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This SAE Information Report SAE J2836/6 establishes use cases for communication between plug-in electric vehicles and the EVSE for wireless energy transfer as specified in SAE J2954. It addresses the requirements for communications between the on-board charging system and the wireless EV supply equipment (WEVSE) in support of detection of the WEVSE, the charging process, and monitoring of the charging process. Since the communication to the charging infrastructure and the power grid for smart charging will also be communicated by the WEVSE to the EV over the wireless interface, these requirements are also covered. However, the processes and procedures are expected to be identical to those specified for V2G communications specified in SAE J2836/1. Where relevant, the specification notes interactions that may be required between the vehicle and vehicle operator, but does not formally specify them. Similarly, communications between the on-board charging sub-system and the on-board vehicle
Hybrid - EV Committee
This specification covers a corrosion-resistant steel in the form of bars, wire, forgings, and forging stock.
AMS F Corrosion and Heat Resistant Alloys Committee
This specification covers a blend of chromium carbide and a nickel-chromium alloy in the form of powder.
AMS F Corrosion and Heat Resistant Alloys Committee
This document provides recommendations involving BEV battery data retention and battery design that enhance the potential for BEV battery reuse and serviceability and that can improve recyclability. These recommendations have been developed by a group of professionals skilled in the secondary-use of batteries and in the research, development, and manufacture of BEV batteries and battery systems.
Secondary Battery Use Committee
This study focuses on a hydrogen ejector for a proton exchange membrane fuel cell (PEMFC) with a maximum power of 150 kW. Experimental tests were conducted to obtain the operating parameters of the stack under 100 kW and 150 kW conditions, which were used as simulation boundary conditions. A three-dimensional numerical model of the ejector was established and validated. Based on this model, the effects of key structural parameters—including nozzle throat radius (Rnt), nozzle position (NXP), mixing chamber radius (Rm), diffuser outlet radius (Rde), secondary flow inlet radius (Rs), suction chamber radius (Rf), and constant-pressure mixing chamber length (Lpm)—on ejector performance were systematically analyzed. The results indicate that Rnt and Rf are negatively correlated with ejector performance, while Rs and Lpm are positively correlated. In contrast, NXP, Rm, and Rde exhibit an optimal range, leading to a single-peak characteristic in ejector performance. This research provides a
Liu, GuoqingTai, ShupengXi, FuqiangLi, ZongjiJi, ShaoboWang, XiuyuWei, Hui
Stochastic preignition (SPI) or low-speed preignition (LSPI) is an abnormal combustion phenomenon observed in downsized turbocharged direct-injection spark-ignition engines at highly boosted conditions. SPI results from the ignition of the air-fuel mixture from a fuel or oil droplet or a detached deposit before the spark discharge, and its occurrence can lead to extremely high peak pressures and severe knock, which can cause physical damage to the engine. This phenomenon limits the downsizing and boosting potential of direct-injection spark-ignition engines, thereby constraining the efficiency benefits that can be achieved. The propensity for SPI to occur is impacted by engine operating conditions as well as the properties of the fuel, fuel additives, lubricant, and lubricant additives. To mitigate its occurrence, it is important to understand the factors that impact the frequency of SPI events. As this abnormal combustion phenomenon is relatively recent, there was a lack of a standard
Gopujkar, SiddharthDavis, RichardWorm, JeremyTuma, NicShukla, PrajwalReilly, VeronicaChapman, ElanaCiaravino, JosephSeyfried, Philipp
This part of SAE J514 covers general and dimensional specifications for 37 degree flared tube fittings. Also included are 37 degree flared fittings with NPTF pipe threads in Appendix B. These fittings are intended for general application in hydraulic systems on industrial equipment and commercial products. These fittings are capable of providing leak-proof, full flow connections in hydraulic systems operating at working pressures as specified in Table 6. Since many factors influence the pressure at which a hydraulic system will or will not perform satisfactorily, the values shown in Table 6 should not be construed as a guaranteed minimum. For any application, it is recommended that sufficient testing be conducted and reviewed by both the user and fitting manufacturer to assure that performance levels will be safe and satisfactory.
