masking noise. The demand from customers for premium EVs with high speed and high torque density introduces additional NVH risks. Conventional gear design strategies to reduce the pitch-line velocity and increase contact ratio may impact EV torque capacitor or its efficiency. Furthermore, microgeometry optimization has limited design space to reduce gear noise over a wide range of torque loads. This paper presents a comprehensive investigation into the optimization of transfer gear blanks in a single-speed two-stage FDW electric drive unit (EDU) with the objective of reducing both mass and noise. A detailed multi-body dynamics (MBD) model is constructed for the entire EDU system using a finite-element-based time-domain solver. This investigation focuses on the analysis and optimization of asymmetric gear blank design features with three-slot patterns. A design-of-experiment (DOE
Rotor skewing is a commonly used technique to mitigate noise and vibration challenges of permanent magnet synchronous motor. The intention of rotor skewing is to minimize targeted electromagnetic forces, thereby enhancing motor NVH performance. However, achieving improved NVH performance may be attainable by merely altering the rotor skew pattern while keeping the summation of radial and tangential electromagnetic forces the same. This research investigates the impact of different rotor skewing patterns on the NVH performance of permanent magnet synchronous motor. With summation of radial and tangential electromagnetic forces remaining the same, four different skew patterns are applied to generate electromagnetic forces across each motor slice. Multi-slice method is used for different skew patterns when applying electromagnetic forces on the motor model. Noise and vibration level will be compared to identify the best skew pattern for proposed motor.
As the automotive industry moves toward electrification, new challenges emerge in keeping pleasant acoustics inside vehicles and their surroundings. This paper proposes a method for anticipating the main sound sources at driver’s ear for custom driving scenarios. Different categories of Road and Wind noise were created from a dataset of multiple vehicles. Using innovative sound synthesis techniques, it enables Valeo to make early predictions of the emergence of an electric axle powertrain (ePWT) once it is combined with this masking noise. Realistic signals could be generated and compared with actual acoustic measurements to validate the method.
The rapid adoption of electric vehicles (EVs) necessitates updates to the automotive testing standards, particularly for noise emission. This paper examines the vehicle-level noise emission testing of a Nikola Class 8 hydrogen fuel cell electric semi-truck and the component-level noise emission testing needed to create a predictive simulation model using Wave6 software. The physical, component-level noise emission testing focused on individual cooling fans in a semi-anechoic chamber to assess their isolated noise contributions. With this data, an initial model was developed using spatial gradient statistical energy analysis, which successfully predicted pass-by noise levels based on varying fan locations and speeds. Real-world pass-by testing confirmed the model's accuracy across different cooling fan speeds. By leveraging advanced simulation techniques, engineers aim to enhance the accuracy and reliability of pass-by noise predictions through cost-effective studies of architectural
For electric vehicles, it is critical to develop drive units that produce a minimal amount of noise while meeting efficiency needs for a given application. Modern computational resources and accumulated experience allow for engineers to evaluate gear noise early in the development process and influence the design of the drive unit. This paper documents a high-fidelity virtual engineering approach to evaluate gear noise in a concept parallel axis drive unit and provide learnings to influence the design of external structures to improve NVH performance. By using the latest simulation tools to calculate and visualize the noise and vibration characteristics of the drive unit, designers and developers can implement design changes in optimization iterations to reduce noise and vibration. Gear harmonic response is firstly analyzed through a system model which considers structural deflection and misalignment, then a FE housing model is incorporated which is used for noise radiation evaluation
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