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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.

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.

Advances in avionics and display technology are significantly changing the cockpit environment in current transport aircraft. The MIT Aeronautical Systems Lab (ASL) has developed a part-task flight simulator specifically to study the effects of these new technologies on flight crew situational awareness and performance. The simulator is based on a commercially-available graphics workstation, and can be rapidly reconfigured to meet the varying demands of experimental studies. The simulator has been successfully used to evaluate graphical microburst alerting displays, electronic instrument approach plates, terrain awareness and alerting displays, and ATC routing amendment delivery through digital datalinks.

The adoption of turbine engines for automotive power plants has been hampered by the high cost, high leakage and high wear rate of present designs of ceramic-matrix regenerators. Proposals are made and analyzed here for design directions to achieve substantial improvements in all three areas. These include lower-cost extruded and pressed matrices; and clamping seals coupled with incremental movement of the rotary-regenerator matrix.

This paper presents the results of a study of how people interacted with a production voice-command based interface while driving on public roadways. Tasks included phone contact calling, full address destination entry, and point-of-interest (POI) selection. Baseline driving and driving while engaging in multiple-levels of an auditory-vocal cognitive reference task and manual radio tuning were used as comparison points. Measures included self-reported workload, task performance, physiological arousal, glance behavior, and vehicle control for an analysis sample of 48 participants (gender balanced across ages 21-68). Task analysis and glance measures confirm earlier findings that voice-command interfaces do not always allow the driver to keep their hands on the wheel and eyes on the road, as some assume.

Currently, states that are out of compliance with the National Ambient Air Quality Standards must, according to the Clean Air Act Amendments of 1990 (CAAA), develop and implement control strategies that demonstrate specific degrees of reduction in emissions-with the degree of reduction depending upon the severity of the problem. One tool that has been developed to aid regulators in both deciding an appropriate course of action and to demonstrate the desired reductions in mobile emissions is EPA's Mobile 5a emission estimation model. In our study, Mobile 5a has been used to examine the effects of regulatory strategies, as applied to the Northeast United States, on vehicle emissions under worst-case ozone-forming conditions.

The existing vehicle designs exhibit a high level of coupling. For instance the coupling in the suspension and steering systems manifests itself through the change in wheel alignment parameters (WAP) due to suspension travel. This change in the WAP causes directional instability and tire-wear. The approach of the industry to solve this problem has been twofold. The first approach has been optimization of suspension link lengths to reduce the change in WAP to zero. Since this is not possible with the existing architecture, the solution used is the optimization of the spring stiffness K to get a compromise solution for comfort (which requires significant suspension travel and hence a soft spring) and directional stability (which demands least possible change in wheel alignment parameters and hence a stiff spring).

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.

Knowledge mapping is a first and mandatory step in creation of system architecture. This paper considers the conceptual modeling of automotive systems, and discusses the creation of a knowledge-based model with respect to the Object Process Methodology an approach used in designing intelligent systems by depicting them using object models and process models. With this knowledge, systems engineer should consider what a product is comprised of (its structure), how it operates (its dynamics), and how it interacts with the environment. As systems have become more complex, a prevalent problem in systems development has been the number of accruing errors. A clearly defined and consistent mapping of knowledge regarding structure, operation and interaction is necessary to construct an effective and useful system. An interactive, iterative and consistent method is needed to cope with this complex and circular problem.

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.

Hybrid powertrains with multiple sources of power have generated new control challenges in the automotive industry. Purdue University's participation in EcoCAR 2, an Advanced Vehicle Technology Competition managed by the Argonne National Laboratories and sponsored by GM and DOE, has provided an exciting opportunity to create a comprehensive test-bench for the development and validation of advanced hybrid powertrain control strategies. As one of 15 competing university teams, the Purdue EcoMakers are re-engineering a donated 2013 Chevrolet Malibu into a plug-in parallel- through-the-road hybrid-electric vehicle, to reduce its environmental impact without compromising performance, safety or consumer acceptability. This paper describes the Purdue team's control development process for the EcoCAR 2 competition.

Painting is the most expensive unit operation in automobile manufacturing and the source of over 90 percent of the air, water and solid waste emissions at the assembly plant. While innovative paint technologies such as waterborne or powder paints can potentially improve plant environmental performance, implementing these technologies often requires major capital investment. A process-based technical cost model was developed for examining the environmental and economic implications of automotive painting at the unit operation level. The tradeoffs between potential environmental benefits and their relative costs are evaluated for current and new technologies.

