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Highway safety remains a significant concern, especially in mixed traffic scenarios involving heavy-duty vehicles (HDV) and smaller passenger cars. The vulnerability of HDVs following closely behind smaller cars is evident in incidents involving the lead vehicle, potentially leading to catastrophic rear-end collisions. This paper explores how automatic speed enforcement systems, using speed cameras, can mitigate risks for HDVs in such critical situations. While historical crash data consistently demonstrates the reduction of accidents near speed cameras, this paper goes beyond the conventional notion of crash occurrence reduction. Instead, it investigates the profound impact of driver behavior changes within desired travel speed distribution, especially around speed cameras, and their contribution to the safety of trailing vehicles, with a specific focus on heavy-duty trucks in accident-prone scenarios.

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.

China's plug-in electric vehicle (PEV) market with stocks at 7.8 million is the world's largest in 2021, and it accounts for half of the global PEV growth in 2021. The PEV market in China has dramatically evolved since the pandemic in 2020: over 20% of all new PEV sales are from China by mid-2022. Recent features of PEV market dynamics, consumer acceptance, policies, and infrastructure have important implications for both the global energy market and manufacturing stakeholders. From the perspective of demand pull-supply push, this study analyzes China's PEV industry with a market dynamics framework by reviewing sales, product and brand, infrastructure, and government policies from the last few years and outlooking the development of the new government’s 14th Five-Year Plan (2021-2025).

The US Department of Energy’s Co-Optimization of Engine and Fuels Initiative (Co-Optima) investigated how unique properties of bio-blendstocks considered within Co-Optima help address emissions challenges with mixing controlled compression ignition (i.e., conventional diesel combustion) and enable advanced compression ignition modes suitable for implementation in a diesel engine. Additionally, the potential synergies of these Co-Optima technologies in hybrid vehicle applications in the medium- and heavy-duty sector was also investigated. In this work, vehicles system were simulated using the Autonomie software tool for quantifying the benefits of Co-Optima engine technologies for medium-duty trucks. A Class 6 delivery truck with a 6.7 L diesel engine was used for simulations over representative real-world and certification drive cycles with four different powertrains to investigate fuel economy, criteria emissions, and performance.

G-Equation models represent propagating flame fronts with an implicit two-dimensional surface representation (level-set). Level-set methods are fast, as transport source terms for the implicit surface can be solved with finite-volume operators on the finite-volume domain, without having to build the actual surface. However, they include approximations whose practical effects are not properly understood. In this study, we improved the numerics of the FRESCO CFD code’s G-Equation solver and developed a new method to simulate kernel growth using signed distance functions and the analytical sphere-mesh overlap. We analyzed their role for simulating propane/air flames, using three well-established constant-volume configurations: a one-dimensional, freely propagating laminar flame; a disc-shaped, constant-volume swirl combustor; and torch-jet flame development through an orifice from a two-chamber device.

In this study the volume and hardness were measured for thermoplastics and thermosetting resins with diesel containing up to 30% of the following blend stocks: biodiesel, renewable diesel, n-undecane, dibutoxymethane, 1-octanol, hexyl hexanoate, and 2-nonanone. Thermoplastics included polyphenylene sulfide (PPS), polyethylene terephthalate (PET), polytetrafluoroethylene (PTFE), polyvinylidene fluoride (PVDF), polyoxymethylene (POM), polybutylene terephthalate (PBT), polypropylene (PP), high density polyethylene (HDPE), nylons, acetals, polyetherimide (PEI), polyetheretherketone (PEEK), a PET co-polymer, polyphthalamides (PPAs), polyarylamide (PARA) and ethylene tetrafluoroethylene (ETFE). Three thermosetting resins were also evaluated. The material specimens were exposed to the test fuels under ambient conditions for 16 weeks.

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.

