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Viewing 1 to 30 of 1576
2010-10-05
Technical Paper
2010-01-2011
Carsten John
Geometric product representations are of gaining importance in product manufacturing industries. Several case studies yield that the utilization of three-dimensional digital product data in the product development chain has given many manufacturing companies a big advantage in business competition. The field of application for 3D technology is versatile and its further implementation still proceeds along product delivery processes. Leveraging 3D graphics in service information creation processes like the creation of manual illustrations or service instruction imagery is currently a big topic at many companies. E. g. the utilization of animated 3D product representations for explanation of service tasks becomes possible due to the recent advances in computer hardware more and more popular.
2010-04-12
Technical Paper
2010-01-0457
Zhenhai Gao, Fei Gao, Lifei Duan
According to the process that a new driver becomes a low skill level driver and finally a skilled driver from learning how to drive, especially in light of the understanding on the vehicle lateral dynamics that will change from linear characteristic under low speed to strong nonlinear character under high speed, a novel driver model is established. At low speed linear range, off-line optimization based on genetic tuning is introduced into the model to get the optimal control parameters which is viewed as a basic understanding of the vehicle dynamic characteristics of a low skill level driver. On basis of the previous established model, neural network adaptive mechanism is introduced to the driver model which enables the driver to adjust the control online even at high speed non-linear area, reflecting a deeper understanding of the vehicle dynamic model. At last, simulation has been taken in order to verify the correctness and accuracy of the model.
2010-10-10
Technical Paper
2010-01-1679
Dragan Aleksendric, Velimir Cirovic
Wear of brake friction materials were found to be a complex combination of abrasion, adhesion, fatigue, delamination, and thermal decomposition. Stochastic nature of wear of brake friction materials is result of these wear mechanisms and their transition from one combination to another. The dominant wear mechanism of brake friction materials is influenced by braking regimes and friction material characteristics. Regarding friction material characteristics, the most important influences are related to its formulation and manufacturing conditions. Prediction of friction materials wear versus their manufacturing conditions can be considered as an important issue for further friction materials development.
2010-10-10
Technical Paper
2010-01-1697
Jaroslaw Grochowicz, Karl-Heinz Wollenweber, Carlos Agudelo, Harald Abendroth
Modern project management including brake testing includes the exchange of reliable results from different sources and different locations. The ISO TC22/SWG2-Brake Lining Committee established a task force led by Ford Motor Co. to determine and analyze root causes for variability during dynamometer brake performance testing. The overall goal was to provide guidelines on how to reduce variability and how to improve correlation between dynamometer and vehicle test results. This collaborative accuracy study used the ISO 26867 Friction behavior assessment for automotive brake systems. Future efforts of the ISO task force will address NVH and vehicle-level tests. This paper corresponds to the first two phases of the project regarding performance brake dynamometer testing and presents results, findings and conclusions regarding repeatability (within-lab) and reproducibility (between-labs) from different laboratories and different brake dynamometers.
2010-05-05
Technical Paper
2010-01-1539
Bin Wang, Xuexun Guo
On road simulation, both the traditional iterative method based on frequency response function (FRF) and adaptive control method based on the CARMA model are realized by using linear model to identify the target test system. However the real test system is very complicated because of various nonlinear factors. Linear models approximately describe the system only in a small range. Therefore, system simulation methods can not be used to validate the developed control algorithm and the uncertainty of test accordingly increases. As mentioned above, this paper presents a model to identify the nonlinear test system using NARMA dynamic neural network and discusses how to make the model parameters in detail. Using the test input-output series data, this network was trained by Levenberg-Marquardt method. Results of verification simulation show the validation of the nonlinear model.
2010-10-10
Technical Paper
2010-01-1698
Andreas Bender, Karl Haesler, Claus Thomas, Jaroslaw Grochowicz
Brake system development and testing is spread over vehicle manufacturers, system and component suppliers. Test equipment from different sources, even resulting from different technology generations, different data analysis and report tools - comprising different and sometimes undocumented algorithms - lead to a difficult exchange and analysis of test results and, at the same time, contributes to unwanted test variability. Other studies regarding the test variability brought up that only a unified and unambiguous data format will allow a meaningful and comparative evaluation of these data and only standardization will reveal the actual reasons of test variability. The text at hand illustrates that a substantial part of test variability is caused by a misinterpretation of data and/or by the application of different algorithms.
