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Mechanisms of NH₃ generation using LNT-like catalysts have been studied in a bench reactor over a wide range of temperatures, flow rates, reformer catalyst types and synthetic exhaust-gas compositions. The experiments showed that the on board production of sufficient quantities of ammonia on board for SCR operation appeared feasible, and the results identified the range of conditions for the efficient generation of ammonia. In addition, the effects of reformer catalysts using the water-gas-shift reaction as an in-situ source of the required hydrogen for the reactions are also illustrated. Computations of the NH₃ and NOx kinetics have also been carried out and are presented. Design and impregnation of the SCR catalyst in proximity to the ammonia source is the next logical step. A heated synthetic-exhaust gas flow bench was used for the experiments under carefully controlled simulated exhaust compositions.

An optical-based sensor for detecting particulate matter (PM) in diesel engine exhaust has been demonstrated. The position of the sensor during the experiments was the exhaust manifold prior to the turbocharger. The sensor is constructed of fiber optics which transmit 532-nm laser light into the exhaust pipe and collect backscattered light in a 180° geometry. Due to the optical nature of the probe, PM sensing can occur at high temporal rates. Experiments conducted by changing the fuel injection properties of one cylinder of a four cylinder engine demonstrated that the sensor can resolve cycle dependent events. The feasibility of the probe for examining PM emissions in the exhaust manifold will be discussed.

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.

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.

A critical evaluation of the current ASTM method of rating diesel fuels, and of the available non-engine techniques for the estimation of cetane nunbers of diesel fuels is presented. The relationship between ignition quality and fuel composition is reviewed and it is shown that each member of an homologous series of hydrocarbons does not have the same ignition characteristics as the other members of the series. It is emphasized that the belief that paraffins have relatively high cetane ratings as compared to aromatics and cycloparaffins is not always correct. The basic flaw in the cetane index correlations, which use the easily measurable physical properties of the fuels as independent parameters, is explained. A fuel data base has been used to compare the different correlations.

Accurate perception of the driving environment and a highly accurate position of the vehicle are paramount to safe Autonomous Vehicle (AV) operation. AVs gather data about the environment using various sensors. For a robust perception and localization system, incoming data from multiple sensors is usually fused together using advanced computational algorithms, which historically requires a high-compute load. To reduce AV compute load and its negative effects on vehicle energy efficiency, we propose a new infrastructure information source (IIS) to provide environmental data to the AV. The new energy–efficient IIS, chip–enabled raised pavement markers are mounted along road lane lines and are able to communicate a unique identifier and their global navigation satellite system position to the AV. This new IIS is incorporated into an energy efficient sensor fusion strategy that combines its information with that from traditional sensor.

The U.S. Government plays an important role in the Partnership for a New Generation of Vehicles' (PNGV) electrical and electronics technologies with a program consisting of high-risk research and development (R&D) projects. The Department of Energy (DOE) plays the largest role in supporting these technologies to specifically address automotive needs. DOE has three Automotive Integrated Power Module (AIPM) contractors and two Automotive Electric Motor Drive (AEMD) contractors working to become viable suppliers for PNGV. Materials development projects are working to improve materials and devices needed in automotive motors and drives, such as permanent magnets, capacitors, sensors, connectors, and thermal management materials. Advancements in inverters, controls, and motors and generators conducted at DOE's national laboratories are also presented.

The Enhanced/Synthetic Vision System (E/SVS) is a Technology Demonstrator (TD) project supported by the Chief, Research and Development of the Canadian Department of National Defence. E/SVS displays an augmented visual scene to the pilot that includes three separate image sources: a synthetic computer - generated terrain image; an enhanced visual image from an electro-optical sensor (fused as an inset); and aircraft instrument symbology, all displayed to the pilot on a Helmet Mounted Display (HMD). The synthetic component of the system provides a 40 degree vertical by 80 degree horizontal image of terrain and local features. The enhanced component digitizes imagery from electro-optic sensors and fuses the sensor image as an inset (20 degrees by 25 degrees) within the synthetic image. Symbology can be overlaid in any location within the synthetic field-of-view and may be head, aircraft, target or terrain referenced.

Reliable means for on-board detection of particulate filter failures or malfunctions are needed to meet diagnostics (OBD) requirements. Detecting these failures, which result in tailpipe particulate matter (PM) emissions exceeding the OBD limit, over all operating conditions is challenging. Current approaches employ differential pressure sensors and downstream PM sensors, in combination with particulate filter and engine-out soot models. These conventional monitors typically operate over narrowly-defined time windows and do not provide a direct measure of the filter’s state of health. In contrast, radio frequency (RF) sensors, which transmit a wireless signal through the filter substrate provide a direct means for interrogating the condition of the filter itself.

With the recent advancement in sensing and controller technologies architecture design of an autonomous driving system becomes an important issue. Researchers have been developing different sensors and data processing technologies to solve the issues associated with fast processing, diverse weather, reliability, long distance recognition performance, etc. Necessary considerations of diverse traffic situations and safety factors of autonomous driving have also increased the complexity of embedded software as well as architecture of autonomous driving. In these circumstances, there are almost countless numbers of possible architecture designs. However, these design considerations have significant impacts on cost, controllability, and system reliability. Thus, it is crucial for the designers to make a challenging and critical design decision under several uncertainties during the conceptual design phase.

This paper demonstrates the potential for fast absorption spectroscopy measurements in diesel-engine exhaust to track H2O concentration transients. Wavelength-agile absorption spectroscopy is an optical technique that measures broadband absorption spectra between 10 kHz and 100 MHz. From these measured spectra, gas temperature and absorber concentrations can be determined. We introduce a laser spectroscopy system for high speed (10 μs) diesel exhaust measurements in the presence of significant particulate matter. The sensor can be broadly applicable for understanding the dynamics of EGR distribution, cycle-to-cycle stability, cylinder-to-cylinder charge uniformity, and NOX regeneration strategies. The method precision is 10% and 5%, for individual 10-μs measurements and 2-ms averaged measurements, respectively. The sensitivity or detection limit of the measurement is 0.5% H2O.

We report on a feasibility study of an optical micro-electro-mechanical systems (MEMS) flow sensor to measure flow rate using Moiré fringe displacement of a floating element. Due to constraints on weight, power, and size for space environmental systems, the development of sensor components that minimize the equivalent systems mass (ESM) while maintaining or exceeding required specifications is highly desirable. A feature of the optical detection method is a physical separation of electrical components from the flow stream. The geometric Moiré fringe shift optically amplifies small displacements by the ratio of the fringe pitch to the movable grating pitch that is detected using an external CCD imager, providing an electrically isolated, robust, direct scheme for detecting flow from shear stress induced displacement.

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.

Lane detection plays a critical role in autonomous vehicles for safe and reliable navigation. Lane detection is traditionally accomplished using a camera sensor and computer vision processing. The downside of this traditional technique is that it can be computationally intensive when high quality images at a fast frame rate are used and has reliability issues from occlusion such as, glare, shadows, active road construction, and more. This study addresses these issues by exploring alternative methods for lane detection in specific scenarios caused from road construction-induced lane shift and sun glare. Specifically, a U-Net, a convolutional network used for image segmentation, camera-based lane detection method is compared with a radar-based approach using a new type of sensor previously unused in the autonomous vehicle space: radar retro-reflectors.