Hydraulic Tube Fittings Committee
This SAE Recommended Practice is intended to provide basic information on properties and characteristics of high-strength carbon and alloy steels which have been subjected to special die drawing. This includes both cold drawing with heavier-than-normal drafts and die drawing at elevated temperatures.
Metals Technical Committee
This Information Report relates to a special class of automotive adaptive equipment which consists of modifications to the power brake booster systems provided as original equipment of motor vehicles. These modifications are generically called "Reduced Effort Power Brakes" (REPB) The purpose of the modification is to lower the amount of driver effort required to apply the brakes. Retention of reliability, ease of use and maintainability for disabled drivers, passengers, and the general public is of primary concern. Reduced Effort Power Brake modifications should be qualified by the tests referenced in the Recommended Test Procedure. The tests set forth in that procedure should be applied, and failure of a Reduced Effort Power Brake modification to meet those tests should disqualify the modification from the claim of meeting the specifications of this Information Report. Because this is an Information Report, the numerical values for performance measurements presented in this report and
Adaptive Devices Standards Committee
In electrified vehicles, auxiliary components can represent a dominant source of noise, one of which is the refrigerant scroll compressor. Compared with vehicles equipped with internal combustion engines, electrified vehicles require larger refrigerant compressors, as thermal management is needed not only for the passenger compartment but also for the battery and electric drive components. Excitation mechanisms within the compressor, arising from the cyclic compression process and the eccentric motion of the scroll, induce housing vibrations and result in airborne sound radiation. To investigate the vibroacoustic noise generation mechanisms of a scroll compressor, operational vibrations were analysed using accelerometers and three-dimensional laser scanning vibrometry. In addition, the radiated sound was characterised using microphones and near-field sound intensity measurements. The results demonstrate a strong correlation between surface vibrations and airborne sound radiation, with
Saur, LukasBeer, GabrielFritzsche, MarcoBecker, Stefan
The vibro-acoustic performance of a vehicle is a critical factor in customer perception of quality and comfort, yet optimizing for Noise, Vibration, and Harshness (NVH)—specifically road noise—presents a persistent challenge in the modern automotive development cycle. While advanced Finite Element Method (FEM) analysis is essential, the increasing complexity and volume of CAE simulation data often overwhelm manual interpretation, potentially leading to prolonged development times or compromises in final comfort quality. To address these challenges, this paper introduces the application of CDH/ACE (Autonomous Computational Experiments), a framework that integrates conventional CAE simulation workflows with advanced machine learning in an iterative, cyclic process. This creates an exceptionally user-friendly and self-correcting system that autonomously defines, performs, and learns from computational experiments. By leveraging machine learning algorithms to build robust predictive models
Visser, Rene
Simulations can only be searched, reused and leveraged as training data for machine learning methods if suitable metadata are related. Manually obtaining these metadata is time-consuming and requires expert knowledge. Consequently, there often is a lack of metadata and this prohibits the reutilization of simulation data. Therefore, automated frameworks for metadata extraction are essential to obtain metadata information quickly, effortlessly and cost-efficiently. At present, there are no toolboxes for Finite-Element-Simulation data. Nevertheless, machine learning methods are a promising solution for this task. Training classical supervised machine learning methods for metadata generation often faces the lack of labeled data since manual labelling can be very costly. Therefore, rule-based extraction algorithms are used as an alternative for fundamental metadata extraction. For more enhanced tasks they are often not feasible. Active Learning is a suitable technique to overcome this
Luegmair, MarinusGröttrup, Sören
Monitoring inputs and states of a structural dynamic system is often challenging, as direct measurements are costly or even infeasible. A virtual sensing methodology is presented for jointly estimating the input and state of a structure when subjected to multi-directional base excitations. The approach uses a tuned Kalman Filter combined with a model-order reduction of the system model to ensure a low computational cost whilst allowing accurate estimation from a limited number of acceleration measurements. This enables real-time virtual health monitoring strategies and reduction in instrumentation during data acquisition without additional information such as location and direction of application about the inputs. The proposed methodology is validated numerically and experimentally using a notched aluminum beam excited on a multi-directional shaker table, driven simultaneously in two in-plane directions. The study demonstrates accurate full-field estimation of multiple responses along
