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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.

On a gasoline engine platform, homogeneous charge compression ignition (HCCI) holds the promise of improved fuel economy and greatly reduced engine-out NOx emissions, without an increase in particulate matter emissions. In this investigation, a variable compression ratio (CR) engine equipped with a throttle and intake air heating was used to test the robustness of these control parameters to accommodate a series of fuels blended from reference gasoline, straight run refinery naphtha, and ethanol. Higher compression ratios allowed for operation with higher octane fuels, but operation could not be achieved with the reference gasoline, even at the highest compression ratio. Compression ratio and intake heat could be used separately or together to modulate combustion. A lambda of 2 provided optimum fuel efficiency, even though some throttling was necessary to achieve this condition. Ethanol did not appear to assist combustion, although only two ethanol-containing fuels were evaluated.

In order to meet common fuel specifications such as cetane number and volatility, a refinery must blend a number of refinery stocks derived from various process units in the refinery. Fuel chemistry can be significantly altered in meeting fuel specifications. Additionally, fuel specifications are seldom changed in isolation, and the drive to meet one specification may alter other specifications. Homogeneous charge compression ignition (HCCI) engines depend on the kinetic behavior of a fuel to achieve reliable ignition and are expected to be more dependent on fuel specifications and chemistry than today's conventional engines. Regression analysis can help in determining the underlying relationships between fuel specifications, chemistry, and engine performance. Principal Component Analysis (PCA) is used as an adjunct to regression analysis in this work, because of its ability to deal with co-linear variables and potential to uncover ‘hidden’ relationships between the variables.

We present simulated fuel economy and emissions of city transit buses powered by conventional diesel engines and diesel-hybrid electric powertrains of varying size. Six representative city drive cycles were included in the study. In addition, we included previously published aftertreatment device models for control of CO, HC, NOx, and particulate matter (PM) emissions. Our results reveal that bus hybridization can significantly enhance fuel economy by reducing engine idling time, reducing demands for accessory loads, exploiting regenerative braking, and shifting engine operation to speeds and loads with higher fuel efficiency. Increased hybridization also tends to monotonically reduce engine-out emissions, but tailpipe (post-aftertreatment) emissions are affected by complex interactions between engine load and the transient catalyst temperatures, and the emissions results were found to depend significantly on motor size and details of each drive cycle.

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.

It is well known that spark ignited engine performance and efficiency is closely coupled to fuel octane number. The present work combines historical and recent trends in spark ignition engines to build a database of engine design, performance, and fuel octane requirements over the past 80 years. The database consists of engine compression ratio, required fuel octane number, peak mean effective pressure, specific output, and combined unadjusted fuel economy for passenger vehicles and light trucks. Recent trends in engine performance, efficiency, and fuel octane number requirement were used to develop correlations of fuel octane number utilization, performance, specific output. The results show that historically, engine compression ratio and specific output have been strongly coupled to fuel octane number.

This paper summarizes initial results and findings of a model developed to determine the braking performance of commercial motor vehicles in motion regardless of brake type or gross weight. Real-world data collected by Oak Ridge National Laboratory for a U.S. Department of Energy study was used to validate the model. Expanding on previous proof-of-concept research showing the linear relationship of brake application pressure and deceleration additional parameters such as elevation were added to the model. Outputs from the model consist of coefficients calculated for every constant pressure braking event from a vehicle that can be used to calculate a deceleration and thus compute a stopping distance for a given scenario. Using brake application pressure profiles derived from the dataset, stopping distances for light and heavy loads of the same vehicle were compared for various speed and road grades.

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.

In recent years there has been an increased interest in raising the octane level of gasoline to enable higher compression ratios (CR) in spark-ignition engines to improve vehicle fuel efficiency. A number of studies have examined opportunities to increase efficiency in future vehicles, but potential impacts on the legacy fleet have not received as much attention. This effort focused on experimental studies on an engine using high-octane fuels without changing the engine’s CR. Spark timing was advanced until maximum torque was reached or knock was encountered for each engine condition, using each individual fuel to maximize engine efficiency. Knock-limited conditions occurred as the output brake mean effective pressure (BMEP) neared the maximum attainable output at a given engine speed. Increasing research octane numbers generally enabled knock-free operation under a greater number of operating conditions.

The nine CRC fuels for advanced combustion engines (FACE fuels) have been evaluated in a simple, premixed HCCI engine under varying conditions of fuel rate, air-fuel ratio, and intake temperature. Engine performance was found to vary mainly as a function of combustion phasing as affected by fuel cetane and engine control variables. The data was modeled using statistical techniques involving eigenvector representation of the fuel properties and engine control variables, to define engine response and allow optimization across the fuels for best fuel efficiency. In general, the independent manipulation of intake temperature and air-fuel ratio provided some opportunity for improving combustion efficiency of a specific fuel beyond the direct effect of targeting the optimum combustion phasing of the engine (near 5 CAD ATDC).

