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Potentials for Platooning in U.S. Highway Freight Transport
Smart technologies enabling connection among vehicles and between vehicles and infrastructure as well as vehicle automation to assist human operators are receiving significant attention as a means for improving road transportation systems by reducing fuel consumption - and related emissions - while also providing additional benefits through improving overall traffic safety and efficiency. For truck applications, which are currently responsible for nearly three-quarters of the total U.S. freight energy use and greenhouse gas (GHG) emissions, platooning has been identified as an early feature for connected and automated vehicles (CAVs) that could provide significant fuel savings and improved traffic safety and efficiency without radical design or technology changes compared to existing vehicles. A statistical analysis was performed based on a large collection of real-world U.S. truck usage data to estimate the fraction of total miles that are technically suitable for platooning. In particular, our analysis focuses on estimating 'platoonable' mileage based on overall highway vehicle use and prolonged high-velocity traveling, and established that about 65% of the total miles driven by combination trucks from this data sample could be driven in platoon formation, leading to a 4% reduction in total truck fuel consumption. This technical potential for 'platoonable' miles in the United States provides an upper bound for scenario analysis considering fleet willingness and convenience to platoon as an estimate of overall benefits of early adoption of connected and automated vehicle technologies. A benefit analysis is proposed to assess the overall potential for energy savings and emissions mitigation by widespread implementation of highway platooning for trucks.
Washington State Highway Seismic Screening Tool (HSST)
The U.S. Department of Homeland Security’s Regional Resiliency Assessment Program (RRAP) undertook the Washington State Transportation Systems RRAP project in 2017 to assess the seismic vulnerabilities of that state’s transportation system to a Cascadia Subduction Zone (CSZ) earthquake. The specific goal of that project was to assess the vulnerability of, and prioritize, transportation infrastructure for moving goods and supplies from Incident Support Bases to Federal Staging Areas within Washington State following a CSZ earthquake. As part of this analysis, Argonne National Laboratory (Argonne), with input and support from the Washington State Department of Transportation (WSDOT) maintenance and pavements offices and state geotechnical engineer, developed the Highway Seismic Screening Tool (HSST) to inform a system-level analysis of highway recovery and reopening times. This report provides a full discussion of the HSST’s development, implementation, and results for the CSZ scenario earthquake. The primary outcome of the HSST is to determine the approximate reopening times of highway segments or bridges damaged by a CSZ magnitude 9.0 seismic event. This is done by calculating the permanent ground deformation (PGD) caused by soil liquefaction along state highways, which can cause significant damage or displacement of highway pavement structures. The term “reopening time” is different from “restoration time” and is intended to represent the approximate amount of time that would be needed to return highways to a minimum state of repair sufficient to support the limited movement of emergency and response vehicles. Additionally, the HSST is referred to as a “screening tool,” as it uses network- or system-level asset management information that WSDOT provided to assess seismic-related hazards and vulnerabilities of highways; it does not conduct an asset-level engineering analysis of seismic-related vulnerabilities. Accordingly, the results of the HSST analysis should only be used for system- or corridor-level planning and investment, and not for engineering or management decisions at the asset level. The HSST is also applied to assess the exposure of statewide rail systems to soil liquefaction and PGD hazards. However, little information is available about the sensitivity or vulnerability of railways and rail yards to such hazards, and therefore researchers were unable to determine approximate reopening times for the state’s rail system following a CSZ earthquake.
Investigating Bacterial-Fungal Interactions using Fungal Highway Columns in Diverse Environments and Substrates
Bacterial-fungal interactions (BFIs) play an integral role in shaping microbial community composition, biogeochemical functions, spatial dynamics, and microbial dispersal. Mycelial networks created by filamentous fungi or other filamentous microorganisms (e.g., Oomycetes) act as 'fungal highways' that can be utilized by bacteria for transport throughout heterogeneous environments, greatly facilitating their mobility and granting them access to regions that may be challenging or impossible to reach on their own (e.g., due to air pockets within the soil). Several devices and experimental protocols have been created to study these fungal highways, including fungal highway columns. The fungal highway column designed by our group can be used for a variety of in situ or in vitro applications, as well as with diverse environmental and host-associated sample types. Herein, we describe the methods for performing experiments with these columns, including designing, printing, sterilizing, and preparing the devices. The options for analyzing data obtained from the use of these devices are also discussed here, and troubleshooting advice regarding potential pitfalls associated with experiments using fungal highway columns is offered. These devices can be used to gain a more comprehensive understanding of the diversity, mechanisms, and dynamics of fungal highway BFIs to provide valuable insights into the structural and functional dynamics within complex environments (e.g., soils) and across diverse habitats in which bacteria and fungi co-exist.