2010-04-12
Technical Paper
2010-01-0743
Christof Paar, Andy Rupp, Kai Schramm, Andre Weimerskirch, Marko Wolf
Due to economic, environmental and political reasons, there is an increasing demand for zero-emission vehicles. With the wide-scale deployment of electric car systems, a variety of parties with conflicting interests will be interacting, and there will be incentives for dishonest behavior. Consequently, new technical challenges that are related to IT security and embedded security arise in the context of electric vehicle systems. For instance, payment and metering needs to be secured, privacy needs to be preserved, and the infrastructure needs to be protected. This work investigates for the first time the security threats that must be addressed in intelligent transportation systems, it discusses possible solutions, and it presents the benefits that IT security provides in this context.
2011-04-12
Journal Article
2011-01-0204
Hagen Stübing, Attila Jaeger, Nikolas Wagner, Sorin A. Huss
Intelligent networking of cars and infrastructure (Car-to-X, C2X) by means of dedicated short range communication represents one of the most promising attempts towards enhancement of active safety and traffic efficiency in the near future. Nevertheless, as an open and decentralized system, Car-to-X is exposed to various attacks against security and driver's privacy. This paper presents an approach for enhancing security and privacy on physical layer, i.e., already during sending and receiving of messages. This technique is called Secure Beamforming. In previous works we deployed a simulation-based approach for defining an antenna-array appropriate for most of the safety-related use cases as defined by the Car-to-Car Communication Consortium (C2C-CC). In this paper we demonstrate a concept for integrating Secure Beamforming into an overall Car-to-X system architecture.
2011-04-12
Technical Paper
2011-01-0090
Wei Liu, Wenku Shi Sr
In this paper, a Magneto-Rheological (MR) fluid semi-active suspension system was tested on a commercial vehicle, a domestic light bus, to determine the performance improvements compared to passive suspensions. MR fluid is a material that responds to an applied magnetic field with a significant change in its rheological behavior. When the magnetic field is applied, the properties of such a fluid can change from a free-flowing, low viscosity fluid to a near solid, and this change in properties takes place in a few milliseconds and is fully reversible. A quarter suspension test rig was built out to test the nonlinear performance of MR damper. Based on a large number of experimental data, a phenomenological model of MR damper based on the Bouc-Wen hysteresis model was adopted to predict both the force-displacement behavior and the complex nonlinear force-velocity response.
2013-04-08
Technical Paper
2013-01-1418
Christian Schleiffer, Marko Wolf, André Weimerskirch, Lars Wolleschensky
The need for vehicular data security and privacy protection is already enormous and increases even further. Prominent application areas are for instance theft protection, anti-counterfeiting, secure data storage and secure communication inside the vehicle and from the vehicle to the outside world. However, most of the vehicular security and privacy protection solutions involve modern cryptography and require availability of cryptographic keys in the vehicle and in related backend infrastructure. A central aspect for ensuring this availability and a controlled usage of such cryptographic keys is a secure key management, which affects the whole lifecycle of the key, from creation and distribution, usage, backup and update up to key deactivation.
2013-04-08
Journal Article
2013-01-1324
Ravi Anand
Measuring Overall Equipment Effectiveness (OEE) is important for identifying areas of improvement within a Manufacturing Process such as Quality, Performance and Availability losses. In many cases one maybe related to the other. This paper explores how “OEE on the Cloud Computing platform” offers a unique value proposition that can greatly help reduce Production downtime and other losses. This solution enables the enterprise to monitor the production process in real-time and in a cost effective manner. This paper also analyzes some of the challenges faced in implementing such a solution and means of overcoming these challenges.
2013-04-08
Journal Article
2013-01-1230
Steffen Ostendorff, Joerg Sachsse, Heinz-Dietrich Wuttke, Jorge Meza Escobar
This paper presents an adaptive test approach to improve the structural testing of printed circuit boards (PCB) found in electronic automotive components. The approach makes use of FPGAs available on the PCBs, and its applicability is supported by the global trend taking place in the automotive industry of replacing ASICs with programmable devices such as FPGAs. For structural testing of PCBs, Boundary Scan (BScan) is mostly used. However, BScan has the disadvantage of being a static test method due to the slow execution speed reducing the fault coverage concerning dynamic faults. FPGAs support BScan as well, but they also offer a vast number of programmable resources. These resources can be configured for testing purposes. Our approach is to speed-up the testing process during the PCB manufacturing by moving data intensive processing from the external software side (Test-PC) to the programmable hardware side on-board (FPGA), reducing the data transfer over the slow JTAG interface.
