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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.

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.

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.

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).

Downsized turbocharged engines have been increasingly popular in modern light-duty vehicles due to their fuel efficiency benefits. However, high power density in such engines is achieved thanks to high in-cylinder pressure and temperature conditions that increase knock propensity. Next-cycle control has been studied as a method to reduce the damaging effects of knock by operating the engine in a low knock probability condition. This exploratory study looks at the feasibility of in-cycle knock prediction as a tool for advanced knock control algorithms. A methodology is proposed to 1) choose in-cycle features of the pressure trace that highly correlate with knock events and 2) train artificial neural networks to predict in-cycle knock events before knock onset. The methodology was validated at different operating conditions and different levels of generalization. Precision and recall were used as metrics to evaluate the binary classifier.

Dilute combustion with exhaust gas recirculation (EGR) in spark-ignition engines presents a cost-effective method for achieving higher levels of engine efficiency. At high levels of EGR, however, cycle-to-cycle variability (CCV) of the combustion process is exacerbated by sporadic occurrences of misfires and partial burns. Previous studies have shown that temporal deterministic patterns emerge at such conditions and certain combustion cycles have a significant influence over future events. Due to the complexity of the combustion process and the nature of CCV, harnessing all the deterministic information for control purposes has remained challenging even with physics based 0-D, 1-D, and high-fidelity computational fluid dynamics (CFD) models. In this study, we present a data-driven approach to optimize the combustion process by controlling CCV adjusting the cycle-to-cycle fuel injection quantity.

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.

Eco-Approach and Departure (EAD) has been considered as a promising eco-driving strategy for vehicles traveling in an urban environment, where information such as signal phase and timing (SPaT) and geometric intersection description is well utilized to guide vehicles passing through intersections in the most energy-efficient manner. Previous studies formulated the optimal trajectory planning problem as finding the shortest path on a graphical model. While this method is effective in terms of energy saving, its computation efficiency can be further enhanced by adopting machine learning techniques. In this paper, we propose an innovative deep learning-based queue-aware eco-approach and departure (DLQ-EAD) system for a plug-in hybrid electric bus (PHEB), which is able to provide an online optimal trajectory for the vehicle considering both the downstream traffic condition (i.e. traffic lights, queues) and the vehicle powertrain efficiency.

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.

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.

Advances in engine controls and sensor technology are making advanced, direct, high-speed control of engine combustion more feasible. Control of combustion rate and phasing in low-temperature combustion regimes and active control of cyclic variability in dilute SI combustion are being pursued in laboratory environments with high-quality data acquisition systems, using metrics calculated from in-cylinder pressure. In order to implement these advanced combustion controls in production, lower-quality data will need to be tolerated even if indicated pressure sensors become available. This paper examines the effects of several data quality issues, including phase shifting (incorrect TDC location), reduced data resolution, pressure pegging errors, and random noise on calculated combustion metrics that are used for control feedback.

The CRC Fuels for Advanced Combustion Engines (FACE) Working Group has provided a matrix of experimental diesel fuels for use in studies on the effects of three parameters, Cetane number (CN), aromatics content, and 90 vol% distillation temperature (T90), on combustion and emissions characteristics of advanced combustion strategies. Various types of fuel analyses and engine experiments were performed in well-known research institutes. This paper reviews a collection of research findings obtained with these nine fuels. An extensive collection of analyses were performed by members of the FACE working group on the FACE diesel fuels as a means of aiding in understanding the linkage between fuel properties and combustion and emissions performance. These analyses included non-traditional chemical techniques as well as established ASTM tests. In a few cases, both ASTM tests and advanced analyses agreed that some design variables differed from their target values when the fuels were produced.

This paper introduces a promising approach for developing an integrated traction motor drive based on the Integrated Modular Motor Drive (IMMD) concept. The IMMD concept strives to meet aggressive power density and performance targets by modularizing both the machine and power electronics and then integrating them into a single combined machine-plus-drive structure. Physical integration of the power electronics inside the machine makes it highly desirable to increase the power electronics operating temperature including higher power semiconductor junction temperatures and improved device packaging. Recent progress towards implementing the IMMD concept in an integrated traction motor drive is summarized in this paper. Several candidate permanent magnet (PM) machine configurations with different numbers of phases between 3 and 6 are analyzed to compare their performance characteristics and key application features.

Diesel particulate samples were collected from a light duty engine operated at a single speed-load point with a range of biodiesel and conventional fuel blends. The oxidation reactivity of the samples was characterized in a laboratory reactor, and BET surface area measurements were made at several points during oxidation of the fixed carbon component of both types of particulate. The fixed carbon component of biodiesel particulate has a significantly higher surface area for the initial stages of oxidation, but the surface areas for the two particulates become similar as fixed carbon oxidation proceeds beyond 40%. When fixed carbon oxidation rates are normalized to total surface area, it is possible to describe the oxidation rates of the fixed carbon portion of both types of particulates with a single set of Arrhenius parameters. The measured surface area evolution during particle oxidation was found to be inconsistent with shrinking sphere oxidation.

This paper presents on-going finite element modeling efforts of friction stir spot welding (FSSW) process using Abaqus/Explicit as a finite element solver. Three-dimensional coupled thermal-stress model was used to calculate thermo-mechanical response of FSSW process. Adaptive meshing and advection schemes, which makes it possible to maintain mesh quality under large deformations, is utilized to simulate the material flow and temperature distribution in FSSW process. The predicted overall deformation shape of the weld joint resembles that experimentally observed. Temperature and stress graphs in the radial direction as well as temperature-deformation distribution plots are presented.

Forty-nine plant-wide energy efficiency assessments have been undertaken under sponsorship of the U.S. Department of Energy's Industrial Technologies Program. Plant-wide assessments are comprehensive, systematic investigations of plant energy efficiency, including plant utility systems and process operations. Assessments in industrial facilities have highlighted opportunities for implementing best practices in industrial energy management, including the adoption of new, energy-efficient technologies and process and equipment improvements. Total annual savings opportunities of $201 million have been identified from the 40 completed assessments. Many of the participating industrial plants have implemented efficiency-improvement projects and already have realized total cost savings of more than $81 million annually. This paper provides an overview of the assessment efforts undertaken and presents a summary of the major energy and cost savings identified to date.

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 objective of the Heavy Vehicle Propulsion Materials Program is to develop the enabling materials technology for the clean, high-efficiency diesel truck engines of the future. The development of cleaner, higher-efficiency diesel engines imposes greater mechanical, thermal, and tribological demands on materials of construction. Often the enabling technology for a new engine component is the material from which the part can be made. The Heavy Vehicle Propulsion Materials Program is a partnership between the Department of Energy (DOE), and the diesel engine companies in the United States, materials suppliers, national laboratories, and universities. A comprehensive research and development program has been developed to meet the enabling materials requirements for the diesel engines of the future.

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.