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Neutron diffraction is a powerful tool that can be used to identify the phases present and to measure the spacing of the atomic planes in a material. Thus, the residual stresses can be determined within a component and/or the phases present. New intercritically austempered irons rely on the unique properties of the austenite phase present in their microstructures. If these materials are to see widespread use, methods to verify the quality (behavior consistency) of these materials and to provide guidance for further optimization will be needed. Neutron diffraction studies were performed at the second generation neutron residual stress facility (NRSF2) at the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory on a variety of intercritically austempered irons. For similar materials, such as TRIP steels, the strengthening mechanism involves the transformation of metastable austenite to martensite during deformation.

A transient, one-dimensional lean NOx trap (LNT) model is described and implemented for vehicle systems simulations. The model accounts for conservation of chemical species and thermal energy, and includes the effects of O₂ storage and NOx storage (in the form of nitrites and nitrates). Nitrites and nitrates are formed by diffusion of NO and NO₂, respectively, into sorbent particles, and reaction rates are controlled by chemical kinetics and solid-phase diffusion. The model also accounts for thermal aging and sulfation by means of empirical correlations, which have been derived from laboratory experiments. Example simulation results using the Powertrain Systems Analysis Toolkit (PSAT) are presented.

Lignin by-products from biorefineries has the potential to provide a low-cost alternative to petroleum-based precursors to manufacture carbon fiber, which can be combined with a binding matrix to produce a structural material with much greater specific strength and specific stiffness than conventional materials such as steel and aluminum. The market for carbon fiber is universally projected to grow exponentially to fill the needs of clean energy technologies such as wind turbines and to improve the fuel economies in vehicles through lightweighting. In addition to cellulosic biofuel production, lignin-based carbon fiber production coupled with biorefineries may provide $2,400 to $3,600 added value dry Mg−1 of biomass for vehicle applications. Compared to producing ethanol alone, the addition of lignin-derived carbon fiber could increase biorefinery gross revenue by 30% to 300%.

The Friction Stir Spot Welding (FSSW) process is a derivative of the friction stir welding (FSW) process, without lateral movement of the tool during the welding process. It has been applied in the production of aluminum joining for various Mazda and Toyota vehicles. Most of the applications and published studies were concentrated in aluminum sheet in the range of 1.0 to 1.5 mm, suitable for non-structural automotive closure applications. The objective of this study is to study the feasibility of FSSW process for automotive structural aluminum joining, up to 3 mm in thickness, for potentially replacement of self-piercing rivets (SPR) process. Joining thicker aluminum with FSSW tooling with a typical smooth concave shoulder and threaded probing pin, requires long process time, which would not be appropriate in mass-production automotive body construction. In this paper, an innovative FSSW tool with grooved shoulder was developed.

Current artificial intelligence techniques for end to end driving of autonomous vehicles typically rely on a single form of learning or training processes along with a corresponding dataset or simulation environment. Relatively speaking, success has been shown for a variety of learning modalities in which it can be shown that the machine can successfully “drive” a vehicle. However, the realm of real-world driving extends significantly beyond the realm of limited test environments for machine training. This creates an enormous gap in capability between these two realms. With their superior neural network structures and learning capabilities, humans can be easily trained within a short period of time to proceed from limited test environments to real world driving.

Today’s transportation is quickly transforming with the nascent advent of connectivity, automation, shared-mobility, and electrification. These technologies will not only affect our safety and mobility, but also our energy consumption, and environment. As a result, it is of unprecedented importance to understand the overall system impacts due to the introduction of these emerging technologies and concepts. Existing modeling tools are not able to effectively capture the implications of these technologies, not to mention accurately and reliably evaluating their effectiveness with a reasonable scope. To address these gaps, a dynamometer-in-the-loop (DiL) development and testing approach is proposed which integrates test vehicle(s), chassis dynamometer, and high fidelity traffic simulation tools, in order to achieve a balance between the model accuracy and scalability of environmental analysis for the next generation of transportation systems.

Carbon fiber composite use in automobiles and light trucks could dramatically reduce energy use and engine-out emissions. However, worldwide capacity of 28,000 tonnes per year of carbon fiber from polyacrylonitrile (PAN) and petroleum pitch could support limited automotive use. Production of high-volume, industrial-grade fiber from renewable and recycled polymers (lignin, recycled plastics, regenerated cellulosics) could meet automotive demand. Profiles of material volumes, carbon content, and melting points indicate several attractive candidates for production melt-spun carbon fiber feedstocks. Effects on the carbon fiber production cycle and its integration into automotive production are discussed.

Lean Gasoline Direct Injection (LGDI) combustion is a promising technical path for achieving significant improvements in fuel efficiency while meeting future emissions requirements. Though Stoichiometric Gasoline Direct Injection (SGDI) technology is commercially available in a few vehicles on the American market, LGDI vehicles are not, but can be found in Europe. Oak Ridge National Laboratory (ORNL) obtained a European BMW 1-series fitted with a 2.01 LGDI engine. The vehicle was instrumented and commissioned on a chassis dynamometer. The engine and after-treatment performance and emissions were characterized over US drive cycles (Federal Test Procedure (FTP), the Highway Fuel Economy Test (HFET), and US06 Supplemental Federal Test Procedure (US06)) and steady state mappings. The vehicle micro hybrid features (engine stop-start and intelligent alternator) were benchmarked as well during the course of that study.

