Results for “Connected and Automated Vehicles” — Page 7

DOE OSTI2020

Impact of Mixed Traffic on the Energy Savings of a Truck Platoon: Preprint

A two-truck platoon based on a prototype cooperative adaptive cruise control (CACC) system was tested on a closed test track in a variety of realistic traffic and transient operating scenarios - conditions that truck platoons are likely to face on real highways. The fuel consumption for both trucks in the platoon was measured using the SAE J1321 gravimetric procedure as well as calibrated J1939 instantaneous fuel rate, serving as proxies to evaluate the impact of aerodynamic drag reduction under constant-speed conditions. These measurements demonstrate the effects of: the presence of a multiple-passenger-vehicle pattern ahead of and adjacent to the platoon, cut-in and cut-out manoeuvres by other vehicles, transient traffic, the use of mismatched platooned vehicles (van trailer mixed with flatbed trailer), and the platoon following another truck with adaptive cruise control (ACC). These scenarios are intended to address the possibility of “background aerodynamic platooning” impacting realized savings on public roads. Using calibrated J1939 fuel rate analysis, fuel savings for curved track sections versus straight track sections were also evaluated for these scenarios, highlighting differences in the implementation of the CACC control strategies compared to a stock ACC implementation. The use of different trailer types and the presence of passenger-vehicle traffic patterns showed a measurable impact on platoon performance in some conditions, but the basic fuel savings trends were retained.

adaptive cruise control↗

DOE OSTI2020

Impact of Lateral Alignment on the Energy Savings of a Truck Platoon

A truck platooning system was tested using two heavy-duty tractor-trailer trucks on a closed test track to investigate the sensitivity of intentional lateral offsets over a range of intervehicle spacings. The fuel consumption for both trucks in the platoon was measured using the SAE J1321 gravimetric procedure while travelling at 65 mph and loaded to a gross weight of 65,000 lb. In addition, the SAE J1939 instantaneous fuel rate was calibrated against the gravimetric measurements and used as proxy for additional analyses. The testing campaign demonstrated the effects of intervehicle gaps, following-vehicle longitudinal control, and manual lateral control. The new results are compared to previous truck-platooning studies to reinforce the value of the new information and demonstrate similarity to past trends. Fuel savings for the following vehicle was observed to exceed 10% at closer following distances. The results showed that energy savings generally increased in a non-linear fashion as the gap was reduced. The impact of different following-truck lateral offsets had a measurable impact, with up to 4% reduction in total fuel-savings (relative to an isolated vehicle condition) observed for offsets up to 1.3 m. The fuel-consumption savings on the straight segments of the track exceeded those on the curved segments by upwards of 6% and highlight some potential differences expected between close-track testing and on-highway use.

adaptive cruise control↗

DOE OSTI2026

Detect the Unobservable: Abnormality Detection in mixed Autonomy for Lane Change Maneuver with Following Vehicles’ Trajectories Only

Highly Automated Vehicles (HAVs) and Advanced Driver-Assistance Systems (ADAS) are transforming modern transportation with enhanced mobility, safety, and efficiency. Despite their advantages, cybersecurity vulnerabilities in these systems can lead to abnormal behavior, posing significant risks to surrounding human-driven vehicles (HDVs) in mixed traffic environments. Here, this article addresses the challenge of detecting abnormal lateral movements of HAVs/ADAS vehicles using only trajectory profiles of following HDVs. Specifically, we propose a novel modeling approach that captures both normal and abnormal lateral behaviors through vehicle kinematics, integrated decision-making processes, vehicle control using symbolic regression for lane change vehicles. Additionally, we introduce an abnormality detection framework that relies on observable HDV data, even in occlusion scenarios. The framework evaluates the sensitivity of various car-following models to detect abnormal behaviors, providing insights into the interaction between HAVs/ADAS and HDVs in mixed autonomy systems.

