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Vehicle manufacturers are suffering from increasing expenses for fixing software issues. This fact is mainly driving their desire to use mobile communication channels for doing Software Updates Over The Air (SOTA). Software updates today are typically done at vehicle service stations by connecting the vehicles’ electronic network via the On Board Diagnostic (OBD) interface to a service computer. These operations are done under the control of trained technicians. SOTA means that the update process must get handled by the driver. Two critical aspects need to get considered when doing SOTA at Electronic Brake Control (EBC) systems. Both will determine the acceptance of SOTA by legal authorities and by the passengers: The safety and security of the vehicle The availability of the vehicle for the passengers The security aspect includes the necessity to protect the vehicle and the manufacturers IP from unwanted attacks.

In order to detect degradation of engine oil lubricant, bench testing along with a number of diesel-powered Ford trucks were instruments and tested. The purpose of the bench testing was primarily to determine performance aspects such as repeatability, hysteresis effects and so on. Vehicle testing was conducted by designing and installing a separate oil reservoir along with a circulation system which was mounted in the vicinity of the oil pan. An innovative oil sensor was directly installed on the reservoir which can measure five (5) independent oil parameters (viscosity, density, permittivity, conductance, temperature). In addition, the concept is capable of detecting the oil level continuously during normal engine operation. The sensing system consists of an ultrasonic transducer for the oil level detection as well as a Tuning Fork mechanical resonator for the oil condition measurement.

Empirical models for engine-out oxides of Nitrogen (NOx) and smoke emissions have been developed for the purpose of minimizing transient emissions while maintaining transient response. Three major issues have been addressed: data acquisition, data processing and modeling method. Real and virtual transient parameters have been identified for acquisition. Accounting for the phase shift between transient engine events and transient emission measurements has been shown to be very important to the quality of model predictions. Several methods have been employed to account for the transient transport delays and sensor lags which constitute the phase shift. Finally several different empirical modeling methods have been used to determine the most suitable modeling method for transient emissions. These modeling methods include several kinds of neural networks, global regression and localized regression.

Predominant brake noise evaluation and rating was developed many years ago and no longer fulfills the need of modern development work. An extended description of a noisy brake event (European expert group guideline EKB 3006) and a standardized test data exchange format, allowing the comparison of different source test results (EKB 3008) are presented. Today's noise rating systems are described and compared by selected examples. The paper proposes an open 4 level noise rating system (EKB 3007). It starts with simple occurrence statistics, noise rating based on sound levels, situational noise rating including duration and finally based on the human perception, described by psychoacoustics.

The design of a safe transportation system requires numerous design decisions that should be based on data acquired by rigorous scientific method. Naturalistic data collection and analysis methods are a relatively new addition to the engineer's toolbox. The naturalistic method is based on unobtrusively monitoring driver and vehicle performance under normal, everyday, driving conditions; generally for extended collection periods. The method generates a wealth of data that is particularly well-suited for identifying the underlying causes of safety deficiencies. Furthermore, the method also provides robust data for the design and evaluation of safety enhancement systems through field studies. Recently the instrumentation required to do this type of study has become much more cost effective allowing larger numbers of vehicles to be instrumented at a fraction of the cost. This paper will first provide an overview of the naturalistic method including comparisons to other available methods.

MIL-STD-704 defines power quality in terms of transient, steady-state, and frequency-domain metrics that are applicable throughout a military aircraft electric power system. Maintaining power quality in more electric aircraft power systems has become more challenging in recent years due to the increase in load dynamics and power levels in addition to stricter requirements of power system characteristics during a variety of operating conditions. Further, power quality is often difficult to assess directly during experiments and aircraft operation or during data post-processing for the integrated electric power system (including sources, distribution, and loads). While MIL-STD-704 provides guidelines for compliance testing of electric load equipment, it does not provide any instruction on how to assess the power quality of power sources or the integrated power system itself, except the fact that power quality must be satisfied throughout all considered operating conditions.

This study outlines an approach for speeding up the simulation of the dynamic response of vehicle models that include hysteretic nonlinear tire components. The method proposed replaces the hysteretic nonlinear tire model with a surrogate model that emulates the dynamic response of the actual tire. The approach is demonstrated via a dynamic simulation of a quarter vehicle model. In the proposed methodology, training information generated with a reduced number of harmonic excitations is used to construct the tire hysteretic force emulator using a Neural Network (NN) element. The proposed approach has two stages: a learning stage, followed by an embedding of the learned model into the quarter car model. The learning related main challenge stems from the attempt to capture with the NN element the behavior of a hysteretic element whose response depends on its loading history.

