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SAE International Journal of Alternative Powertrains

  • Journal
  • V129-8EJ
To be published on 2020-06-30 by SAE International in United States
This is the electronic format of the journal.

SAE International Journal of Transportation Safety

  • Journal
  • V129-9EJ
To be published on 2020-06-30 by SAE International in United States
This is the electronic format of the journal

SAE International Journal of Transportation Cybersecurity and Privacy

  • Journal
  • V129-11EJ
To be published on 2020-06-30 by SAE International in United States
This is the electronic format of the journal.
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A Numerical Investigation on VVA Influence on the Combustion Phase for Premixed Combustion Engine Under Partial Load Conditions

DMMM - Politecnico di Bari-Sergio Camporeale
DMMM - Politecnico di Bari, GNFM INDAM-Francesco Fornarelli
  • Technical Paper
  • 2020-37-0005
To be published on 2020-06-23 by SAE International in United States
Nowadays, the vehicle hybridization and the use of more clean fuel in heavy-duty applications brings to a new beginning in the use of spark ignition engine. In standard intake system, the pre-mixed fuel air mixture is controlled by the injection of fuel after the throttle valve. Then, intake system, consisting in intake duct, valve number and geometry and cylinder head shape influence the characteristics of the intake flow within the cylinder up to the ignition of the combustion by the spark plug. The technology advancement in fluid-power and electrical actuation gives the opportunity to decouple the intake and exhaust valve actuation with respect to the standard cam shaft distribution. The Variable Valve Actuation (VVA) concepts is not new, but its application is now affordable and flexible enough to be applied in partial load conditions. Here, by means of three-dimensional numerical simulations the intake and combustion process is studied with a finite volume approach to solve the mass, momentum and energy equations together with an Extended Coherent Flamelet Model (ECFM). Two different approaches in driving the…
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A Diagnostic Technology of Powertrain Parts that Cause Abnormal Noises using Artificial Intelligence

Hanyang University-Kyoungjin Noh, Joon-Hyuk Chang
Hyundai Motor Company-Insoo Jung, Dongchul Lee, Dongkyu Yoo, Kibeen Lim
  • Technical Paper
  • 2020-01-1565
To be published on 2020-06-03 by SAE International in United States
In general, when a problem occurs in a component, various phenomena appear, and abnormal noise is one of them. The service technicians diagnose the noise through the analysis using hearing and equipment. Depending on their experiences, the analysis time and diagnosis accuracy vary widely. The newly developed AI-based diagnostic technology diagnoses parts that cause abnormal noises within seconds when a noise is input to the equipment. To create a learning model for diagnosis, we collected as many abnormal noises as possible from various parts, and selected good and bad data. This process is very important in the development of diagnostic techniques. Artificial intelligence was learned by deep learning with selected good data. This paper is about the technology that can diagnose the abnormal noises generated from the engine, transmission, drivetrain and PE (Power Electric) parts of the eco-friendly vehicle through the diagnosis model composed of various methods of deep learning.
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Challenges in Vibroacoustic Vehicle Body Simulation Including Uncertainties

BMW AG-Marinus Luegmair, Michal Styrnik
Technical University of Munich / BMW AG-Johannes D. Schmid
  • Technical Paper
  • 2020-01-1571
To be published on 2020-06-03 by SAE International in United States
For many years, the model quality and frequency range of NVH simulation with Finite Element (FE) models have been increased and led to a better vehicle quality. Nowadays, model range and quality are on such a high fidelity and there is often no further improvement, even with extreme modelling and computation effort. So in order to improve the quality of predictions, the next step is to take uncertainties into account. With this approach there are many challenges on the way to valid and useful simulation models and they can be divided into three areas: the input uncertainties, the propagation of uncertainties through the FE model and finally the statistical output quantities. Each of them must be investigated to choose sufficient methods for a valid and fast prediction of vehicle body vibroacoustics. With a discrimination of different types of uncertainties it can be shown that the dimensionality of the corresponding random space is tremendously high. Therefore, a substantial reduction of the dimensionality is crucial. Next step is to choose a proper method to model uncertainties and…
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Efficient Modeling and Simulation of the Transverse Isotropic Stiffness and Damping Properties of Laminate Structures using the Finite Element Method