Washington State Highway Bridge Seismic Screening Tool (BSST) (Technical Report)
The Regional Resiliency Assessment Program (RRAP) is a cooperative assessment of specific critical infrastructure within a designated geographic area and a regional analysis of the surrounding infrastructure that addresses a range of infrastructure resilience issues that could have regionally and nationally significant consequences. In 2017, the U.S. Department of Homeland Security’s Cybersecurity and Infrastructure Security Agency (CISA) sponsored the Washington State Transportation Systems RRAP project in coordination with the Washington Emergency Management Division (EMD), Washington State Department of Transportation (WSDOT), and other regional stakeholders. This project is focused on assessing the impacts of a Cascadia Subduction Zone (CSZ) earthquake on state transportation systems and, in particular, how those impacts may affect the ability of emergency response efforts to move supplies into the region. The intended outcome of this analysis is the prioritization of transportation routes and modes for additional planning, investment, hardening, or other activities to enhance their resilience - and therefore, to enhance their ability to support response and recovery efforts following a CSZ earthquake. An important part of this transportation system-level assessment has been to assess the seismic vulnerability of the state highway system. In doing so, the RRAP project team developed a Bridge Seismic Screening Tool (BSST) to assess, at a system-level, the potential impacts that a CSZ earthquake could have on state highway bridges. The first step in the BSST is to assess the seismic vulnerability of highway bridge infrastructure in Washington State following a CSZ earthquake to determine a projected or potential damage state. Damage states are then used to determine approximate reopening times for bridge crossings.2 This document provides details on the methodological development of the BSST, the implementation of that tool to analyze the projected damage incurred in a CSZ earthquake scenario, and the determination of corresponding reopening times of interstate and state highway bridges following such an event. The BSST was implemented programmatically using a Microsoft Excel macro-enabled workbook; instructions on use of this tool are provided in the accompanying report, Washington State Highway Bridge Seismic Screening Tool (BSST) - User Manual.
Design of a Highly Integrated Electric-Hydraulic Machine for Electrifying Off-Highway Vehicles
Off-highway vehicles represent a major portion of US energy consumption and greenhouse gas emissions. Electrifying or hybridizing these systems has the potential to yield substantial fuel savings through both efficiency improvements and energy recovery over the drive cycle. However, the extreme power density and transients of these systems pose unique challenges to electrification that have so-far prevented broad commercial success. To overcome these challenges, this paper proposes an integrated electric-to-hydraulic conversion machine. Three novel topologies are proposed which combine the rotor of radial and axial flux machines with an eccentric ball piston hydraulic pump. Design objectives for use in off-highway vehicles are defined, a sizing analysis is performed on each topology, and one topology is selected and optimized to investigate its suitability for meeting the unique requirements of an off-highway vehicle.
Economic Optimal Control for Minimizing Fuel Consumption of Heavy-Duty Trucks in a Highway Environment
This paper provides a comparative assessment of three economic optimal control strategies, aimed at minimizing the fuel consumption of heavy-duty trucks in a highway environment, under a representative lead vehicle model informed by traffic data. These strategies fuse a global, offline dynamic programming (DP) optimization with online model predictive control (MPC). We then show how two of the three strategies can be adapted to accommodate the presence of traffic and optimally navigate signalized intersections using infrastructure-to-vehicular (I2V) communication. The MPC optimization, which is local in nature, makes refinements to a coarsely (but globally, subject to grid resolution) optimized target velocity profile from the DP optimization. The three candidate economic MPC formulations that are evaluated include: a nonlinear time-based formulation that directly penalizes predicted fuel consumption, a nonlinear time-based formulation that penalizes braking effort as a surrogate for fuel consumption, and a linear distance-based convex formulation that maintains a tradeoff between energy expenditure and tracking of the coarsely optimized velocity profile obtained from DP. Using a medium-fidelity Simulink model, based on a Volvo truck's longitudinal and engine dynamics, we analyze the optimization's performance on four highway routes under various traffic scenarios. Results demonstrate 3.7-8.3% fuel economy improvement on highway routes without traffic and 6.5-10% on the same routes with traffic included. Furthermore, we present a detailed analysis of energy usage by "type" (aerodynamic losses, braking losses, and comparison of brake-specific fuel consumption), under each candidate control strategy.