In prior work, the EGR loop catalytic reforming strategy developed by ORNL has been shown to provide a relative brake engine efficiency increase of more than 6% by minimizing the thermodynamic expense of the reforming processes, and in some cases achieving thermochemical recuperation (TCR), a form of waste heat recovery where waste heat is converted to usable chemical energy. In doing so, the EGR dilution limit was extended beyond 35% under stoichiometric conditions. In this investigation, a Microlith®-based metal-supported reforming catalyst (developed by Precision Combustion, Inc. (PCI)) was used to reform the parent fuel in a thermodynamically efficient manner into products rich in H2 and CO. We were able to expand the speed and load ranges relative to previous investigations: from 1,500 to 2,500 rpm, and from 2 to 14 bar break mean effective pressure (BMEP).

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.

Several standard methods exist for testing composites, metals and plastics in Mode I fracture. However, these standard test methods have limitations that disqualify them as candidates for testing certain automotive materials. In order to conduct successful fracture toughness tests with these automotive materials, a modified double cantilever beam testing geometry and associated new procedure have been developed. Both the test procedure and the data analysis have been fully documented in a draft standard. Representative SRIM composite, e-coat steel and epoxy were selected to develop and validate the testing procedure.

Automobiles of the future will be forced to travel further on a tank of fuel while discharging lower levels of pollutants. Currently, the United States uses in excess of 18 million barrels of petroleum per day. Sixty-six percent is used in the transportation of people and goods. Highway vehicles currently account for just under two-thirds of the nation's gasoline consumption, and about one-third of the total United States energy usage [1] while contributing a significant amount to the annual U.S. air pollutant burden. In 1997, 57.5% of the carbon monoxide, 29.8% of the nitrogen oxides, 27.2% of the volatile organic compounds, and 23.8% of the carbon dioxide came from highway vehicles [2] The U.S. government has supported R&D pertinent to highway vehicles since the early 1960's, to mitigate these problems.

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.

Ethanol offers significant potential for increasing the compression ratio of SI engines resulting from its high octane number and high latent heat of vaporization. A study was conducted to determine the knock-limited compression ratio of ethanol-gasoline blends to identify the potential for improved operating efficiency. To operate an SI engine in a flex fuel vehicle requires operating strategies that allow operation on a broad range of fuels from gasoline to E85. Since gasoline or low ethanol blend operation is inherently limited by knock at high loads, strategies must be identified which allow operation on these fuels with minimal fuel economy or power density tradeoffs. A single-cylinder direct-injection spark-ignited engine with fully variable hydraulic valve actuation (HVA) is operated at WOT and other high-load conditions to determine the knock-limited compression ratio (CR) of ethanol fuel blends. The geometric CR is varied by changing pistons, producing CR from 9.2 to 12.87.

Hydrogen (H2) is an excellent reductant, and has been shown to be highly effective when introduced into a variety of catalysts such as three-way catalysts, lean NOx traps (LNTs), and hydrocarbon lean NOx catalysts (also termed hydrocarbon selective catalytic reduction (SCR) catalysts). Furthermore, since lean-burn engines offer improved fuel efficiency yet difficult NOx emission control, H2 production during lean operation for the purpose of NOx reduction could be beneficial. On-board generation of hydrogen is being explored via catalytic or plasma-based reformers. A possible alternative to these add-on systems is generation of the H2 in-cylinder with standard fuel injection hardware. This paper details experiments relating to the production and measurement of H2 under net-lean operation in a common-rail diesel engine. In-cylinder fuel control is used to tailor the combustion process such that H2 is generated while maintaining a lean Air:Fuel ratio in the bulk exhaust gas.

Advanced diesel combustion regimes such as High Efficiency Clean Combustion (HECC) offer the benefits of reduced engine out NOx and particulate matter (PM) emissions. Lower PM emissions during advanced combustion reduce the demand on diesel particulate filters (DPFs) and can, thereby, reduce the fuel penalty associated with DPF regeneration. In this study, a SiC DPF was loaded and regenerated on a 1.7-liter 4-cylinder diesel engine operated in conventional and advanced combustion modes at different speed and load conditions. A diesel oxidation catalyst (DOC) and a lean NOx trap (LNT) were also installed in the exhaust stream. Five steady-state speed and load conditions were weighted to estimate Federal Test Procedure (FTP) fuel efficiency. The DPF was loaded using lean-rich cycling with frequencies that resulted in similar levels of NOx emissions downstream of the LNT.

The use of fuel reformate from catalytic processes is known to have beneficial effects on the spark-ignited (SI) combustion process through enhanced dilution tolerance and decreased combustion duration, but in many cases reformate generation can incur a significant fuel penalty. In a previous investigation, the researchers showed that, by controlling the boundary conditions of the reforming catalyst, it was possible to minimize the thermodynamic expense of the reforming process, and in some cases, realize thermochemical recuperation (TCR), a form of waste heat recovery where exhaust heat is converted to usable chemical energy. The previous work, however, focused on a relatively light-load engine operating condition of 2000 rpm, 4 bar brake mean effective pressure (BMEP). The present investigation demonstrates that this operating strategy is applicable to higher engine loads, including boosted operation up to 10 bar BMEP.

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.