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The effects of charge motion and laminar flame speeds on combustion and exhaust temperature have been studied by using an air jet in the intake flow to produce an adjustable swirl or tumble motion, and by replacing the nitrogen in the intake air by argon or CO₂, thereby increasing or decreasing the laminar flame speed. The objective is to examine the "Late Robust Combustion" concept: whether there are opportunities for producing a high exhaust temperature using retarded combustion to facilitate catalyst warm-up, while at the same time, keeping an acceptable cycle-to-cycle torque variation as measured by the coefficient of variation (COV) of the net indicated mean effective pressure (NIMEP). The operating condition of interest is at the fast idle period of a cold start with engine speed at 1400 RPM and NIMEP at 2.6 bar. A fast burn could be produced by appropriate charge motion. The combustion phasing is primarily a function of the spark timing.

This paper presents the final phase of a study to develop the modeling methodology for an advanced steering assembly with a safety-enhanced steering wheel and an adaptive energy absorbing steering column. For passenger cars built before the 1960s, the steering column was designed to control vehicle direction with a simple rigid rod. In severe frontal crashes, this type of design would often be displaced rearward toward the driver due to front-end crush of the vehicle. Consequently, collapsible, detachable, and other energy absorbing steering columns emerged to address this type of kinematics. These safety-enhanced steering columns allow frontal impact energy to be absorbed by collapsing or breaking the steering columns, thus reducing the potential for rearward column movement in severe crashes. Recently, more advanced steering column designs have been developed that can adapt to different crash conditions including crash severity, occupant mass/size, seat position, and seatbelt usage.

Motivated by a combination of increasing consumer demand for fuel efficient vehicles, more stringent greenhouse gas, and anticipated future Corporate Average Fuel Economy (CAFE) standards, automotive manufacturers are working to innovate in all areas of vehicle design to improve fuel efficiency. In addition to improving aerodynamics, enhancing internal combustion engines and transmission technologies, and developing alternative fuel vehicles, reducing vehicle weight by using lighter materials and/or higher strength materials has been identified as one of the strategies in future vehicle development. Weight reduction in vehicle components, subsystems and systems not only reduces the energy needed to overcome inertia forces but also triggers additional mass reduction elsewhere and enables mass reduction in full vehicle levels.

This paper quantifies the potential of electric propulsion systems to reduce petroleum use and greenhouse gas (GHG) emissions in the 2030 U.S. light-duty vehicle fleet. The propulsion systems under consideration include gasoline hybrid-electric vehicles (HEVs), plug-in hybrid vehicles (PHEVs), fuel-cell hybrid vehicles (FCVs), and battery-electric vehicles (BEVs). The performance and cost of key enabling technologies were extrapolated over a 25-30 year time horizon. These results were integrated with software simulations to model vehicle performance and tank-to-wheel energy consumption. Well-to-wheel energy and GHG emissions of future vehicle technologies were estimated by integrating the vehicle technology evaluation with assessments of different fuel pathways. The results show that, if vehicle size and performance remain constant at present-day levels, these electric propulsion systems can reduce or eliminate the transport sector's reliance on petroleum.

Engine dynamometer testing was performed comparing fuels having different octane ratings and ethanol content in a Ford 3.5L direct injection turbocharged (EcoBoost) engine at three compression ratios (CRs). The fuels included midlevel ethanol “splash blend” and “octane-matched blend” fuels, E10-98RON (U.S. premium), and E85-108RON. For the splash blends, denatured ethanol was added to E10-91RON, which resulted in E20-96RON and E30-101 RON. For the octane-matched blends, gasoline blendstocks were formulated to maintain constant RON and MON for E10, E20, and E30. The match blend E20-91RON and E30-91RON showed no knock benefit compared to the baseline E10-91RON fuel. However, the splash blend E20-96RON and E10-98RON enabled 11.9:1 CR with similar knock performance to E10-91RON at 10:1 CR. The splash blend E30-101RON enabled 13:1 CR with better knock performance than E10-91RON at 10:1 CR. As expected, E85-108RON exhibited dramatically better knock performance than E30-101RON.

Since the environment of vehicle operation is dynamic in nature, dynamic methods should be used in vehicle durability analysis. Due to the constraints in current computer resources, simulation of vehicle durability tests and structural fatigue life assessment need special approaches and efficient CAE tools. The purpose of this paper is to present an efficient methodology and a feasible vehicle dynamic durability analysis process. Two examples of structural durability analysis using transient dynamics are given. The examples show that vehicle stress analysis and fatigue life prediction using dynamic method is now feasible by employing the presented method and process.

