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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.

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.

Internal combustion engines are becoming ever more efficient as mankind seeks to mitigate the effects of climate change while still maintaining the benefits that a mechanized society has brought to the global economy. As peak values, mass production spark-ignition engines can now achieve approximately 40% brake thermal efficiency and heavy-duty truck compression-ignition engines can approach 50%. While commendable, the unfortunate truth is that the remainder gets emitted as waste heat and is sent to the atmosphere to no useful purpose. Clearly, if one could recover some of this waste heat for beneficial use then this is likely to become important as new means of mitigating fossil CO2 emissions are demanded. A previous study by the authors has identified that the closed Joule cycle (or complications of it beginning to approximate the closed Ericsson cycle) could reasonably be developed to provide a practical means of recovering exhaust heat when applied to a large ship engine.

Hydrogen is anticipated to play a pivotal role as a green energy carrier in both heavy industry and transportation. Utilizing hydrogen directly in internal combustion engines (ICE) could offer several advantages compared to alternative technologies. To achieve this objective, a proper understanding of the physical mechanisms and dynamics involved in the injection of this fuel is needed. This study applied high-fidelity computational fluid dynamics (CFD) simulations to describe the flow characteristics of hydrogen injection using hollow- and single- and multi-solid-cone injectors and their effect on mixing quality and characteristics in a constant volume quiescent environment. A reference hollow-cone configuration was used to validate the model. The results indicate that solid-cone configurations achieve greater penetration due to the flow patterns they generate. However, an increase in the number of holes leads to reduced penetration length, projected area, and induced turbulence.

The urgent need to combat global warming has spurred legislative efforts within the transport sector to transition away from fossil fuels. Hydrogen is increasingly being utilised as a green energy vector, which can aid the decarbonisation of transport, including internal combustion engines. Computational fluid dynamics (CFD) is widely used as a tool to study and optimise combustion systems especially in combination with new fuels like hydrogen. Since the behaviour of the injection event significantly impacts combustion and emissions formation especially in direct injection applications, the accurate modelling of H2 injection is imperative for effective design of hydrogen combustion systems. This work aims to evaluate unsteady Reynolds-Averaged Navier Stokes (URANS) modelling of the advective transport process and related numerical methods.

Reducing criteria pollutants while reducing greenhouse gases is an active area of research for commercial on-road vehicles as well as for off-road machines. The heavy duty on-road sector has moved to reducing NOx by 82.5% compared to 2010 regulations while increasing the engine useful life from 435,000 to 650,000 miles by 2027 in the United States (US). An additional certification cycle, the Low Load Cycle (LLC), has been added focusing on part load operation having tight NOx emissions levels. In addition to NOx, the total CO2 emissions from the vehicle will also be reduced for various model years. The off-road market is following with a 90% NOx reduction target compared to Tier 4 Final for 130-560 kW engines along with greenhouse gas targets that are still being established. The off-road market will also need to certify with a Low Load Application Cycle (LLAC), a version of which was proposed for evaluation in 2021.

1Increasing adoption of downsized, boosted, spark-ignition engines has improved vehicle fuel economy, and continued improvement is desirable to reduce carbon emissions in the near-term. However, this strategy is limited by damaging preignition events which can cause hardware failure. Research to date has shed light on various contributing factors related to fuel and lubricant properties as well as calibration strategies, but the causal factors behind an individual preignition cycle remain elusive. If actionable precursors could be identified, mitigation through active control strategies would be possible. This paper uses artificial neural networks to search for identifiable precursors in the cylinder pressure data from a large real-world data set containing many preignition cycles. It is found that while follow-up preignition cycles in clusters can be readily predicted, the initial preignition cycle is not predictable based on features of the cylinder pressure.

This work for the Coordinating Research Council (CRC) explores dependencies on the opportunity for fuel to impinge on internal engine surfaces (i.e., fuel–wall impingement) as a function of fuel properties and engine operating conditions and correlates these data with measurements of stochastic preignition (SPI) propensity. SPI rates are directly coupled with laser–induced florescence measurements of dye-doped fuel dilution measurements of the engine lubricant, which provides a surrogate for fuel–wall impingement. Literature suggests that SPI may have several dependencies, one being fuel–wall impingement. However, it remains unknown if fuel-wall impingement is a fundamental predictor and source of SPI or is simply a causational factor of SPI. In this study, these relationships on SPI and fuel-wall impingement are explored using 4 fuels at 8 operating conditions per fuel, for 32 total test points.

Ammonia as a fuel suffers from a high ignition energy requirement making it hard to ignite in stoichiometric mixtures, especially with normal spark plugs. On the other hand, pre-chambers are proven to provide high ignition energy by producing multiple ignition spots in the main chamber. A pre-chamber is usually categorized as “active” if it has a dedicated fueling system, and as “passive” if it depends solely on the air- fuel mixture being introduced from the main chamber and is therefore simpler than the active type. In this study, an SI light-duty engine was tested with a conventional spark plug with fuel mixtures of gasoline and gaseous ammonia (0%, 25%, 50%, 75%, 90%, and 100% NH3). The test was then repeated with a passive pre-chamber under the same operating conditions for comparison. Moreover, the engine exhaust was fitted with a fast response analyzer to measure NOX. The use of the conventional spark plug showed stable combustion throughout the fuel mixture sweep.