2013-09-08
Technical Paper
2013-24-0138
Ivan Arsie, Andrea Cricchio, Matteo De Cesare, Cesare Pianese, Marco Sorrentino
The paper describes suited methodologies for developing Recurrent Neural Networks (RNN) aimed at estimating NOx emissions at the exhaust of automotive Diesel engines. The proposed methodologies particularly aim at meeting the conflicting needs of feasible on-board implementation of advanced virtual sensors, such as neural network, and satisfactory prediction accuracy. Suited identification procedures and experimental tests were developed to improve RNN precision and generalization in predicting engine NOx emissions during transient operation. NOx measurements were accomplished by a fast response analyzer on a production automotive Diesel engine at the test bench. Proper post-processing of available experiments was performed to provide the identification procedure with the most exhaustive information content. The comparison between experimental results and predicted NOx values on several engine transients, exhibits high level of accuracy.
2013-09-08
Technical Paper
2013-24-0019
Marco Costa, Gian Bianchi, Claudio Forte
Nowadays, an automotive DI Diesel engine is demanded to provide an adequate power output together with limit-complying NOx and soot emissions so that the development of a specific combustion concept is the result of a trade-off between conflicting objectives. In other words, the development of a low-emission DI diesel combustion concept could be mathematically represented as a multi-objective optimization problem. In recent years, genetic algorithm and CFD simulations were successfully applied to this kind of problem. However, combining GA optimization with actual CFD-3D combustion simulations can be too onerous since a large number of simulations is usually required, resulting in a high computational cost and, thus, limiting the suitability of this method for industrial processes.
2004-03-08
Technical Paper
2004-01-0363
Gary Rushton, Amy Wesley, Armen Zakarian, Tigran Grigoryan
Not all software tools are created equal and not all software tools are created to perform the same tasks. Therefore, different software tools are used to perform different tasks. However, being able to share the information between the different software tools, without having to manually re-enter (duplicate) any of the information, can save a lot of time and improve the quality of the product. The control software interface presented in this paper, allows system engineers to exchange data between software tools in an efficient manner which maximizes each tools capabilities and ultimately reduces development time and improves the quality of the product.
2004-03-08
Technical Paper
2004-01-0426
Han Qiang, Yang Fuyuan, Zhou Ming, Ouyang Minggao
The large amount of controllable fuel injection parameters of Diesel engine equipped with high pressure common-rail fuel injection system makes the control of combustion more flexible, and also makes the workload of calibration and optimization much heavier. For higher efficiency, model-based approaches are presented and researched. This contribution presents a new method for modeling which is constituted by Neural Network and Adaptive Network-based Fussy Inference System (ANFIS). The experiment is carried out on a 6-cylinder common rail diesel engine. The analysis and experiment show that effective modeling can be achieved using this method.
2004-03-08
Technical Paper
2004-01-1574
Andrew J. Barber, Thomas E. Renner, Shawn You, Gary S. Sandlass, Anders Maki
Recent studies have shown that complex vehicle components such as shock absorbers, rubber bushings, and engine mounts can be accurately modeled by combining laboratory measurements with neural network technology. These nonlinear dynamic blackbox models (also known as Empirical Dynamics1 models) make it possible to predict nonlinear and hysteretic component behavior over wide ranges of amplitude and frequency. The models can handle realistic input waveforms as well as multiple inputs and multiple outputs. These techniques have now been applied to rolling pneumatic tires, to enable high accuracy predictions of tire and vehicle handling behavior. Models that predict high amplitude force components (three forces and three moments) using up to four randomly-varying inputs (radial deflection, slip angle, and camber angle, and slip ratio) have been successfully generated, using data obtained from MTS Flat-Trac III tire test equipment.
2004-03-08
Technical Paper
2004-01-1360
G. Gnanam, R.T. Burton, S.R. Habibi, M. Sulatisky
This paper describes the design of an intelligent control strategy that would allow the conversion of a gasoline engine to a bi-fuel form with minimal alteration. Conversion of a conventional gasoline engine to a bi-fuel form is easy and can be achieved at a relatively low cost. By using a bi-fuel engine in vehicles, the advantages of both natural gas and gasoline can be exploited. When the natural gas tank empties, the vehicle can be operated on gasoline until it is refueled with natural gas. This paper describes an add-on control module that is developed and applied to an engine for bi-fuel operation. The control scheme uses neural networks and is capable of substantially improving the operation of a bi-fuel engine in terms of emissions. Simulation results are reported.