This paper assesses the potential improvement of automotive powertrain technologies 25 years into the future. The powertrain types assessed include naturally-aspirated gasoline engines, turbocharged gasoline engines, diesel engines, gasoline-electric hybrids, and various advanced transmissions. Advancements in aerodynamics, vehicle weight reduction and tire rolling friction are also taken into account. The objective of the comparison is the potential of anticipated improvements in these powertrain technologies for reducing petroleum consumption and greenhouse gas emissions at the same level of performance as current vehicles in the U.S.A. The fuel consumption and performance of future vehicles was estimated using a combination of scaling laws and detailed vehicle simulations. The results indicate that there is significant potential for reduction of fuel consumption for all the powertrains examined.

Seat Effective Amplitude Transmissibility (SEAT) values can be obtained from direct measurements at seat track and top or estimated from transmissibility data and seat track input. Vertical transmissibility was measured for sixteen seats and six subjects on the Ford Vehicle Vibration Simulator, and these 96 functions used to estimate the seat top response for rough road input. SEAT values were calculated, and good correlation to values computed from direct seat top measurements obtained (R2 of 0.86). Averaging transmissibilities and direct seat measurements over the 6 subjects to obtain correlations for the 16 seats improved R2 to 0.94, validating this approach.

NASA has long recognized the advantages of providing improved information interfaces to EVA astronauts and has pursued this goal through a number of development programs over the past decade. None of these activities or parallel efforts in industry and academia has so far resulted in the development of an operational system to replace or augment the current extravehicular mobility unit (EMU) Display and Controls Module (DCM) display and cuff checklist. Recent advances in display, communications, and information processing technologies offer exciting new opportunities for EVA information interfaces that can better serve the needs of a variety of NASA missions. Hamilton Sundstrand Space Systems International (HSSSI) has been collaborating with Simon Fraser University and others on the NASA Haughton Mars Project and with researchers at the Massachusetts Institute of Technology (MIT), Boeing, and Symbol Technologies in investigating these possibilities.

Drive shaft modelling effects frontal crash finite element simulation. A 35 mph rigid barrier impact of a body on frame SUV with an one piece drive shaft and a unibody SUV with a two piece drive shaft have been studied and simulated using finite element analyses. In the model, the drive shaft can take significant load in frontal impact crash. Assumptions regarding the drive shaft model can change the predicted engine motion in the simulation. This change influences the rocker @ B-pillar deceleration. Two modelling methods have been investigated in this study considering both joint mechanisms and material failure in dynamic impact. Model parameters for joint behavior and failure should be determined from vehicle design information and component testing. A body on frame SUV FEA model has been used to validate the drive shaft modeling technique by comparing the simulation results with crash test data.

Computer simulation of extravehicular activity (EVA) is increasingly being used in planning and training for EVA. A space suit model is an important, but often overlooked, component of an EVA simulation. Because of the inherent difficulties in collecting angle and torque data for space suit joints in realistic conditions, little data exists on the torques that a space suit’s wearer must provide in order to move in the space suit. A joint angle and torque database was compiled on the Extravehicular Maneuvering Unit (EMU), with a novel measurement technique that used both human test subjects and an instrumented robot. Using data collected in the experiment, a hysteresis modeling technique was used to predict EMU joint torques from joint angular positions. The hysteresis model was then applied to EVA operations by mapping out the reach and work envelopes for the EMU.

The Vehicle Exhaust Emissions Simulator was developed to evaluate the performance of vehicle emissions sampling and analytical systems. The simulator produces a representative tailpipe volume flow rate containing up to five emission constituents, injected via mass flow controllers (MFCs). Eliminating the variability of test results associated with the vehicle, driver, and dynamometer makes the simulator an ideal quality control tool for use in commissioning new test cells, checking data correlation between test cells, and evaluating overall system performance. Earlier vehicle emissions simulators being used in the industry were primarily for checking Constant Volume Samplers (CVSs) and Bag Benches but they did not have the ability to properly simulate tailpipe volume.

Most people still remember the introduction of the IBM PC in 1981 and the first Microsoft Windows operating system in 1985. These were the pioneering technologies that started a revolution in automotive test equipment in the service bay. What was once a purely mechanical garage environment where information was published annually in large paper manuals has evolved into a highly technical computing environment. Today vehicle networks link onboard vehicle control systems with diagnostic systems and updated service information is published daily over the Internet. A lot has changed over the last twenty years, and manufacturers of diagnostic test equipment are learning to deal with the constantly evolving computing platforms and host operating systems. This paper traces the history of automotive diagnostic equipment at Ford Motor Company and shares some of the hard lessons learned from the early systems.