EPA GHG certification of medium- and heavy-duty vehicles: Development of road grade profiles representative of US controlled access highways
In collaboration with the U.S. Environmental Protection Agency and the U.S. Department of Energy, the National Renewable Energy Laboratory has conducted a national analysis of road grade characteristics experienced by U.S. medium- and heavy-duty trucks on controlled access highways. These characteristics have been developed using TomTom's commercially available street map and road grade database. Using the TomTom national road grade database, national statistics on road grade and hill distances were generated for the U.S. network of controlled access highways. These statistical distributions were then weighted using data provided by the U.S. Environmental Protection Agency for activity of medium- and heavy-duty trucks on controlled access highways. Here, the national activity-weighted road grade and hill distance distributions were then used as targets for development of a handful of sample grade profiles potentially to be used in the U.S. Environmental Protection Agency's Greenhouse Gas Emissions Model certification tool as well as in dynamometer testing of medium- and heavy-duty vehicles and their powertrains.
Design of an Axial Flux Machine With an Integrated Hydraulic Pump for Off-Highway Vehicle Electrification
Axial flux machines offer an inherent torque density advantage over conventional radial flux machines and are increasingly being considered for transportation electrification applications. Electrification of off-highway vehicles is notoriously challenging due to extreme power density requirements. This paper investigates the use of axial flux machines for off-highway vehicle electrification. To maximize the power density, a hydraulic pump is integrated with the axial flux machine, resulting in a single modular, electric/hydraulic machine. This paper first compares three popular axial flux machine variants using an FEA-based design optimization approach. The single rotor, single stator variant is identified to be the most promising for integration with the hydraulic pump. Next, a multi-physics framework of the complete integrated hydraulic pump and axial flux machine is developed to search the design space. The results indicate promising potential for this concept to realize efficiency over 85% and power density over 5kW/kg for the complete machine (electric machine, hydraulic pump, and thermal management system), while utilizing conventional materials (thin gauge silicon steel, N45 magnets, and magnet wire)
Design and Optimization of a Fast-Switching Electromagnetic Valve Actuator for Off-highway Vehicle Hydraulic Systems
Electrification of off-highway vehicles offers improved efficiency and reduction in greenhouse gas emissions. However, the extremely low power densities of electric machines relative to the hydraulics used in conventional off-highway vehicles makes electrification challenging. Although the recently proposed Hybrid Hydraulic Electric Architecture (HHEA) can overcome this limitation, it requires frequent switching between the pressure rails using switching valves resulting in losses. Fast switching minimizes the switching losses in these valves, which otherwise significantly reduce the overall efficiency of the system. The primary contribution of this paper is the design and optimization of a linear electromagnetic actuator for a fast-switching valve. This paper identifies the actuator requirements to enable fast switching and presents a design and optimization technique to realize actuators that meet the design objectives. In addition, a multi-physics approach is proposed to model the performance of the complete switching valve by considering the electromagnetic, fluidic, and mechanical physics. An optimal actuator design is obtained using the developed optimization framework, and the multi-physics model demonstrates that the selected design achieves a 30% reduction in throttling-related switching losses by switching 70 % faster than comparable commercial valve designs.
