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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.

This paper uses a model which calculates the flame kernel formation and its early development in spark ignition engines to examine the causes of cycle-to-cycle combustion variations. The model takes into account the primary physical factors influencing flame development. The spark-generated flame kernel size and temperature required to initialize the computation are completely determined by the breakdown energy and the heat conduction from burned region to unburned region. In order to verify the model, the computation results are compared with high-speed Schlieren photography flame development data from an operating spark-ignition engine; they match remarkably well with each other at all test conditions. For the application of this model to the study of cycle-to-cycle variation of the early stage of combustion, additional input is required.

A model for calculating the residual gas fraction in spark ignition engines has been formulated. The model accounts explicitly for the contribution due to the back flow of exhaust gas to the cylinder during the valve overlap period. The model has been calibrated with in-cylinder hydrocarbon measurements at different values of intake pressure, engine speed, and valve overlap timings.

Homogenous Charge Compression Ignition (HCCI) engine operation has been demonstrated using both residual trapping and residual re-induction. A number of production valve train technologies can accomplish either of these HCCI modes of operation. Wide-scale testing of the many valve timing and duration options for an HCCI engine is both time and cost prohibitive, thus a modeling study was pursued to investigate optimal HCCI valve-train designs using the geometry of a conventional gasoline Port-Fuel-Injected (PFI) Spark-Ignition (SI) engine. A commercially available engine simulation program (WAVE), as well as chemical kinetic combustion modeling tools were used to predict the best approaches to achieving combustion across a wide variety of valve event durations and timings. The results of this study are consistent with experimental results reported in the literature: both residual trapping and residual re-induction are possible strategies for HCCI combustion.

A novel engine management strategy for achieving fast catalyst light-off without the use of an exhaust air pump in a port-fuel-injected, spark ignition engine was developed. A conventional 4-cylinder engine was operated with three cylinders running rich and the fourth one as an air pump to supply air to the exhaust manifold. Under steady-state cold coolant conditions, this strategy achieved near total oxidation of CO and HC with sufficiently retarded spark timing, resulting in a 400% increase in feedgas enthalpy flow and a 90% reduction in feedgas HC emissions compared to conventional operation. The strategy was also evaluated for crank starts. Using the existing engine hardware, implementing the strategy resulted in a reduction in catalyst light-off time from 28.0 seconds under conventional operation to 9.1 seconds.

This paper presents a numerical model of the rotational and lateral dynamics of the piston (secondary motion) and piston slap in mixed lubrication. Piston dynamic behavior, frictional and impact forces are predicted as functions of crank angle. The model considers piston skirt surface waviness, roughness, skirt profile, thermal and mechanical deformations. The model considers partially-flooded skirt and calculates the pressure distributions and friction in the piston skirt region for both hydrodynamic and boundary lubrication. Model predictions are compared with measurements of piston position using gap sensors in a single-cylinder engine and the comparison between theory and measurement shows remarkable agreement.

A twin-land oil control ring (TLOCR) model is used to evaluate TLOCR friction and the results are compared to the experiment measurement in a single cylinder floating liner engine under motoring condition. The model is based on a correlation between the hydrodynamic pressure and film thickness, which is generated using a deterministic model. The well-known three-regime lubrication is predicted with the model for ring with different ring tensions under various engine running conditions. A good match is found for the model and experiment results.

A fuel volatility model based on the major species present in the fuel has been formulated. The model accurately predicts the ASTM distillation curves and Reid Vapor Pressure for hydrocarbon fuels. The model may be used to assess the fuel effects on the extent of evaporation and the vapor composition in the mixture preparation process.

This paper describes the use of a modified quasi-dimensional spark-ignition engine simulation code to predict the extent of cycle-to-cycle variations in combustion. The modifications primarily relate to the combustion model and include the following: 1. A flame kernel model was developed and implemented to avoid choosing the initial flame size and temperature arbitrarily. 2. Instead of the usual assumption of the flame being spherical, ellipsoidal flame shapes are permitted in the model when the gas velocity in the vicinity of the spark plug during kernel development is high. Changes in flame shape influence the flame front area and the interaction of the enflamed volume with the combustion chamber walls. 3. The flame center shifts due to convection by the gas flow in the cylinder. This influences the flame front area through the interaction between the enflamed volume and the combustion chamber walls. 4. Turbulence intensity is not uniform in cylinder, and varies cycle-to-cycle.

Cranking and startup fuel control has become increasingly important due to ever tightening emission requirements. Additionally, engine-off strategies during idle will require substantially more engine startup events with the associated need for very clean starts. Thus, knowledge of an engine's Air-Fuel Ratio (AFR) during its early cycles is necessary in order to optimize cranking and startup fueling. This paper examines and compares two methods of measuring an engine's AFR during engine startup (approximately the first second of operation); an in-cylinder technique using a Fast Flame Ionization Detector (FFID) and the conventional exhaust based Universal Exhaust Gas Oxygen (UEGO) sensor method. Engine starts using a Ford Zetec engine were performed at three different temperatures (0, 20 and 90 C) as well as different initial engine starting positions.