2004-03-08
Technical Paper
2004-01-1361
Kenichi Mitsuhashi, Takashi Tsuchiya, Shin Morishita, Toshihiko Shiraishi, Hiroshi Sasaki
The performance of various types of control systems for an electric governor of a diesel engine was examined. The amount of fuel injection of a diesel engine is usually controlled by an electric governor system in these decades, and a PID controller is installed for the electric governor. Even when the optimal parameters for PID controller are well tuned, it is difficult to keep constant rotation speed of the engine, because the applied load to generators may vary according to its running conditions. In this study, a neural network was applied to regulate the parameters in the PID controller for the axial-moving DC motor to control the amount of fuel injection. Experimental studies show that the parameter regulation system using neural network presented here showed good performance under various running conditions. Furthermore, it was shown that various types of training algorithms were applied to neural network control systems and their performance was compared.
2004-03-08
Technical Paper
2004-01-1362
Thomas Winsel, Mohamed Ayeb, Heinz J. Theuerkauf, Stefan Pischinger, Christof Schernus, Georg Lütkemeyer
The modern engine design process is characterized by shorter development cycles and a reduced number of prototypes. However, simultaneously exhaust after-treatment and emission testing is becoming increasingly more sophisticated. The introduction of predictive real-time simulation tools that represent the entire powertrain can likely contribute to improving the efficiency of the calibration process. Engine models, which are purely based on physical first principles, are usually not capable of real-time applications, especially if the simulation is focused on cold start and warm-up behavior. However, the initial data definition for the ECU using a Hardware-in-the-Loop (HiL)-Simulator requires a model with both real-time capability and sufficient accuracy. The use of artificial intelligence systems becomes necessary, e.g. neural networks. Methods, structures and the realization of a hybrid real-time model are presented in this paper, which combines physical and neural network models.
2004-03-08
Technical Paper
2004-01-1363
Helge Nareid, Neil Lightowler
New environmental legislation places increasing demands on automobile emission controls, requiring new approaches to engine management and diagnostics systems. This paper demonstrates the use of an Artificial Neural Network (ANN) solution for misfire detection in spark ignition engines. The solution is based on a truly parallel hardware implementation of an ANN. The network is developed by a data-driven training process, using data with known incidences of misfires. No analytical or algorithmic methods need to be developed in order to train or use the ANN for misfire detection. There is minimal processing overhead on the main processor of the engine management unit, freeing resources for alternative engine management tasks or enabling the use of less costly processor solutions.
2004-03-08
Technical Paper
2004-01-1695
Joerg Angstenberger, Viktor Tiederle
The requirements of the link between different applications in the automotive area have been increasing rapidly during the last years. Especially the reliability of the electric/optical interface device for MOST® (Media Oriented System Transport) applications, FOT (fiber optical transceiver), is very important and of concern. The compliance of the required specification (for electrical or optical parameters in the entire environment) is proven by an unique and innovative procedure for automotive components. For the FOT this means also a specific and dedicated qualification procedure to evaluate the portion of the optical data transfer. Today's qualification procedures for semiconductor devices consider electrical and assembly (package) related parameters. The special optical part of the FOT (LED and photodiode) and the accompanying circuits represent a new class of parameters that have to be qualified.
2004-03-08
Technical Paper
2004-01-1193
Herbert M. Guzman, Whitman E. McConnell, Darrin A. Smith
The vehicle dynamics of non-collinear, low-velocity front-to- rear collisions have received little formal study. The twenty-three angled collisions conducted for this project revealed significant vehicle dynamic differences when compared with similar-energy collinear rear-end collisions. Two recent model year vehicles were used to conduct non-collinear collisions at a nominal 12 km/h impact velocity. The pre-collision angles between the test vehicles were established so that the striking vehicle's line of action through its CG was either 15 or 30 degrees from the stationary struck vehicle's initial heading. Both vehicles had accelerometers at their CG's measuring longitudinal and lateral accelerations. The struck vehicle also had sensors to measure CG vertical accelerations, yaw rates, and longitudinal and lateral velocities. Film from three high-speed 16-mm [film] cameras was digitized and analyzed for each collision. The ΔV at various points within the struck vehicle was studied.
2004-03-08
Technical Paper
2004-01-1195
Thomas F. Fugger, Bryan C. Randles, Jerry J. Eubanks
Recent models of General Motors (GM) and selected Ford vehicles may be equipped with an event data recorder (EDR) that records information in the airbag sensing and diagnostic module (GM-SDM) or restraint control module (Ford-RCM). These systems have become a resource to the accident reconstructionist in the analysis of collisions involving data recorder equipped vehicles, as typically the data can be downloaded via the Vetronix Crash Data Retrieval (CDR) System. The purpose of this paper is to investigate the use of the CDR System in pedestrian accidents. A series of impacts using a pedestrian dummy and SDM equipped vehicles were performed. After each test, the SDM was downloaded via the CDR system and the data evaluated. The dummy and vehicle kinematics were documented and the vehicle impact response was compared with the SDM recorded velocity change and impact speed.