The challenge of developing a robust, real-time driver gaze classification system is that it has to handle difficult edge cases that arise in real-world driving conditions: extreme lighting variations, eyeglass reflections, sunglasses and other occlusions. We propose a single-camera end-toend framework for classifying driver gaze into a discrete set of regions. This framework includes data collection, semi-automated annotation, offline classifier training, and an online real-time image processing pipeline that classifies the gaze region of the driver. We evaluate an implementation of each component on various subsets of a large onroad dataset. The key insight of our work is that robust driver gaze classification in real-world conditions is best approached by leveraging the power of supervised learning to generalize over the edge cases present in large annotated on-road datasets.

The article presents an innovative approach to the implementation of a robust design optimization solution in an automobiles assembly process. The approach of the entire project is specific to the 6 Sigma optimization process, by applying the DMAIC cycle integrated in a robust engineering approach for rendering lean the final product assembly process. According to the improvement cycle, the aspects specific for such a process are presented sequentially starting with the “Define” phase for presenting the encountered problem and continuing with the presentation of the scope of the project and its objectives. The “Improvement” cycle phase is applied by the analysis of the monitored 6 Sigma metrics (defined during the previous “Measure” phase and the cause and effect analysis, done during a brainstorming meeting developed during the “Analyze” phase). There follows a proposal for the innovative robust solution by which the assembly process is optimized.

To better reflect real world driving conditions, the EPA 5-Cycle Fuel Economy method encompasses high vehicle speeds, aggressive vehicle accelerations, climate control system use and cold temperature conditions in addition to the previously used standard City and Highway drive cycles in the estimation of vehicle fuel economy. A standard Powersplit Hybrid Electric Vehicle (HEV) system simulation environment has long been established and widely used within Ford to project fuel economy for the standard EPA City and Highway cycles. Direct modeling and simulation of the complete 5-Cycle fuel economy test set for HEV's presents significant new challenges especially with respect to modeling vehicle thermal management system and interactions with HEV features and system controls. It also requires a structured, systematic approach to validate the key elements of the system models and complete vehicle system simulations.

To manage the function of a vehicle's engine, transmission, and related subsystems, almost all modern vehicles make use of one or more electronic controllers running embedded software, henceforth referred to as a Powertrain Controller System or PCS. Fully validating this PCS is a necessary step of vehicle development, and the validation process requires extensive amounts of testing. Within the automotive industry, more and more of this validation testing is being performed using Hardware-in-the-Loop (HIL) simulators to automate the extensive test sequences. A HIL simulation typically mates the physical PCS to a closed-loop real time computer simulation of a powertrain. Interfacing the physical PCS hardware to a powertrain simulation requires the HIL simulator to have extensive signal input/output (I/O) electronics and simulated actuator electrical loading.

The viscous criterion (V*C) has been proposed by biomechanics researchers as a generic biomechanical index for potential soft tissue injury. It is defined by the product of the velocity of deformation and the instantaneous compression of torso and abdomen. This criterion requires calculation and differentiation of measured torso/abdomen compression data. Various computational algorithms for calculating viscous criterion are reviewed and evaluated in this paper. These include methods developed by Wayne State University (WSU), NHTSA (DOT) and Ford. An evaluation has been conducted considering the accuracy of these algorithms with both theoretical and experimental data from dummy rib compressions obtained during a crash test. Based on these results, it is found that: V*C results depend on the scheme used in the computation process, the sampling rate and filtering of original raw data. The NHTSA method yields the lowest V*C value.

This paper provides an overview of spark-ignition engine unburned hydrocarbon emissions mechanisms, and then uses this framework to relate measured engine-out hydrocarbon emission levels to the processes within the engine from which they result. Typically, spark-ignition engine-out HC levels are 1.5 to 2 percent of the gasoline fuel flow into the engine; about half this amount is unburned fuel and half is partially reacted fuel components. The different mechanisms by which hydrocarbons in the gasoline escape burning during the normal engine combustion process are described and approximately quantified. The in-cylinder oxidation of these HC during the expansion and exhaust processes, the fraction which exit the cylinder, and the fraction oxidized in the exhaust port and manifold are also estimated.

QFD, FMEA, Process Improvement, Taguchi, Simultaneous Engineering, PDP, Project Management, DVP, DOE, …and the list goes on. Today's automotive product design engineers face a myriad of “tools” (methodologies, techniques, procedures) that are expected to be mastered and used in the course of performing their job. The list continually grows with new tools being added to the existing ones. And each new tool has an associated acronym to add to the confusion. New and inexperienced engineers are often confused by these tools being tossed at them …school did not cover all this ! The experienced engineer is often skeptical. After all, “if I have been a successful engineer for 20 years, why do I need to start doing these things now?” Nevertheless, most of these tools are truly needed by engineers today in order to be competitive in the increasingly complex and sophisticated world of automotive product design.