Connected and Automated vehicles↗

DOE OSTI2020

Impact of Lateral Alignment on the Energy Savings of a Truck Platoon: Preprint

A truck platooning system was tested using two heavy-duty tractor-trailer trucks on a closed test track to investigate the sensitivity of intentional lateral offsets over a range of intervehicle spacings. The fuel consumption for both trucks in the platoon was measured using the SAE J1321 gravimetric procedure while travelling at 65 mph and loaded to a gross weight of 65,000 lb. In addition, the SAE J1939 instantaneous fuel rate was calibrated against the gravimetric measurements and used as proxy for additional analyses. The testing campaign demonstrated the effects of intervehicle gaps, following-vehicle longitudinal control, and manual lateral control. The new results are compared to previous truck-platooning studies to reinforce the value of the new information and demonstrate similarity to past trends. Fuel savings for the following vehicle was observed to exceed 10% at closer following distances. The results showed that energy savings generally increased in a non-linear fashion as the gap was reduced. The impact of different following-truck lateral offsets had a measurable impact, with up to 4% reduction in total fuel-savings (relative to an isolated vehicle condition) observed for offsets up to 1.3 m. The fuel-consumption savings on the straight segments of the track exceeded those on the curved segments by upwards of 6% and highlight some potential differences expected between close-track testing and on-highway use.

adaptive cruise control↗

DOE OSTI2021

Analysis of the Unsteady Wakes of Heavy Trucks in Platoon Formation and Their Potential Influence on Energy Savings

The authors present transient wind velocity measurements from two successive, well-documented truck platooning track-test campaigns to assess the wake-shedding behavior experienced by trucks in various platoon formations. Utilizing advanced analytics of data from fast-response (100-200-Hz) multi-hole pressure probes, this analysis examines aerodynamic flow features and their relationship to energy savings during close-following platoon formations. Applying Spectral analysis to the wind velocity signals, we identify the frequency content and vortex-shedding behavior from a forward truck trailer, which dominates the flow field encountered by the downstream trucks. The changes in dominant wake-shedding frequencies correlate with changes to the lead and follower truck fuel savings at short separation distances. All followers experience a reduction in wake-shedding frequency at following distances less than 20 m—the same zone where lead trucks experience fuel savings/drag reductions and where followers experience a reduction in platooning benefits. The dominant vortex-shedding frequency decreased by upwards of 35% in this region. Additionally, the third truck in a platoon is shown to experience a lower wake-shedding frequency than the second vehicle in either a two- or three-truck platoon. The correlation of changes in the vortex-shedding frequency to trends in drag reduction, and the associated energy savings, suggest that these phenomena may be related and may provide an opportunity for improving the efficiency potential of truck platooning.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Virtual Community Trains

When access to transit solutions is restricted to parts of a community, a division occurs what leads to disadvantaged socioeconomic conditions that only grows over time. This restriction causes difficulties in accessing needed resources such as medical facilities, employment, healthy food, education, healthy food, and more. To solve this restricted access, Labyrinth Smart Mobility and partners are developing a virtual community train that encourages scalability, energy efficiency, and equitability to all regardless of individual circumstances and limitation. The implementation of virtual community trains allows access to resources previously out of reach. The virtual community train enables one operator to drive a lead vehicle. This lead vehicle would then provide direction to a follow vehicle through vehicle-to-vehicle communication. Labyrinth Smart Mobility in conjunction with partners used various methods and resources to prove market feasibility, technical feasibility, economic need and viability, and more as well as design system requirements and architecture emphasis efficiency and safety. In doing so, Labyrinth Smart Mobility and partners will continue these advancements to further develop the virtual community train.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Leveraging Real-World Driving Data for Design and Impact Evaluation of Energy Efficient Control Strategies

