Browse Topic: Tests and Testing
This specification covers a leaded bronze in the form of sand and centrifugal castings (see 8.6).
This SAE Recommend Practice establishes for passenger cars, light trucks, and multipurpose vehicles with GVW of 4500 kg (10000 pounds) or less, as defined by the EPA, and M1 category vehicles, as defined by the European Commission:
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 SAE Standard provides testing and functional requirements to meet specified minimum performance criteria for electronic probe-type leak detectors, so they will identify smaller refrigerant leaks when servicing all motor vehicle air conditioning systems, including those engineered with improved sealing and smaller refrigerant charges to address environmental concerns and increase system efficiency. This document does not address any safety issues concerning their design or use.
This SAE Recommended Practice is intended to establish a procedure to certify the fundamental driving skill levels of professional drivers. This certification can be used by the individual driver to qualify their skills when seeking employment or other professional activity. These certification levels may also be used by test facilities or other organizations when seeking test or professional drivers of various skills. The associated family of documents listed below establish driving skill criteria for various specific categories. SAE J3300: Driving level SAE J3300/1: Low mu/winter driving SAE J3300/2: Trailer towing SAE J3300/3: Automated driving Additional certifications to be added as appropriate. This main document provides: (1) common definitions and general guidance for using this family of documents, (2) directions for obtaining certification through Probitas Authentication®1, and (3) driving level examination requirements.
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
This article follows a companion article [1] presented at the SAE NVC 2021, in which a new system for the measurement on small samples of the normal-incidence Insertion Loss (IL) of multilayers used for the manufacturing of automotive sound package parts was first introduced. In addition to simplifying the evaluation of the sound-insulation of multi-layers used to produce sound-package components, the system aims at overcoming the limitations of the test procedure based on the ASTM E2611 standard. In this article, the latter point is demonstrated by comparing the insertion loss results obtained with the new system with those obtained with the test procedure based on the ASTM E2611 standard on a few multilayers commonly used for the manufacturing of automotive sound package parts. Results indicate that the data obtained by means of the newly developed system are more meaningful, practically usable and less prone to edge-effects, compared to those obtained according to the ASTM E2611
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.
High-frequency whine noise in electric vehicles (EVs) is a significant issue that impacts customer perception and alters their overall view of the vehicle. This undesirable acoustic environment arises from the interaction between motor polar resonance and the resonance of the engine mount rubber. To address this challenge, the proposal introduces an innovative approach to predicting and tuning the frequency response by precisely adjusting the shape of rubber flaps, specifically their length and width. The approach includes the cumulation of two solutions: a precise adjustment of rubber flap dimensions and the integration of ML. The ML model is trained on historical data, derived from a mixture of physical testing conducted over the years and CAE simulations, to predict the effects of different flap dimensions on frequency response, providing a data-driven basis for optimization. This predictive capability is further enhanced by a Python program that automates the optimization of flap
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.
The implementation of active sound design models in vehicles requires precise tuning of synthetic sounds to harmonize with existing interior noise, driving conditions, and driver preferences. This tuning process is often time-consuming and intricate, especially facing various driving styles and preferences of target customers. Incorporating user feedback into the tuning process of Electric Vehicle Sound Enhancement (EVSE) offers a solution. A user-focused empirical test drive approach can be assessed, providing a comprehensive understanding of the EVSE characteristics and highlighting areas for improvement. Although effective, the process includes many manual tasks, such as transcribing driver comments, classifying feedback, and identifying clusters. By integrating driving simulator technology to the test drive assessment method and employing machine learning algorithms for evaluation, the EVSE workflow can be more seamlessly integrated. But do the simulated test drive results
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