Development of an Integrated Electro-Hydraulic Machine to Electrify Off-highway Vehicles
Electrification of off-highway vehicles is notoriously challenging due to extreme power density requirements. This paper proposes and develops an axial flux machine integrated with a hydraulic pump to realize a single modular, electro-hydraulic machine to electrify off-highway vehicle implements. This integrated machine eliminates redundant bearings, couplings, and shaft seals, re-uses surfaces, and enables direct cooling of the electric machine with the hydraulic fluid, to significantly increase power density. Three popular axial flux machine variants are first compared using an FEA-based design optimization approach. The single rotor, single stator variant is identified to be the most promising for integration with the hydraulic pump. Next, a multi-physics framework of the complete integrated hydraulic pump and axial flux machine is developed to characterize the design space. The results indicate promising potential for this concept to realize efficiency over 85% and power density over 5kW/kg for the complete machine (electric machine, hydraulic pump, and thermal management system), while utilizing conventional materials (thin gauge silicon steel, N45 magnets, and enamelled copper wire). Furthermore, a prototype axial flux machine has been experimentally characterized and integrated with a hydraulic pump to demonstrate the integrated electro-hydraulic machine concept.
Evaluation of Torque-Dense Electric Machine Technology for Off-Highway Vehicle Electrification
Electrification of off-highway vehicles offers the benefits of improved energy efficiency, enhanced control, and reduction in greenhouse gas emissions. However, progress towards electrification has been limited by the low torque density (30 kNm/m 3 ) of conventional electric machines compared to mobile hydraulic machines (up to approximately 1000 kNm/m 3 ). This paper reviews emerging variants of electric machines that offer a step improvement in torque density and potential pathway to enable electrified off-highway vehicles. First, sizing approaches for electric and hydraulic machines are developed, and torque-dense electric machines reviewed in literature are compared to commercial hydraulic machines to identify design trends in terms of speed, torque density, and power density. Next, key metrics are identified for the electric machine, based on which the following four emerging electric machine variants that promise to improve torque density are reviewed: i) multi-harmonic machines injection, ii) combined radial-axial flux machines, iii) magnetic gears, and iv) magnetically-geared machines. Here, the findings from this review show that these new electric machines can achieve upwards of 300% improvement in electric machine torque density, with several designs exceeding 100 kNm/m 3 , making them worthy candidates for further research to bridge the torque density gap with hydraulic machines.
A data-centric weak supervised learning for highway traffic incident detection
Using the data from loop detector sensors for near-real-time detection of traffic incidents on highways is crucial to averting major traffic congestion. While recent supervised machine learning methods offer solutions to incident detection by leveraging human-labeled incident data, the false alarm rate is often too high to be used in practice. Specifically, the inconsistency in the human labeling of the incidents significantly affects the performance of supervised learning models. To that end, we focus on a data-centric approach to improve the accuracy and reduce the false alarm rate of traffic incident detection on highways. We develop a weak supervised learning workflow to generate high-quality training labels for the incident data without the ground truth labels, and we use those generated labels in the supervised learning setup for final detection. This approach comprises three stages. First, we introduce a data preprocessing and curation pipeline that processes traffic sensor data to generate high-quality training data through leveraging labeling functions, which can be domain knowledge-related or simple heuristic rules. Second, we evaluate the training data generated by weak supervision using three supervised learning models-random forest, k-nearest neighbors, and a support vector machine ensemble-and long short-term memory classifiers. The results show that the accuracy of all of the models improves significantly after using the training data generated by weak supervision. Third, we develop an online real-time incident detection approach that leverages the model ensemble and the uncertainty quantification while detecting incidents. Finally, we show that our proposed weak supervised learning workflow achieves a high incident detection rate (0.90) and low false alarm rate (0.08).
System Design of Dynamic Wireless Power Transfer for Automated Highways
Dynamic wireless power transfer (DWPT) has been proposed as a solution to power electric vehicles (EVs)on future electrified highways. However, there has been little consideration of how the coordination of electric connected and automated vehicles (CAVs) could impact DWPT system designs in future scenarios. In this paper, a DWPT system design is optimized for a future highway where CAVs travel in coordinated groups, with each CAV in the group powered by the same DWPT section. As the distribution of smaller light-duty vehicles (LDVs) and larger heavy-duty vehicles (HDVs) in each group is varied, the DWPT system power level, transmitter length, and the equivalent receiver loads are adjusted to minimize the infrastructure requirements and energy losses of the DWPT system. The outputs from this analysis are used to determine the optimal groupings of vehicles for a given DWPT system. The analysis suggests that CAV coordination could aid the deployment of DWPT systems and reduce the overall infrastructure and energy losses of DWPT systems.