Modeling and simulation are crucial in the development of advanced energy efficient control strategies. Utilizing real-world driving data as the underlying basis for control design and simulation lends veracity to projected real-world energy savings. Standardized drive cycles are limited in their utility for evaluating advanced driving strategies that utilize connectivity and on-vehicle sensing, primarily because they are typically intended for evaluating emissions and fuel economy under controlled conditions. Real-world driving data, because of its scale, is a useful representation of various road types, driving styles, and driving environments. The scale of real-world data also presents challenges in effectively using it in simulations. A fast and efficient simulation methodology is necessary to handle the large number of simulations performed for design analysis and impact evaluation of control strategies. In this study, two methods are presented of leveraging real-world data in both design optimization of energy efficient control strategies and in evaluating the real-world impact of those control strategies upon large-scale deployment. Through these methodologies, strategies with highest impact on energy savings were selected to be implemented as control algorithms. The developed algorithms were incorporated into a vehicle dynamics and powertrain control architecture implemented on a Cadillac CT6 demonstration vehicle. The control algorithms were then exercised on real-world driving scenarios to determine their impact on collective energy savings. The methodology utilizes the large-scale driving data sets maintained by the National Renewable Energy Laboratory to extract real-world driving scenarios and efficient simulation software tools. The insights obtained through this research help in guiding technology selection for energy efficient driving controls.

ARPA-E↗

DOE OSTI2021

Impact of Lateral Alignment for Cooling Airflow during Heavy-Truck Platooning

A truck platooning system was tested using two heavy-duty tractor-trailer trucks on a closed test track to investigate the thermal control/heat rejection system sensitivity to intentional lateral offsets over a range of intervehicle spacings. Previous studies have shown the following vehicle can experience elevated temperatures and reduced airflow through the cooling package as a result of close-formation platooning. Four anemometers positioned across the grille of the following trucks as well as aligned and multiple offset positions are used to evaluate the sensitivity of the impact. Straight sections of the track are isolated for the most accurate airflow impact measurements and to be most representative of on-highway driving. An intentional lateral offset in truck platooning is considered as a controls approach to mitigate reduced cooling efficacy at close following scenarios where the highest platoon savings are achieved. The truck platooning system was tested at four gap distances from 9 to 23 meters and the following truck was either aligned with the lead or offset with a 25% width (0.65 m) or 50% width (1.3 m) lateral distance. The experimental results clearly show that offset driving is effective at bringing cooling airflow from 56% with an aligned platoon back up to 75%-80% with minimal impact to fuel savings. In addition, the offset driving can also reduce the trailing truck’s engine coolant temperature by 2°C and the corresponding underhood temperature rise by 10°C. The test outcomes indicate a plausible controls strategy to mitigate reduced cooling flow during hot-weather truck platooning while still enabling the highest-energy-saving, close-following scenarios.

27 ARPA - Advanced Research Projects Agency-Energy↗

DOE OSTI2022

Energy Efficient Mobility Systems 2021 Annual Progress Report

DOE conducts research to understand how the changing mobility landscape will affect transportation energy consumption and identifies opportunities to create more efficient, affordable, reliable, accessible, equitable, and secure transportation options that enhance mobility for individuals and businesses. Within EERE, the EEMS Program is responsible for this research portfolio. This APR describes work that the EEMS Program conducted during FY 2021 in support of the EEMS Program goals.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Advance Local Mobility Through Energy Efficient Mobility Systems Technologies

Everyone deserves reliable, affordable, and safe transportation to connect people to jobs, healthcare, education, and recreation. Our transportation systems are interconnected, multimodal networks working together to move people and goods. These systems are dynamic and are being reshaped by factors such as population trends, new technologies, shifting labor models, economic forces, and changing climate. Energy efficient mobility systems (EEMS) technologies can help transportation planners ensure changes in our transportation systems are equitable and sustainable by improving energy efficiency, travel time, and affordability, as well as overall access to mobility. Transportation planners and decision makers can use the following EEMS tools and strategies to advance local mobility.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

Transforming Energy through Sustainable Mobility: Expanding Energy-Efficient Mobility Options

As the nation's premier facility for energy-efficient transportation research and development solutions, NREL blazes new trails with a whole-system approach that combines vehicle, building, and infrastructure connectivity and automation to optimize mobility and increase energy efficiency. NREL also explores new options for high-performance engines to maximize energy efficiency and on-road vehicle performance, while minimizing emissions, enhancing energy security, increasing U.S. jobs, and lowering drivers' costs.