Non-petroleum based liquid fuels are essential for reducing oil dependence and greenhouse gas generation. Increased substitution of alcohol fuel for petroleum based fuels could be achieved by 1) use in high efficiency spark ignition engines that are employed for heavy duty as well as light duty operation and 2) use of methanol as well as ethanol. Methanol is the liquid fuel that is most efficiently produced from thermo-chemical gasification of coal, natural gas, waste or biomass. Ethanol can also be produced by this process but at lower efficiency and higher cost. Coal derived methanol is in limited initial use as a transportation fuel in China. Methanol could potentially be produced from natural gas at an economically competitive fuel costs, and with essentially the same greenhouse gas impact as gasoline. Waste derived methanol could also be an affordable low carbon fuel.

Low volume manufacturing has become increasingly important for the automotive industry. Globalization trends have led automakers and their suppliers to operate in developing regions where minimum efficient scales can not always be achieved. With proper maintenance, standard cast iron stamping tools can be used to produce millions of parts, but require large investments. Thus at high production volumes, the impact of the tooling investment on individual piece costs is minimized. However, at low volumes there is a substantial cost penalty. In light of the trends towards localized manufacturing and relatively low demands in some developing markets, low cost stamping tools are needed. Several alternate tooling technologies exist, each of which require significantly lower initial investments, but suffer from greatly reduced tool lives. However, the use of these technologies at intermediate to high volumes requires multiple tool sets thus eliminating their cost advantage.

An adaptive air/fuel ratio controller for SI engine throttle transient was developed. The scheme is based on an event- based, single- parameter fuel dynamics model. A least- square- error algorithm with an active forgetting factor was used for parameter identifications. A one- step- look- ahead controller was designed to maintain the desired air/fuel ratio by canceling the fuel dynamics with the controller setting updated adaptively according to the identified parameters. When implemented on a Ford Ztech engine and tested under a set of throttle- transient operations, the adaptive controller learned quickly and performed well.

Shafaat and Kenley in 2015 identified the opportunity to improve System Engineering Standards by incorporating the design principle of learning. The System Level Assessment (SLA) Methodology is an approach that fulfills this need by efficiently capturing the learnings of a team of subject matter experts in the early stages of product system design. By gathering expertise, design considerations are identified that when used with market and business requirements improve the overall quality of the product system. To evaluate the effectiveness of this approach, the methodology has been successfully applied over 400 times within each realm of the New Product Introduction process, including most recently to a Technology Development program (in the earliest stages of the design process) to assess the viability of various electrification technologies under consideration by an automotive Tier 1 supplier.

Rare earths are a group of elements whose availability has been of concern due to monopolistic supply conditions and environmentally unsustainable mining practices. To evaluate the risks of rare earths availability to automakers, a first step is to determine raw material content and value in vehicles. This task is challenging because rare earth elements are used in small quantities, in a large number of components, and by suppliers far upstream in the supply chain. For this work, data on rare earth content reported by vehicle parts suppliers was assessed to estimate the rare earth usage of a typical conventional gasoline engine midsize sedan and a full hybrid sedan. Parts were selected from a large set of reported parts to build a hypothetical typical mid-size sedan. Estimates of rare earth content for vehicles with alternative powertrain and battery technologies were made based on the available parts' data.

Simulations used to estimate carbon dioxide (CO2) emissions and fuel consumption of medium- and heavy-duty vehicles over prescribed drive cycles often employ engine fuel maps consisting of engine measurements at numerous steady-state operating conditions. However, simulating the engine in this way has limitations as engine controls become more complex, particularly when attempting to use steady-state measurements to represent transient operation. This paper explores an alternative approach to vehicle simulation that uses a “cycle average” engine map rather than a steady state engine fuel map. The map contains engine CO2 values measured on an engine dynamometer on cycles derived from vehicle drive cycles for a range of generic vehicles. A similar cycle average mapping approach is developed for a powertrain (engine and transmission) in order to show the specific CO2 improvements due to powertrain optimization that would not be recognized in other approaches.

Commercial automotive diesel engine service and repair, post a diagnostic trouble code trigger, relies on standard troubleshooting steps laid down to identify or narrow down to a faulty engine component. This manual process is cumbersome, time-taking, costly, often leading to incorrect part replacement and most importantly usually associated with significant downtime of the vehicle. Current study aims to address these issues using a novel in-house simulation-based approach developed using a Digital Twin of the engine which is capable of conducting in-mission troubleshooting with real world vehicle/engine data. This cost-effective and computationally efficient solution quickly provides the cause of the trouble code without having to wait for the vehicle to reach the service bay. The simulation is performed with a one-dimensional fluid dynamics, detailed thermodynamics and heat transfer-based diesel engine model utilizing the GT-POWER engine performance tool.

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.