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The fuel consumption and emissions of diesel engines is strongly influenced by the injection rate pattern, which influences the in-cylinder mixing and combustion process. Knowing the exact injection rate is mandatory for an optimal diesel combustion development. The short injection time of no more than some milliseconds prevents a direct flow rate measurement. However, the injection rate is deduced from the pressure change caused by injecting into a fuel reservoir or pipe. In an ideal case, the pressure increase in a fuel pipe correlates with the flow rate. Unfortunately, real measurement devices show measurement inaccuracies and errors, caused by non-ideal geometrical shapes as well as variable fuel temperature and fuel properties along the measurement pipe. To analyze the thermal effect onto the measurement results, an available rate measurement device is extended with a flexible heating system as well as multiple pressure and temperature sensors.

The Ford GT Program Team was allocated just 22 months from concept to production to complete the Electrical and Electronics systems of the Ford GT. This reduced vehicle program timing - unlike any other in Ford's history -- demanded that the team streamline the standard development process, which is typically 54 months. This aggressive schedule allowed only 12 weeks to design the entire electrical and electronic system architecture, route the wire harnesses, package the components, and manufacture and/or procure all components necessary for the first three-vehicle prototype build.

The High Voltage Power Distribution Unit (PDU) is constructed of magnesium in support of Fuel Cell Electric Vehicle (FCEV) weight reduction efforts. The PDU distributes and controls a nominal 75 kilowatts of power generated by the Fuel Cell, the primary source of High Voltage power, to all the vehicle loads and accessories. The constraints imposed on the design of the PDU resulted in a component highly susceptible to general and galvanic corrosion. Corrosion abatement was the focus of the PDU redesign. This paper describes the redesign efforts undertaken by Ford personnel to improve the part robustness and corrosion resistance.

The performance of three way and NOx catalysts was evaluated on vehicles utilizing non-feedback fuel control and electronic feedback fuel control. The vehicles accumulated 80,450 km (50,000 miles) using fuels representing the extremes in hydrogen-carbon ratio available for commercial use. Feedback carburetion compared to non-feedback carburetion improved highway fuel economy by about 0.4 km/l (1 mpg) and reduced deterioration of NOx with mileage accumulation. NOx emissions were higher with the low H/C fuel in the three way catalyst system; feedback reduced the fuel effect on NOx in these cars by improving conversion efficiency with the low H/C fuel. Feedback had no measureable effect on HC and CO catalyst efficiency. Hydrocarbon emissions were lower with the low H/C fuel in all cars. Unleaded gasoline octane improver, MMT, at 0.015g Mn/l (0.06 g/gal) increased tailpipe hydrocarbon emissions by 0.05 g/km (0.08 g/mile).

The proposed technique is a tailored deep neural network (DNN) training approach which uses an iterative process to support the learning of DNNs by targeting their specific misclassification and missed detections. The process begins with a DNN that is trained on freely available annotated image data, which we will refer to as the Base model, where a subset of the categories for the classifier are related to the automotive theater. A small set of video capture files taken from drives with test vehicles are selected, (based on the diversity of scenes, frequency of vehicles, incidental lighting, etc.), and the Base model is used to detect/classify images within the video files. A software application developed specifically for this work then allows for the capture of frames from the video set where the DNN has made misclassifications. The corresponding annotation files for these images are subsequently corrected to eliminate mislabels.

A study was conducted using Ford's nine standard CFD calibration models as described in SAE paper 940323. The models are identical from the B-pillar forward but have different back end configurations. These models were created for the purpose of evaluating the effect of back end geometry variations on aerodynamic lift and drag. Detailed experimental data is available for each model in the form of surface pressure data, surface flow visualization, and wake flow field measurements in addition to aerodynamic lift and drag values. This data is extremely useful in analyzing the accuracy of the numerical simulations. The objective of this study was to determine the capability of a digital physics based commercial CFD code, PowerFLOW ® to accurately simulate the physics of the flow field around the car-like benchmark shapes.

A hydrogen economy is an increasingly popular solution to lower global carbon dioxide emissions. Previous research has been focused on the economic conditions necessary for hydrogen to be cost competitive, which tends to neglect the effectiveness of greenhouse gas mitigation for the very solutions proposed. The holistic carbon footprint assessment of hydrogen production, distribution, and utilization methods, otherwise known as “well-to-wheels” carbon intensity, is critical to ensure the new hydrogen strategies proposed are effective in reducing global carbon emissions. When looking at these total carbon intensities, however, there is no single clear consensus regarding the pathway forward. When comparing the two fundamental technologies of steam methane reforming and electrolysis, there are different scenarios where either technology has a “greener” outcome.