Two-poster rig plays a very important role in accelerated durability evaluation in a motorcycle industry, similar to what a four-poster rig does in a car industry. The rig simulates the exact road conditions in the vertical direction through tire coupling by applying feedback control on displacement. On account of its ability to simulate to the exact customer usage conditions, it reproduces the failures realistically as it happens on the field. However, as complete vehicle is required for testing on the rig, the testing happens mostly in the advanced stages of product development. Any failures beyond the concept stage have a huge impact on the development time and cost and the same should be avoided. Therefore, in this paper, a virtual testing methodology is proposed, based on which potential failures on the vehicles can be captured at the concept design stage itself. An ADAMS model of a motorcycle was created.

Hat sections made of steel are frequently encountered in automotive body structural components such as front rails. These components can absorb significant amount of impact energy during collisions thereby protecting occupants of vehicles from severe injury. In the initial phase of vehicle design, it will be prudent to incorporate the sectional details of such a component based on an engineering target such as peak load, mean load, energy absorption, or total crush, or a combination of these parameters. Such a goal can be accomplished if efficient and reliable data-based models are available for predicting the performance of a section of given geometry as alternatives to time-consuming and detailed engineering analysis typically based on the explicit finite element method.

The debate surrounding roof deformation and occupant injury potential has existed in the automotive community for over 30 years. In analysis of real-world rollovers, assessment of roof deformation and occupant compartment space starts with the post-accident roof position. Dynamic movement of the roof structure during a rollover sequence is generally acknowledged but quantification of the dynamic roof displacement has been limited. Previous assessment of dynamic roof deformation has been generally limited to review of the video footage from staged rollover events. Rollover testing for the evaluation of injury potential has typically been studied utilizing instrumented test dummies, on-board and off-board cameras, and measurements of residual crush. This study introduces an analysis of previously undocumented real-time data to be considered in the evaluation of the roof structure's dynamic behavior during a rollover event.

In this research, the interior noise of a passenger car was measured, and the sound quality metrics including sound pressure level, loudness, sharpness, and roughness were calculated. An artificial neural network was designed to successfully apply on automotive interior noise as well as numerous different fields of technology which aim to overcome difficulties of experimentations and save cost, time and workforce. Sound pressure level, loudness, sharpness, and roughness were estimated by using the artificial neural network designed by using the experiment values. The predicted values and experiment results are compared. The comparison results show that the realized artificial intelligence model is an appropriate model to estimate the sound quality of the automotive interior noise. The reliability value is calculated as 0.9995 by using statistical analysis.

Collision avoidance systems for rear-end collisions have been researched and developed. It is necessary to activate collision warnings and automatic braking systems with appropriate timing determined by a monitoring system of a driver's braking action. Although there are various systems to monitor driving behavior, this study aims to create a monitoring system using a driver model. This study was intended to construct a model of a driver's braking action with the Time Delay Neural Network (TDNN). An experimental scenario focuses on rear-end collisions on a highway, such as the driver of a host vehicle controlling the brake to avoid a collision into a leading vehicle in a stationary condition caused by a traffic jam. In order to examine the accuracy of the TDNN model, this study used four parameters: the number of learning, the number of neurons in the hidden layer, the sampling time with 0.01 second as a minimum value, and the number of the delay time.

In order to comply with more and more stringent emission standards, like EU6 which will be mandatory starting in September 2014, GDI engines have to be further optimized particularly in regard of PN emissions. It is generally accepted that the deposition of liquid fuel wall films in the combustion chamber is a significant source of particulate formation in GDI engines. Particularly the wall surface temperature and the temperature drop due to the interaction with liquid fuel spray were identified as important parameters influencing the spray-wall interaction [1]. In order to quantify this temperature drop at combustion chamber surfaces, surface temperature measurements on the piston of a single-cylinder engine were conducted. Therefore, eight fast-response thermocouples were embedded 0.3 μm beneath the piston surface and the signals were transmitted from the moving piston to the data acquisition system via telemetry.