The quest for higher efficiency and performance of automotive vehicles requires application of materials with high strength, stiffness and lower weight in their construction. Particulate-reinforced aluminum-matrix composites are cost-competitive materials, which can meet these requirements. MMCC, Inc. has been optimizing particulate-reinforced alloy systems and developing the Advanced Pressure Infiltration Casting (APIC™) process for the manufacture of components from these materials. This paper discusses the results of a recent study in which composites reinforced with 55 vol.% alumina were cast using two sizes of alumina particulate and eight different matrix alloys based on Al-4.5 wt.% Cu with varying amounts of silicon and magnesium. Optimum heat treatments for each alloy were determined utilizing microhardness studies. The tensile strength and fracture toughness were evaluated as a function of alloy chemistry, particulate size, and heat treatment.

A method for determining the flame contour based on the flame arrival time at the fiber optic (FO) spark plug and at the head gasket ionization probe (IP) locations has been developed. The experimental data were generated in a single-cylinder Ricardo Hydra spark-ignition engine. The head gasket IP, constructed from a double-sided copper-clad circuit board, detects the flame arrival time at eight equally spaced locations at the top of the cylinder liner. Three other IP's were also installed in the cylinder head to provide additional intermediate data on flame location and arrival time. The FO spark plug consists of a standard spark plug with eight symmetrically spaced optical fibers located in the ground casing of the plug. The cylinder pressure was recorded simultaneously with the eleven IP signals and the eight optical signals using a high-speed PC-based data acquisition system.

One of the goals of designing engineering systems is to maximize the system's reliability. A reliable system must satisfy its functional requirements without failure throughout its intended lifecycle. The typical means to achieve a desirable level of reliability is through preventive maintenance of a system; however, this involves cost. A more fundamental approach to the problem is to maximize the system's reliability by preventing failures from occurring. A key question is to find mechanisms (and the means to implement them into a system) that will prevent its system range from going out of the design range. Functional periodicity is a means to achieve this goal. Three examples are discussed to illustrate the concept. In the new electrical connector design, it is the geometric functional periodicity provided by the woven wire structure. In the case of integrated manufacturing systems, it is the periodicity in scheduling of the robot motion.

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 Wankel rotary engine is more compact than conventional piston engines, but its oil and fuel consumption must be reduced to satisfy emission standards and customer expectations. A key step toward this goal is to develop a better understanding of the apex seal lubrication to reduce oil injection while reducing friction and maintaining adequate wear. This paper presents an apex seal dynamics model capable of estimating relative wear and predicting friction, by modeling the gas and oil flows at the seal interfaces with the rotor housing and groove flanks. Model predictions show that a thin oil film can reduce wear and friction, but to a limited extent as the apex seal running face profile is sharp due to the engine kinematics.

The rotary engine provides high power density compared to piston engine, but one of its downside is higher oil consumption. A model of the oil seals is developed to calculate internal oil consumption (oil leakage from the crankcase through the oil seals) as a function of engine geometry and operating conditions. The deformation of the oil seals trying to conform to housing distortion is calculated to balance spring force, O-ring and groove friction, and asperity contact and hydrodynamic pressure at the interface. A control volume approach is used to track the oil over a cycle on the seals, the rotor and the housing as the seals are moving following the eccentric rotation of the rotor. The dominant cause of internal oil consumption is the non-conformability of the oil seals to the housing distortion generating net outward scraping, particularly next to the intake and exhaust port where the housing distortion valleys are deep and narrow.

In this paper we investigate the optimal forming of conical cups of AL 2008-T4 through the use of real-time process control. We consider a flat, frictional binder the force of which can be determined precisely through closed-loop control. Initially the force is held constant throughout the forming of the cup, and various levels of force are tested experimentally and with numerical simulation. Excellent agreement between experiment and simulation is observed. The effects of binder force on cup shape, thickness distribution, failure mode and cup failure height are investigated, and an “optimal” constant binder force is determined. For this optimal case, the corresponding punch force is recorded as a function of punch displacement and is used in subsequent closed-loop control experiments. In addition to the constant force test, a trial variable binder force test was performed to extend the failure height beyond that obtained using the “optimal” constant force level.

Future human space exploration includes returning to the Moon and continuing to Mars. Essential to these missions is each planetary extravehicular activity, or EVA, where astronauts and robotic agents will explore lunar and planetary surfaces. Real-time decision support systems will help these explorers in efficiently planning and re-planning under time pressure sorties. Information and functional requirements for such a system are recommended and are based on on-going human-computer collaboration research.