Ford Motor Company has recently implemented a Hardware-In-the-Loop (HIL) testing system for a new, highly complex, hybrid electric vehicle (HEV) Electronic Control Unit (ECU). The implementation of this HIL system has been quick and effective, since it is based on proven Commercial-Off-The-Shelf (COTS) automation tools for real-time that allow for a very flexible and intuitive design process. An overview of the HIL system implementation process and the derived development benefits will be shown in this paper. The initial concept for the use of this HIL system was a complete closed-loop vehicle simulation environment for Vehicle System Controller testing, but the paper will show that this concept has evolved to allow for the use of the HIL system for many facets of the design process.

Two oxygenated fuels were evaluated on a single-cylinder diesel engine and compared to three hydrocarbon diesel fuels. The oxygenated fuels included canola biodiesel (canola methyl esters, CME) and CME blended with dibutyl succinate (DBS), both of which are or have the potential to be bio-derived. DBS was added to improve the cold flow properties, but also reduced the cetane number and net heating value of the resulting blend. A 60-40 blend of the two (60% vol CME and 40% vol DBS) provided desirable cold flow benefits while staying above the U.S. minimum cetane number requirement. Contrary to prior vehicle test results and numerous literature reports, single-cylinder engine testing of both CME and the 60-40 blend showed no statistically discernable change in NOx emissions relative to diesel fuel, but only when constant intake oxygen was maintained.

In future vehicles (e.g. fuel cell vehicles, hybrid electric vehicles), the electrical system will have an important impact on the mechanical systems in the car (e.g. powertrain, steering). Furthermore, this coupling will become increasingly important over time. In order to develop effective designs and appropriate control systems for these systems, it is important that the plant models capture the detailed physical behavior in the system. This paper will describe models of two electrical components, a battery and a supercapacitor, which have been modeled in two ways: (i) modeling the plant and controller using block diagrams in Simulink and (ii) modeling the plant and controller in Dymola followed by compiling this model to an S-function for simulation in Simulink. Both the battery and supercapacitor model are based on impedance spectroscopy measurements and can be used for highly dynamic simulations.

Combustion in modern spark-ignition (SI) engines is increasingly knock-limited with the wide adoption of downsizing and turbocharging technologies. Fuel autoignition conditions are different in these engines compared to the standard Research Octane Number (RON) and Motor Octane Numbers (MON) tests. The Octane Index, OI = RON - K(RON-MON), has been proposed as a means to characterize the actual fuel anti-knock performance in modern engines. The K-factor, by definition equal to 0 and 1 for the RON and MON tests respectively, is intended to characterize the deviation of modern engine operation from these standard octane tests. Accurate knowledge of K is of central importance to the OI model; however, a single method for determining K has not been well accepted in the literature.

Hat-sections, single and double, made of steel are frequently encountered in automotive body structural components. These components play a significant role in terms of impact energy absorption during vehicle crashes thereby protecting occupants of vehicles from severe injury. However, with the need for higher fuel economy and for compliance to stringent emission norms, auto manufacturers are looking for means to continually reduce vehicle body weight either by employing lighter materials like aluminum and fiber-reinforced plastics, or by using higher strength steel with reduced gages, or by combinations of these approaches. Unlike steel hat-sections which have been extensively reported in published literature, the axial crushing behavior of hat-sections made of fiber-reinforced composites may not have been adequately probed.

With concerns over increasing worldwide demand for gasoline and greenhouse gases, many automotive companies are increasing their product lineup of vehicles to include flex-fuel vehicles that are capable of operating on fuel blends ranging from 100% gasoline up to a blend of 15% gasoline/85% ethanol (E85). For the purpose of this paper, data was obtained that will enable an evaluation relating to the effect the use of E85 fuel has on exhaust system surface temperatures compared to that of regular unleaded gasoline while the vehicle undergoes a typical drive cycle. Three vehicles from three different automotive manufacturers were tested. The surface of the exhaust systems was instrumented with thermocouples at specific locations to monitor temperatures from the manifold to the catalytic converter outlet. The exhaust system surface temperatures were recorded during an operation cycle that included steady vehicle speed operation; cold start and idle and wide open throttle conditions.