This paper presents a novel methodology to develop and validate fuel consumption models of Refuse Collecting Vehicles (RCVs). The model development is based on the improvement of the classic approach. The validation methodology is based on recording vehicle drive cycles by the use of a low cost data acquisition system and post processing them by the use of GPS and map data. The corrected data are used to feed the mathematical energy models and the fuel consumption is estimated. In order to validate the proposed system, the fuel consumption estimated from these models is compared with real filling station refueling records. This comparison shows that these models are accurate to within 5%.

Cost reduction in the automotive industry becomes a widely-adopted operational strategy not only for Original Equipment Manufacturers (OEMs) that take cost leader generic corporation strategy, but also for many OEMs that take differentiation generic corporation strategy. Since differentiation generic strategy requires an organization to provide a product or service above the industry average level, a premium is typically included in the tag price for those products or services. Cost reduction measures could increase risks for the organizations that pursue differentiation strategy. Although manufacturers in the automotive industry dramatically improved production efficiency in past ten years, they are still facing the pressure of cost control. The big challenge in cost control for automakers and suppliers is increasing prices of raw materials, energy and labor costs. These costs create constraints for the traditional economic expansion model.

Laser cladding is a method of material deposition through which a powdered or wire feedstock material is melted and consolidated by use of a laser to coat part of a substrate. Determining the parameters to fabricate the desired clad bead geometry for various configurations is problematic as it involves a significant investment of raw materials and time resources, and is challenging to develop a predictive model. The goal of this research is to develop an experimental methodology that minimizes the amount of data to be collected, and to develop a predictive model that is accurate, adaptable, and expandable. To develop the predictive model of the clad bead geometry, an integrated five-step approach is presented. From the experimental data, an artificial neural network model is developed along with multiple regression equations.

This paper proposes a new method of predicting the sound absorption performance of polymer wool using artificial neural networks (ANN) model. Some important parameters of the proposed model have been adjusted to best fit the non-linear relationship between the input data and output data. What's more, the commonly used multiple non-linear regression model is built to compare with ANN model in this study. Measurements of the sound absorption coefficient of polymer wool based on transfer function method are also performed to determine the sound absorption performance according to GB/T18696. 2-2002 and ISO10534- 2: 1998 (E) standards. It is founded that predictions of the new model are in good agreement with the experiment results.

On-board fuel identification is important to ensure engine safe operation, similar power output, fuel economy and emissions levels when different fuels are used. Real-time detection of physical and chemical properties of the fuel requires the development of identifying techniques based on a simple, non-intrusive sensor. The measured crankshaft speed signal is already available on series engine and can be utilized to estimate at least one of the essential combustion parameters such as peak pressure and its location, rate of cylinder pressure rise and start of combustion, which are an indicative of the ignition properties of the fuel. Using a dynamic model of the crankshaft numerous methods have been previously developed to identify the fuel type but all with limited applications in terms of number of cylinders and computational resources for real time control.

Career paths are not something that one can predict. They are as much about being in the right spot at the right time with the desired skill set as they are about having a detailed, calculated plan. How does one go from being a young Original Equipment Manufacturer (OEM) test engineer to being an airline Senior Vice President of Safety, Security and Compliance and the joint industry/FAA co-chair of the Commercial Aviation Safety Team? It is a bit unusual that a non-pilot ends up on an airline Operations Specification listed as the Federal Aviation Regulations (FAR) Part 119 Director of Safety for one of the largest airlines in the world. Engineering background and experience were key stepping stones on that journey along with a healthy dose of skepticism. An initial assignment to make an airline's safety program robust, credible and data driven, much like the very successful aircraft reliability programs, set the direction and path forward.

One of the hardest tasks involving wind tunnel characterization is to determine the air-flow condition inside the test section. The Log-Tchebycheff method and the Equal Area method allow calculation of local velocities from measured differential pressures on rectangular and circular ducts. However, these two standard methods for air flow measurement are limited by the number of accurate pressure readings by the Pitot tube. In this paper, a new approach is presented for wind tunnel calibrations. This approach is based on a limited number of dynamic pressure measurements and a predictive technique using Neural Network (NN). To optimize the NN, the extended great deluge (EGD) algorithm is used. Wind tunnel testing involves a large number of variables such as wind direction, velocity, rate flow, turbulence characteristics, temperature variation and pressure distribution on airfoils.