Salazar Colunga, RodrigoPandiya, NimishDindorf, ChristianNaets, Frank
The rapid electrification of the automotive industry introduces new challenges in noise, vibration, and harshness (NVH). In particular, in a virtual prototyping phase of the e-vehicles development, the rubber mounts are often one of the key elements to be considered when analysing the structure borne noise contributions. Having an accurate experimental characterization of the mount dynamic stiffness curves is therefore very relevant. However, conventional mount characterization methods are often pushed to their limits, partly due to the use of stiffer bushings, and partly because the frequency range of interest is extended toward higher frequencies. When using inverse substructuring, the dynamic stiffness curves can be obtained from frequency response function measurements. The required test setup consists of excitations and responses, located on each side of the mount via dedicated fixtures. The measured frequency response functions are reduced into 6 degrees of freedom representation
Bianciardi, FabioForrier, BartMinervini, DomenicoBarbieri, MarcoJanssens, Karl
This study investigates the NVH characteristics of the spline coupling that connects the motor and reducer shafts in an electric drive unit, using flexible multibody dynamics simulations. Focusing on the source stage of the NVH analysis process, the excitation force magnitude and spline trajectory are examined under various spline design conditions. The study compares spline fit types (side fit vs. major fit), clearance vs. interference conditions, and variations in tooth number and module size. This study analyzes the overall behavior of spline excitation forces under various design conditions, complementing prior research focused mainly on specific causes or manufacturing improvements. Side fit splines exhibit lower first-order excitation forces compared to major fit splines, but significantly higher excitation forces at higher orders. This leads to increased spline trajectory amplitude and amplified whirling of the input shaft. Since the input gear is directly coupled to the input
Kim, Dong-JunHwang, Seung GyuKim, DongheeKim, Seon HyeongLee, SangHanGrant, GeorgeHalse, Christopher
Realistic seat vibration reproduction is essential for delivering authentic haptic cues and enhancing driver immersion in driving simulators. Unlike direct playback of road recordings, simulator applications require vibration synthesis that responds interactively to driver inputs and vehicle dynamics. Reproducing these vibrations at the seat is often complicated by actuator bandwidth limitations and the dynamic behaviour of the seat structure itself, which can alter the intended target response. This work presents vibration synthesis and seat dynamics compensation strategies implemented on a single-axis seat vibration reproduction system equipped with a vertical actuator. Frequency Response Functions (FRFs) were measured to characterise the system dynamics under single-axis excitation. Run-up and coast-down tests were conducted on the seat and compared to target responses measured on an actual vehicle under operational conditions. Several seat dynamics compensation strategies were
Muthu Chaiphas, Joshua DanielCuenca, JacquesBianciardi, FabioColangeli, ClaudioDeckers, ElkeDenayer, HervéJanssens, Karl
Noise pollution is a major environmental and health challenge, yet its strong spatial and temporal variability makes comprehensive mapping highly complex. Current approaches under the European Noise Directive (END) provide only partial coverage and often lack temporal dynamics. The NoiseSphere project, funded by the Austrian Research Promotion Agency FFG, develops an AI-based methodology for dynamic, large-scale noise prediction and mapping. A machine learning model is trained on heterogeneous data sources, including semantically enriched open Sentinel-2 satellite imagery, OpenStreetMap road data and existing noise maps. The model is refined through integration of noise emission data and validated using targeted in-situ measurements. A case study in an urban environment (Graz, Austria) demonstrates the model’s applicability. By combining remote sensing, traffic dynamics, and machine learning, NoiseSphere enables predictive noise mapping even in regions not covered by current
Girstmair, Josef
High-frequency whine from electric drive systems has become a critical issue restricting the improvement of vehicle sound quality. Traditional evaluation methods struggle to accurately identify masked whine risks in the early research and development (R&D) phase, due to incomplete hardware of prototype vehicles and high interior background noise. This often leads to problems being delayed until the mass production stage, resulting in high rectification costs. To address this issue, this paper proposes and validates an early risk assessment method based on the Tone-to-Noise Ratio (TNR). First, the generation mechanism of Electric Drive (E-Drive) whine is systematically analyzed, identifying the electromagnetic noise of the electric motor and the gear whine of the reducer as the two dominant noise sources. To address this bottleneck, the TNR psychoacoustic metric is introduced to quantify the perceptual salience of tonal noise relative to background noise, which effectively mitigates the