In a recent study, quantitative measurements were presented of in-cylinder spatial distributions of mixture equivalence ratio in a single-cylinder light-duty optical diesel engine, operated with a non-reactive mixture at conditions similar to an early injection low-temperature combustion mode. In the experiments a planar laser-induced fluorescence (PLIF) methodology was used to obtain local mixture equivalence ratio values based on a diesel fuel surrogate (75% n-heptane, 25% iso-octane), with a small fraction of toluene as fluorescing tracer (0.5% by mass). Significant changes in the mixture's structure and composition at the walls were observed due to increased charge motion at high swirl and injection pressure levels. This suggested a non-negligible impact on wall heat transfer and, ultimately, on efficiency and engine-out emissions.

Self-piercing rivet (SPR) joints are a key joining technology for lightweight materials, and they have been widely used in automobile manufacturing. Manual visual crack inspection of SPR joints could be time-consuming and relies on high-level training for engineers to distinguish features subjectively. This paper presents a novel machine learning-based crack detection method for SPR joint button images. Firstly, sub-images are cropped from the button images and preprocessed into three categories (i.e., cracks, edges and smooth regions) as training samples. Then, the Artificial Neural Network (ANN) is chosen as the classification algorithm for sub-images. In the training of ANN, three pattern descriptors are proposed as feature extractors of sub-images, and compared with validation samples. Lastly, a search algorithm is developed to extend the application of the learned model from sub-images into the original button images.

This paper presents a design process with data mining technique and advanced optimization strategy. The proposed design method provides insights in three aspects. First, data mining technique is employed for analysis to identify key factors of design variables. Second, relationship between multiple types of size and shape design variables and performance responses can be analyzed. Last but not least, design preference can be initialized based on data analysis to provide priori guidance for the starting design points of optimization algorithm. An exhaust system design problem which largely contributes to the improvement of vehicular Noise, Vibration and Harshness (NVH) performance is employed for the illustration of the process. Two types of design parameters, structural variable (gauge of component) and layout variable (hanger location), are considered in the studied case.

Automotive multiplexing allows sharing information among various intelligent modules inside an automotive electronic system. In order to achieve an optimum functionality, the information should be exchanged among various electronic modules in real time. New features are introduced in automobiles such as Intelligent Vehicle Highway System (IVHS), intelligent transportation support system, engine immobilizers, night vision assistance system, and automated collision avoidance and notification system. The inclusion of such features increases the data traffic over the multiplexing bus. Also, these features require very high speed and expensive bus. Data reduction techniques are used to send the data over a transmission media at high speed. Using the data reduction techniques, we will be able to include new features in automobiles without the need of a high speed bus. Since the automotive environment is different, a special data reduction algorithm is mandated.

Designing a vehicle for superior crash safety performance in consumer rating tests such as US-NCAP is a compelling target in the design of passenger vehicles. In today’s context, there is also a high emphasis on making a vehicle as lightweight as possible which calls for an efficient design. In modern vehicle design, these objectives can only be achieved through Computer-Aided Engineering (CAE) for which a detailed CAD (Computer-Aided Design) model of a vehicle is a pre-requisite. In the absence of the latter (i.e. a matured CAD model) at the initial and perhaps the most crucial phase of vehicle body design, a rational approach to design would be to resort to a knowledge-based methodology which can enable crash safety assessment of an assumed design using artificial intelligence techniques such as neural networks.

The wide applications of automatic sensing devices and data acquisition systems in automotive manufacturing have resulted in a data-rich environment, which demands new data mining methodologies for effective data fusion and information integration to support decision making. This paper presents a new methodology for developing a diagnostic system using manufacturing system data for high-value assets in automotive manufacturing. The proposed method extends the basic attributes control charts with the following key elements: optimal feature subset selection considering multiple features and correlation structure, balancing the type I and type II errors in decision making, on-line process monitoring using adaptive modeling with control charts, and diagnostic performance assessment using shift and trend detection. The performance of the developed diagnostic system can be continuously improved as the knowledge of machine faults is automatically accumulated during production.

In this paper, we proposed a distributed Engineering Computer Aided Learning System. Instead of attending engineering teaching sessions, engineering students are able to interact with the software to gain the same amount of teaching materials. Besides, they will interact with other engineering students from other Engineering schools. The proposed software has the ability to examine the student step by step to reach certain goals. The training and the examination will be different based on the student level and his learning process. Using this system the role of excellent professor can be achieved. The software will have two sessions, i.e. test session and learning session. The software provides the capability of knowledge sharing between multi schools and different educational systems that can provide the students with a large set of training materials. The system was built using JAVA programming language.