2004-03-08
Technical Paper
2004-01-0646
Zhentao Liu, Shaomei Fei
Compressed Natural Gas (CNG)/diesel Dual Fuel Engine(DFE) was one of the best choices for solving energy crisis and environment pollution. In order to study and improve the emission performance of the CNG/diesel DFE, an emission model by means of Radial Basis Function neural network was established. The model identified as a black box model with input-output training data didn't require priori knowledge. There were 100 group experimental data over the operation conditions from low load and low rotate speed to heavy load and high rotate speed using for training the neural network, and 20 group test data using for verifying the model. The study results showed that the predicted results were good agreement with the experimental data. This proves that the developed emission model can be used to successfully predict and optimize the emission performance of DFE.
2004-03-08
Technical Paper
2004-01-0752
Mathieu St-Pierre, Denis Gingras
Land navigation systems need a precise and continuous position in order to function properly. The sensors commonly found in those systems are differential odometer, global positioning system and 2 or 3 axis inertial measurement unit respectively. Two or more of these complementary positioning methods must be integrated together to achieve the required performance at low cost. The integration, which implies the fusion of noisy data provided by each sensor, must be performed in some optimal manner. Most positioning system designers choose the Kalman filter as the data fusion method. An interesting alternative to the Kalman filter is the artificial neural network (ANN). This paper describes the research conducted to evaluate the potential of an ANN as a centralized fusion method and as nonlinear filters for land vehicle positioning.
2004-03-08
Technical Paper
2004-01-0922
Pin Zeng, Dennis N. Assanis
In this paper, a new method for cylinder pressure reconstruction is proposed based on the concept of a dimensionless pressure curve in the frequency domain. It is shown that cylinder pressure profiles, acquired over a wide range of engine speeds and loads, exhibit similarity. Hence, cylinder pressure traces collapse into a set of dimensionless curves within a narrow range after normalization in the frequency domain. The dimensionless pressure traces can be described by a curve-fit family, which can be used for reconstructing pressure diagrams back into the time domain at any desired condition. The accuracy associated with this method is analyzed and its application to engine heat transfer analysis is demonstrated.
2004-03-08
Technical Paper
2004-01-0994
Stefan Pischinger, Christof Schernus, Georg Lütkemeyer, H. J. Theuerkauf, Thomas Winsel, Mohamed Ayeb
The modern engine development process is characterized by shorter development cycles and a reduced number of prototypes. However, simultaneously exhaust after-treatment and emission testing is becoming increasingly more sophisticated. It is expected that predictive simulation tools that encompass the entire powertrain can potentially improve the efficiency of the calibration process. The testing of an ECU using a HiL system requires a real-time model. Additionally, if the initial parameters of the ECU are to be defined and tested, the model has to be more accurate than is typical for ECU functional testing. It is possible to enhance the generalization capability of the simulation, with neuronal network sub-models embedded into the architecture of a physical model, while still maintaining real-time execution. This paper emphasizes the experimental investigation and physical modeling of the port fuel injected SI engine.
2004-10-26
Technical Paper
2004-01-2680
Steve Rogers
Conventional fixed controllers in combination with adaptive neural networks provide a powerful controller architecture. By utilizing the existing controller designs and augmenting them with adaptive neural networks engineers may exploit the merits of both control approaches. By adding on an adaptive component to the existing controller the range of operating conditions is increased and robustness to system degradation is improved. One of the simplest neural network controllers is the adaptive linear combiner. In this paper the adaptive linear combiner is described and the controller architecture is applied to an engine rpm controller. Results are given.
2004-10-26
Technical Paper
2004-01-2669
Steve Rogers, Brian Birge
Optimization for control system design or testing is commonly used. Most of the optimization approaches are based on simplex or gradient descent. If the system is complex these approaches are susceptible to being caught in local minimums. Particle swarm optimization (PSO) is a subset of evolutionary computation, which includes genetic algorithms. Evolutionary search techniques have been introduced as a means of detecting global minimums within a parameter range. PSO has been presented by a number of researchers, with applications in function optimization and neural network training. In this study PSO theory and equations will be detailed. The procedure will be applied to an engine rpm control system and results will be presented. The optimization procedure is used to minimize cumulative error and select parameters for a lead-lag plus integral control system. The simulation was coded in simulink and is shown in the figures.
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