BMW Group-Vlad Somesan, Endre Barti
Fraunhofer Lbf-Thilo Bein
  • Technical Paper
  • 2020-01-1573
To be published on 2020-06-03 by SAE International in United States
The Noise Vibration and Harshness (NVH) characteristics and requirements of vehicles are changing as the automotive manufacturers turn their focus from developing and producing cars propelled by internal combustion engines (ICE) to electrified vehicles. This new strategic orientation enables them to offer products that are more efficient and environmentally friendly. Although electric powertrains have many advantages compared to their established predecessors they also produce new challenges that make it more difficult to match the new requirements especially regarding NVH. Electric motors are one of the most important sources of vibrations in electric vehicles. In order to address the new challenges in developing powertrains that match the acoustic comfort requirements of the customers and also shape the development process as efficiently as possible, car manufacturers use numerical simulation methods to identify NVH problems as early in the design process as possible. Numerically describing the dynamic properties of electric motor components such as the stator or rotor is proving to be especially difficult as they contain heterogeneous parts that have viscoelastic orthotropic or transverse isotropic stiffness and…
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Root Cause Analysis and Structural Optimization of E-Drive Transmission

AVL LIST GmbH-Thomas Resch
AVL-AST d.o.o.-Borislav Klarin, Ivan Grozdanovic, Denis Pevec
  • Technical Paper
  • 2020-01-1578
To be published on 2020-06-03 by SAE International in United States
We face a growing demand for so-called eAxles (electric axle drive) in vehicle development. An eAxle is a compact electric drive solution for full electric vehicles (and P4 hybrids) with integrated electric machine and transmission. The transmission can be rather simple using fixed gear with cylindrical gear steps but increasing demands on power and speed range as well as efficiency increase its complexity with planetary stages or switchable gear steps. Such an electro-mechanic system has different behavior than the classical ICE-driven powertrains, for example regarding NVH, where high frequency and tonal noise from gear whining and electro-magnetic excitation is an important comfort issue that needs to be understood and controlled. As knowledge base for such drives is currently low, development needs to be supported by methodologies, which are not only on high predictive level for NVH responses, but also allow a detailed understanding and insight into the causes and reasons of a certain behavior to identify noise effects and to accelerate learning for such systems. In addition, such methods should lead to the possibility to…
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Simulating and Optimizing the Dynamic Chassis Forces of the Audi e-tron

Audi AG-Stefan Uhlar
  • Technical Paper
  • 2020-01-1521
To be published on 2020-06-03 by SAE International in United States
With battery electric vehicles (BEV), due to the absence of the combustion process, the rolling noise comes even more into play. The BEV technology also leads to different concepts of how to mount the electric engine in the car. Commonly, also applied with the Audi e-tron, the rear engine is mounted on a subframe, which again is connected to the body structure. This concept leads to a better insulation in the high frequency range, yet it bears some problems in designing the mounts for ride comfort (up to 20Hz) or body boom (up to 70Hz). Commonly engine mounts are laid-out based on driving comfort (up to 20Hz). The current paper presents a new method to find an optimal mount design (concerning the stiffness) in order to reduce the dynamic chassis forces which are transferred to the body up to 100Hz. This directly comes along with a reduction of the sound pressure level for the ‘body boom’ phenomena. Here we use multibody simulation along with a sophisticated tire model in the time domain in order to…
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Time Domain Full Vehicle Interior Noise Calculation from Component Level Data by Machine Learning

Mercedes-Benz AG-Dimitrios Ernst Tsokaktsidis, Clemens Nau
Technical University of Munich-Steffen Marburg
  • Technical Paper
  • 2020-01-1564
To be published on 2020-06-03 by SAE International in United States
Computational models directly derived from data gained increased interest in recent years. Data-driven approaches have brought breakthroughs in different research areas such as image-, video- and audio-processing. Often denoted as Machine Learning (ML), these approaches are not widely applied in the field of vehicle Noise, Vibration and Harshness (NVH) yet. Related works mainly discuss the topic with respect to structural health monitoring, psychoacoustics, traffic noise and as improvement to existing numerical simulation methods. Vehicle interior noise is a major quality criterion for today’s automotive development. To estimate noise levels early in the development process, deterministic system descriptions are created by utilizing time-consuming measurement techniques. This paper examines whether pattern-recognizing algorithms are suitable to improve the prediction process for a steering system. Starting from operational measurements, a procedure to calculate the sound pressure level in the passenger cabin is developed and investigated. Component time domain data serves as basis for the computation. The important inputs are determined by a correlation analysis. Input selection is followed by data reduction. After preprocessing, a supervised learning environment is established.…