Metal additive manufacturing has high potential to produce automobile parts, due to its shape flexibility and unique material properties. On the other hand, residual stress which is generated by rapid solidification causes deformation, cracks and failure under building process. To avoid these problems, understanding of internal residual stress distribution is necessary. However, from the view point of measureable area, conventional residual stress measurement methods such as strain gages and X-ray diffractometers, is limited to only the surface layer of the parts. Therefore, neutron which has a high penetration capability was chosen as a probe to measure internal residual stress in this research. By using time of flight neutron diffraction facility VULCAN at Oak Ridge National Laboratory, residual stress for mono-cylinder head, which were made of aluminum alloy, was measured non-distractively. From the result of precise measurement, interior stress distribution was visualized.

Polyoxymethylene dimethyl ethers (PODEs) have shown promise as candidates for diesel fuel blendstocks due to their low sooting tendency, high cetane number, and diesel-comparable boiling point range. However, there is a lack of literature regarding compatibility of PODEs with common automotive elastomers, which would be a prerequisite to their adoption into the marketplace. To address this need, an exposure study and complementary solubility analysis were undertaken. A commercially available blend of PODEs with polymerization degree ranging from 3 to 6 was blended with diesel certification fuel at 0, 33, 50, 67, at 100% by mass. Elastomer coupons were exposed to the various blends for a period of 4 weeks and evaluated for volume swell.

Measuring axial exhaust species concentration distributions within a wall-flow aftertreatment device provides unique and significant insights regarding the performance of complex devices like the SCR-on-filter. In this particular study, a less complex aftertreatment configuration which includes a DOC followed by two uncoated partial flow filters (PFF) was used to demonstrate the potential and challenges. The PFF design in this study was a particulate filter with alternating open and plugged channels. A SpaciMS [1] instrument was used to measure the axial NO2 profiles within adjacent open and plugged channels of each filter element during an extended passive regeneration event using a full-scale engine and catalyst system. By estimating the mass flow through the open and plugged channels, the axial soot load profile history could be assessed.

The compatibility of key fuel system infrastructure elastomers with promising bio-blendstock fuel candidates was examined using Hansen solubility analysis. Thirty-four candidate fuels were evaluated in this study including multiple alcohols, esters, ethers, ketones, alkenes and one alkane. These compounds were evaluated as neat molecules and as blends with the gasoline surrogate, dodecane and a mix of dodecane and 10% ethanol (E10D). The elastomer materials were fluorocarbon, acrylonitrile butadiene rubber (NBR), styrene butadiene (SBR), neoprene, polyurethane and silicone. These materials have been rigorously studied with other fuel types, and their measured volume change results were found to correspond well with their predicted solubility levels. The alcohols showed probable compatibility with fluorocarbon and polyurethane, but are not likely to be compatible at low blend levels with NBR and SBR.

Modeling and simulation of batteries, in conjunction with theory and experiment, are important research tools that offer opportunities for advancement of technologies that are critical to electric motors. The development of data from the application of these tools can provide the basis for managerial and technical decision-making. Together, these will continue to transform batteries for electric vehicles.

The compatibility of notable infrastructure elastomers and plastics with DME and its blends with diesel fuel were examined using solubility analysis. The elastomer materials were fluorocarbon, acrylonitrile butadiene rubber (NBR), styrene butadiene (SBR), neoprene, polyurethane and silicone. Plastic materials included polyphenylene sulfide (PPS), polyethylene terephthalate (PET), polytetrafluoroethylene (PTFE), polyvinylidene fluoride (PVDF), polyoxymethylene (POM), polybutylene terephthalate (PBT), polypropylene (PP), high density polyethylene (HDPE), along with several nylon grades and thermosetting resins. These materials have been rigorously studied with other fuel types, and their volume change results were found to correspond well with their predicted solubility levels. A Hansen solubility analysis was performed for each material with DME, diesel, and blends of both fuel components.