We utilize physically-based models for diesel exhaust catalytic oxidation and urea-based selective catalytic NOx reduction to study their impact on drive cycle performance of hypothetical light-duty diesel-powered hybrid and plug-in hybrid vehicles (HEVs and PHEVs). The models have been implemented as highly flexible SIMULINK block modules that can be used to study multiple engine-aftertreatment system configurations. The parameters of the NOx reduction model have been adjusted to reflect the characteristics of commercially available Cu-zeolite catalysts, which are of widespread current interest. We demonstrate application of these models using the Powertrain System Analysis Toolkit (PSAT) software for vehicle simulations, along with a previously published methodology that accounts for emissions and temperature transients in the engine exhaust.

We summarize results from comparative simulations of hybrid electric vehicles with either stoichiometric gasoline or diesel engines. Our simulations utilize previously published models of transient engine-out emissions and models of aftertreatment devices for both stoichiometric and lean exhaust. Fuel consumption and emissions were estimated for comparable gasoline and diesel light-duty hybrid electric vehicles operating over single and multiple urban drive cycles. Comparisons between the gasoline and diesel vehicle fuel consumptions and emissions were used to identify potential advantages and technical barriers for diesel hybrids.

Three dimensional fabrics have seen increasing use lately as composite reinforcements. Advantages over prepreg or chopped fiber processes can include cost, handling, consistent quality, impact behavior, and resistance to delamination [1]. To gain acceptance in the transportation industry it is imperative that properties including dynamic and fatigue behavior be designable. A Progressive Failure Analysis (PFA) was developed jointly by Alpha Star Corp and NASA to predict fatigue life of composites and determine their damage mechanisms so that the life could be extended. The title of this software package is GENOA™, and it was used to focus on the three dimensional fabric called 3WEAVE™ made by 3TEX, Inc. It was discovered through fatigue testing that void content greatly affected fatigue life for the 3D E-glass fabric reinforcing a polyurethane modified vinyl ester resin called Dion 9800 from Reichhold. This is a common characteristic for most structural materials.

The most significant obstacle to the widespread use of carbon-fiber-based composites by the automotive industry is the high cost of carbon fibers in comparison to other potential structural materials. Carbon fibers are currently produced by thermal pyrolysis of a polyacrylonitrile (PAN) precursor to obtain the desired properties. The most significant cost factors in the process are the high cost of precursors and the high capital equipment and energy costs in conversion to carbon fiber. The Department of Energy is supporting developmental efforts to reduce costs in both precursor production and conversion areas. This paper describes developments in the conversion process. Because of the unsuccessful results of manufacturing carbon fibers through their direct heating with microwave radiation (variable frequency microwave [VFM] and single frequency microwave [SFM] energy), new avenues were explored for this processing.

The performance of large tow-size carbon fiber was evaluated to determine any design impacts that would prohibit their introduction into the fabrication process of compressed natural gas (CNG) storage tanks. The evaluation was based on manufacturing process trials and mechanical property tests. The tests consisted of impregnated strand, composite ring, and composite subscale cylinder tests for static strength, fatigue, and stress rupture. Modifications required in the wet-filament winding process are documented as well as the development of test methodologies required for testing large tow-size impregnated strands.

An operational challenge associated with SCR catalysts is the NH3 slip control, particularly for commercial small pore Cu-zeolite formulations as a consequence of their significant ammonia storage capacity. The desorption of NH3 during increasing temperature transients is one example of this challenge. Ammonia slipping from SCR catalyst typically passes through a platinum based ammonia oxidation catalyst (AMOx), leading to the formation of the undesired byproducts NOx and N2O. We have discovered a distinctive characteristic, an overlapping NH3 desorption and oxidation, in a state-of-the-art Cu-zeolite SCR catalyst that can minimize NH3 slip during temperature transients encountered in real-world operation of a vehicle.

Driver is a key component in vehicle simulation. An ideal driver model simulates driving patterns a human driver may perform to negotiate road profiles. There are simulation packages having the capability to simulate driver behavior. However, it is rarely documented how they work with road profiles. This paper proposes a new truck driver model for vehicle simulation to imitate actual driving behavior in negotiating road grade and curvature. The proposed model is developed based upon Gipps' car-following model. Road grade and curvature were not considered in the original Gipps' model although it is based directly on driver behavior and expectancy for vehicles in a stream of traffic. New parameters are introduced to capture drivers' choice of desired speeds that they intend to use in order to negotiating road grade and curvature simultaneously. With the new parameters, the proposed model can emulate behaviors like uphill preparation for different truck drivers.

Traditional autonomous vehicle perception subsystems that use onboard sensors have the drawbacks of high computational load and data duplication. Infrastructure-based sensors, which can provide high quality information without the computational burden and data duplication, are an alternative to traditional autonomous vehicle perception subsystems. However, these technologies are still in the early stages of development and have not been extensively evaluated for lane detection system performance. Therefore, there is a lack of quantitative data on their performance relative to traditional perception methods, especially during hazardous scenarios, such as lane line occlusion, sensor failure, and environmental obstructions.