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 recently concluded partnership for advancing combustion engines (PACE) was a US Department of Energy consortium involving multiple national laboratories focused on addressing key efficiency and emission barriers in light-duty engines. Generation of detailed experimental data and modeling capabilities to understand and predict cold-start behavior was a major pillar in this program. Cold-start, as defined by the time between first engine crank and three-way catalyst light-off, is responsible for a large percentage of NOx, unburned hydrocarbon, and particulate matter emissions in light-duty engines. Minimizing emissions during cold-start is a trade-off between achieving faster three-way catalyst light-off, and engine out emissions during that period. In this study, engine performance, emissions, and catalyst warmup potential were monitored while the engine was operated using a single direct injection (baseline case) as well as a two-way-equal-split direct injection strategy.

With increasingly stringent regulations mandating the improvement of vehicle fuel economy, automotive manufacturers face growing pressure to develop and implement technologies that improve overall system efficiency. One such technology is an automatic (auto) stop-start feature. Auto stop-start reduces idle time and reduces fuel use by temporarily shutting the engine off when the vehicle comes to a stop and automatically re-starting it when the brake is released, or the accelerator is pressed. As mandated by the U.S. Congress, the U.S. Environmental Protection Agency (EPA) is required to keep the public informed about fuel saving practices. This is done, in partnership with the U.S. Department of Energy (DOE), through the fueleconomy.gov website. The “Fuel-Saving Technologies” and “Gas Mileage Tips” sections of the website are focused on helping the public make informed purchasing decisions and encouraging fuel-saving driving habits.

Gasoline knock resistance is characterized by the Research and Motor Octane Number (RON and MON), which are rated on the CFR octane rating engine at naturally aspirated conditions. However, modern automotive downsized boosted spark ignition (SI) engines generally operate at higher cylinder pressures and lower temperatures relative to the RON and MON tests. Using the naturally aspirated RON and MON ratings, the octane index (OI) characterizes the knock resistance of gasolines under boosted operation by linearly extrapolating into boosted “beyond RON” conditions via RON, MON, and a linear regression K factor. Using OI solely based on naturally aspirated RON and MON tests to extrapolate into boosted conditions can lead to significant errors in predicting boosted knock resistance between gasolines due to non-linear changes in autoignition and knocking characteristics with increasing pressure conditions.

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.

With ever stricter legislative requirements for CO2 and other exhaust emissions, significant efforts by OEMs have launched a number of different technological strategies to meet these challenges such as Battery Electric Vehicles (BEVs). However, a multiple technology approach is needed to deliver a broad portfolio of products as battery costs and supply constraints are considerable concerns hindering mass uptake of BEVs. Therefore, further investment in Internal Combustion (IC) engine technologies to meet these targets are being considered, such as lean burn gasoline technologies alongside other high efficiency concepts such as dedicated hybrid engines. Hence, it becomes of sound reason to further embrace diversity and develop complementary technologies to assist in the transition to the next generation hybrid powertrain. One such approach is to provide increased valvetrain flexibility to afford new degrees of freedom in engine operating strategies.

This paper reports on an analytical study of the heat transfer and fluid flow in an electric vehicle e-Motor cooled by twenty five sprays/jets of oil. A three-dimensional, quasi-steady state, multi-phase, computational fluid dynamics (CFD) and conjugate heat transfer (CHT) model was created using a commercial CFD software. The transport equations of mass, momentum, energy and volume fraction were solved together with models for turbulence and wall treatment. An explicit formulation of the volume of fluid (VOF) technique was used to simulate the sprays, a time-implicit formulation was used for the flow-field and three dimensional conduction heat transfer with non-isotropic thermal conductivities was used to simulate the heat transfer in the windings.

Compared to conventional diesel combustion (CDC), isobaric combustion can achieve higher thermal efficiency while lowering heat transfer losses and nitrogen oxides (NOx). However, isobaric combustion suffers from higher soot emissions. While the aforementioned trends are well established, there is limited literature about the high-temperature reaction zones, the liquid-phase penetration distance, and the flame tip propagation velocity of isobaric combustion. In the present study, the line-of-sight integrated imaging of Mie-scattering, combustion luminosity, and CH* chemiluminescence were conducted in an optically accessible single-cylinder heavy-duty diesel engine. The engine was equipped with a flat-bowl-shaped optical piston to allow bottom-view imaging of the combustion chamber. The experiments were conducted using n-heptane fuel for CDC and isobaric combustion modes.

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.

Investigating combustion characteristics of oxygenated gasoline and gasoline blended ethanol is a subject of recent interest. The non-linearity in the interaction of fuel components in the oxygenated gasoline can be studied by developing chemical kinetics of relevant surrogate of fewer components. This work proposes a new reduced four-component (isooctane, heptane, toluene, and ethanol) oxygenated gasoline surrogate mechanism consisting of 67 species and 325 reactions, applicable for dynamic CFD applications in engine combustion and sprays. The model introduces the addition of eight C1-C3 species into the previous model (Li et al; 2019) followed by extensive tuning of reaction rate constants of C7 - C8 chemistry. The current mechanism delivers excellent prediction capabilities in comprehensive combustion applications with an improved performance in lean conditions.