The compatibility of elastomer materials used in fuel storage and dispensing applications was determined for an off-highway diesel fuel and a blend containing 20% bio-oil (Bio20) derived from a fast pyrolysis process. (This fuel blend is not to be confused with B20, which is a blend of diesel fuel with 20% biodiesel.) The elastomer types evaluated in this study included fluorocarbon, fluorosilicone, acrylonitrile rubber (NBR), styrene butadiene rubber (SBR), polyurethane, neoprene, and silicone. All of these elastomer types are used in sealing applications, but some, like the nitrile rubbers are also common hose materials. The elastomer specimens were exposed to the two fuel types for 4 weeks at 60°C. After measuring the wetted volume and hardness, the specimens were dried for 65 hours at 60°C and then remeasured. A solubility analysis was performed to better understand the performance of plastic materials in fuel blends composed of bio-oil and diesel.

The compatibility of plastic materials used in fuel storage and dispensing applications was determined for an off-highway diesel fuel and a blend containing 20% bio-oil (Bio20) derived from a fast pyrolysis process. Bio20 is not to be confused with B20, which is a diesel blend containing 20% biodiesel. The feedstock, processing, and chemistry of biodiesel are markedly different from bio-oil. Plastic materials included those identified for use as seals, coatings, piping and fiberglass resins, but many are also used in vehicle fueling systems. The plastic specimens were exposed to the two fuel types for 16 weeks at 60°C. After measuring the wetted volume and hardness, the specimens were dried for 65 hours at 60°C and then remeasured to determine extent of property change. A solubility analysis was performed to better understand the performance of plastic materials in fuel blends composed of bio-oil and diesel.

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.

Advanced lightweight materials are increasingly being incorporated into new vehicle designs by automakers to enhance performance and assist in complying with increasing requirements of corporate average fuel economy standards. To assess the primary energy and carbon dioxide equivalent (CO2e) implications of vehicle designs utilizing these materials, this study examines the potential life cycle impacts of two lightweight material alternative vehicle designs, i.e., steel and aluminum of a typical passenger vehicle operated today in North America. LCA for three common alternative lightweight vehicle designs are evaluated: current production (“Baseline”), an advanced high strength steel and aluminum design (“LWSV”), and an aluminum-intensive design (AIV). This study focuses on body-in-white and closures since these are the largest automotive systems by weight accounting for approximately 40% of total curb weight of a typical passenger vehicle.

The compatibility of plastic materials used in gasoline storage and dispensing applications was determined for test fuels representing neat gasoline (Fuel C), and blends containing 25% ethanol (CE25a), 16% isobutanol (CiBu16a), and 24% isobutanol (CiBu24a). A solubility analysis was also performed and compared to the volume swell results obtained from the test fuel exposures. The plastic specimens were exposed to each test fuel for16 weeks at 60°C. After measuring the wetted volume and hardness, the specimens were dried for 65 hours at 60°C and then remeasured for volume and hardness. Dynamic mechanical analysis (DMA), which measures the storage modulus as a function of temperature, was also performed on the dried specimens to determine the temperature associated with the onset of the glass-to-rubber transition (Tg). For many of the plastic materials, the solubility analysis was able to predict the relative volume swell for each test fuel.

The compatibility of elastomeric materials used in fuel storage and dispensing applications was determined for test fuels representing neat gasoline and gasoline blends containing 10 and 17 vol.% ethanol, and 16 and 24 vol.% isobutanol. The actual test fuel chemistries were based on the aggressive formulations described in SAE J1681 for oxygenated gasoline. Elastomer specimens of fluorocarbon, fluorosilicone, acrylonitrile rubber (NBR), polyurethane, neoprene, styrene butadiene rubber (SBR) and silicone were exposed to the test fuels for 4 weeks at 60°C. After measuring the wetted volume and hardness, the specimens were dried for 20 hours at 60°C and then remeasured for volume and hardness. Dynamic mechanical analysis (DMA) was also performed to determine the glass transition temperature (Tg). Comparison to the original values showed that all elastomer materials experienced volume expansion and softening when wetted by the test fuels.

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.