Yun, ZhaoHui, HuiGao, PanXiao, ZhongdiZan, ChenTeng, Charlie
Achieving best-in-class Noise, Vibration, and Harshness (NVH) in electric powertrains demands a paradigm shift in development methodology. This paper presents a practice-oriented overview of simulation methods in NVH development methodology for electric drive units. This includes target cascading and multi-objective optimisation, and by attacking NVH at the source using KPIs early in the design cycle, significant reductions in development time and reliance on traditional testbed loops are realised. Machine learning (Neural Network) algorithms are utilized to find the best-in-class design, using multi-objective optimisation as well as refining simulation accuracy by adding tolerance effects while target cascading ensures alignment of system-level performance objectives down to subsystem contributions. Combined, these strategies enable rapid and robust NVH optimisation, using simulation for next-generation electric powertrain development. Several applications and real-life examples
Mehrgou, MehdiGarcia de Madinabeitia, InigoGraf, BernhardGojo, Josef
The simulation of structure-borne energy flow within a full vehicle trimmed body at mid and high frequencies has always been a challenge due to the large computational cost associated with standard deterministic simulations. This is a particularly pressing problem given that the electrification of the vehicles is extending the presence of structure-borne sources to higher frequencies. While the improvement of computational hardware has allowed OEMs to shift the limit of standard Finite Element (FE) approaches to higher frequencies, no methods have been proposed in the literature that tackle the full frequency range for industrial-sized problems. In this paper, a simulation methodology that uses wave-based processing of the original low-frequency finite element input deck to compute the coupling loss factors is proposed to model structure-borne noise in complex systems at mid and high frequencies. The methodology is validated against numerical and experimental data.
Errico, FabrizioLegault, JulienMordillat, PhilippeZerrad, Mehdi
In vehicles with electrified powertrains, high-frequency tonal noise components have become increasingly prominent and can be perceived as particularly annoying by the driver. While recent advancements in international standardization — such as ECMA-74 [1] and ECMA-418 [2] — have led to powerful new algorithms for tonal noise visualization and analysis, including Tonality-Heatmaps, the measurement side still lacks sensor setups that adequately reflect the spatial sensitivity of noise, especially for tonal components. This challenge is amplified in enclosed vehicle cabins, where room modes create local minima and maxima that become increasingly dense at higher frequencies. As a result, even small head movements can lead to noticeable differences in perceived tonal noise. Current measurement approaches do not sufficiently account for this spatial variability. This contribution addresses the absence of tailored solutions for the driver’s position by introducing an improved microphone
Schecker, DanielRittenschober, Thomas
In this study, we propose a methodology for predicting the acoustic modes and natural frequencies of a sedan using artificial intelligence and demonstrate the feasibility of controlling its acoustic characteristics by modifying the hole distribution of the package tray. In typical sedan structures, the cabin cavity and trunk cavity are acoustically coupled through holes in the package tray. The distribution of these holes significantly affects the natural acoustic modes and frequencies of the vehicle. However, once the exterior shape of the vehicle is finalized during the design stage, options for structural modifications to mitigate noise issues caused by these modes become extremely limited. To address this challenge efficiently, we develop a deep learning-based neural network model trained on data derived from a simplified acoustic analysis model of a sedan that includes a package tray. Finite element analysis is performed to generate acoustic modes and natural frequencies, which
Lee, Jin WooCho, JaehoNam, YounsicHan, Yongha
As acoustic requirements for NVH trim components become increasingly constrained by mass, cost, and sustainability targets, traditional approaches to inner dash design based on spatially averaged Transmission Loss (TL) metrics are reaching their practical limits. In fully built vehicles, the acoustic performance of the inner dash is governed by its global insulation capability but also by strong spatial heterogeneity and its interaction with spatially distributed noise sources such as the power unit, gearbox, and tyre-road excitation. This paper presents a test-based methodology for the spatial optimisation of inner dash acoustic performance using reciprocal holography. By applying a calibrated sound power source within the vehicle cabin and measuring the reciprocal response in the engine bay and wheel-arch regions, a high-resolution spatial Transmission Loss “hologram” of the inner dash is obtained under in-situ conditions. The resulting spatial data enables the identification of
Harry, EvanEandi, Giacomo