Empirical Estimation of Shortest Route Length along U.S. Interstate Highways Based on Great Circle Distance
In this study, 98 regression models were specified for easily estimating shortest distances based on great circle distances along the U.S. interstate highways nationwide and for each of the continental 48 states. This allows transportation professionals to quickly generate distance, or even distance matrix, without expending significant efforts on complicated shortest path calculations. For simple usage by all professionals, all models are present in the simple linear regression form. Only one explanatory variable, the great circle distance, is considered to calculate the route distance. For each geographic scope (i.e., the national or one of the states), two different models were considered, with and without the intercept. Based on the adjusted R-squared, it was observed that models without intercepts generally have better fitness. Additionally, all these models generally have good fitness with the linear regression relationship between the great circle distance and route distance. At the state level, significant variations in the slope coefficients between the state-level models were also observed. Furthermore, a preliminary analysis of the effect of highway density on this variation was conducted.
Enhanced Safety of Heavy-Duty Vehicles on Highways through Automatic Speed Enforcement – A Simulation Study
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. Here, 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. To conduct this analysis, we utilize SUMO, an open-source microscopic traffic simulator, enabling realistic simulations of real-world driving behaviors with and without speed cameras. The simulations also consider empirical distributions of desired headway gaps and reaction times exhibited by human drivers. This study offers a fresh perspective on highway safety by examining how the influence of automatic speed enforcement systems on driver behavior enhances the safety of HDV when faced with challenging situations. By shedding light on these dynamics, we contribute valuable insights to the ongoing research efforts on using technology to create safer roadways, ultimately promoting safer coexistence between heavy-duty and passenger vehicles.
Estimating Highway Volumes Using Vehicle Probe Data - Proof of Concept: Preprint
This paper examines the feasibility of using sampled commercial probe data in combination with validated continuous counter data to accurately estimate vehicle volume across the entire roadway network, for any hour during the year. Currently either real time or archived volume data for roadways at specific times are extremely sparse. Most volume data are average annual daily traffic (AADT) measures derived from the Highway Performance Monitoring System (HPMS). Although methods to factor the AADT to hourly averages for typical day of week exist, actual volume data is limited to a sparse collection of locations in which volumes are continuously recorded. This paper explores the use of commercial probe data to generate accurate volume measures that span the highway network providing ubiquitous coverage in space, and specific point-in-time measures for a specific date and time. The paper examines the need for the data, fundamental accuracy limitations based on a basic statistical model that take into account the sampling nature of probe data, and early results from a proof of concept exercise revealing the potential of probe type data calibrated with public continuous count data to meet end user expectations in terms of accuracy of volume estimates.
Enabling off-highway diesel engine downsizing and performance improvement using electrically assisted turbocharging
Internal combustion engine (ICE) downsizing through various turbocharging configurations is generally known by the powertrain design community as an effective means to reduce frictional losses, increase waste heat recovery, and improve fuel efficiency while increasing engine power density. However, often is the case that turbocharging strategies, including variable geometry turbochargers, and regulated two-stage turbochargers, incur the performance tradeoff between transient response and fuel economy (pumping losses) at high engine speeds. For off-highway vehicles having particularly transient and high-powered duty cycles, efforts to improve this tradeoff and increase operational flexibility have turned to evaluating various electrified air system architectures. In this study, a 4.5 L diesel-ICE configured with an electrically driven compressor (eBooster ® ) is placed in series with a conventional turbocharger and integrated into a 48 V mild-hybrid powertrain architecture. The objective of this powertrain configuration is to enable engine downsizing by 34%, replacing the current 6.8 L ICE platform with the hybridized 4.5 L ICE concept. The commercial 1-D simulation software GT-SUITE is used for powertrain system development and system optimization. Development and validation of the GT-SUITE model and air system controls is concurrently supported through experimental data collection. The simulation model development includes using machine learning methods for optimizing exhaust gas dilution, injection timing, and eBooster ® power to improve steady-state and transient brake-specific fuel consumption while minimizing criteria pollutant emissions. It was found that total specific fluid consumption over the standardized non-road transient engine duty cycle could be reduced by 18% over the current 6.8 L engine by using an optimized eBoosted 4.5 L engine. The hybridized 4.5 L engine concept concurrently showed sufficient transient response capability and nearly an order of magnitude reduction in duty cycle total soot production.