ADVANCED PROPULSION SYSTEMS,DIRECT ENERGY CONVERSI↗

DOE OSTI2022

Energy and mobility impacts of connected autonomous vehicles with co-optimization of speed and powertrain on mixed vehicle platoons

Intersections are known to be traffic bottlenecks where a significant amount of energy consumption could be caused due to deceleration/acceleration in the presence of red signals. With an increased level of connectivity and automation of intelligent transportation systems, connected autonomous vehicles (CAVs) are expected to be able to proactively adjust their driving strategies subject to constraints imposed by the predicted future traffic. As a result, many potential benefits can be achieved, such as improved energy efficiency, enhanced traffic safety, among many others. Notably, the way CAVs are controlled affects the following legacy vehicles (LVs) due to complex traffic dynamics. Here, we are particularly interested in studying the energy and mobility impact of CAVs with an improved traffic prediction method on mixed vehicle platoons at various market penetration rates. Leveraging traffic prediction, CAVs are controlled with co-optimization of their speed and gear position. Specifically, a traffic prediction framework in a rolling horizon fashion is employed based upon a modified Payne–Whitham (PW) model capable of handling mixed traffic consisting of CAVs and LVs. The prediction error of the modified PW model is reduced by 53.62% compared to that of the standard PW model under test scenarios. According to the predicted traffic conditions, speed and gear position of CAVs are co-optimized with the primary goal of minimizing energy consumption when driving on a signalized arterial. The energy benefits achieved by CAVs and the impact of CAVs on LVs behind are studied comprehensively for mixed vehicle platoons. The lead LV follows a real-world speed profile collected on TH-55 in Minnesota. Numerical results show that energy benefits achieved by the vehicle platoon range from 2% to 16%, and a 1% to 5% reduction in travel time for LVs behind CAVs is also observed, at different penetration rates of CAVs in various traffic scenarios. Furthermore, it is observed that CAVs using the proposed eco-driving approach appear to have a positive impact on the LVs behind in terms of energy consumption, regardless of the driving styles of the LVs ahead.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

InfoRich VD&PT Controls Project Awarded Under the ARPA-E NEXTCAR Program (CRADA Final Report)

This CRADA supports the proposal led by General Motors LLC (GM) entitled "InfoRich VD&PT Controls" submitted under the Department of Energy (DOE) Advance Research Projects Agency-Energy (ARPA-E) NEXT-Generation Energy Technologies for Connected and Automated on-Road-vehicles (NEXTCAR) Program award DE-AR1564-1552. The project team is led by GM with sub-tier partners Carnegie Mellon University (CMU), and the National Renewable Energy Laboratory (NREL). The work under this NREL CRADA Joint Work Statement (JWS) is in support of GM's pending DOE Cooperative Agreement for DE-AR1564-1552 which extends through 04-01-2020.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

The Highly Integrated Vehicle Ecosystem (HIVE): A Platform for Managing the Operations of On-Demand Vehicle Fleets

This paper introduces the Highly Integrated Vehicle Ecosystem (HIVE), a transportation modeling tool developed by within the Center for Integrated Mobility Sciences (CIMS) group at the National Renewable Energy Laboratory (NREL). HIVE is an agent-based supply/demand model for Mobility on Demand (MoD) which mixes agent-based modeling and centralized dispatch for automated and human-driven fleets and ride hail passengers. Research questions using HIVE span multiple categories, including intelligent fleet planning (assessing fleet, battery, and infrastructure investment decisions), intelligent fleet control (charge management, vehicle dispatching) and strategic business model decision-making (depot-based full-time drivers versus gig-based drivers, human-driven versus automated). The components of the HIVE model are explained and then HIVE is demonstrated in a case study using demand data from the New York City Taxi & Limousine Commission data set.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

Multilane Automated Driving With Optimal Control and Mixed-Integer Programming

Road vehicle lane changes often initiate traffic disturbances and can therefore impact road networks’ energy and time efficiency. Furthermore, unexpected changes in traffic conditions may also render lane changes counterproductive for the lane-changing vehicle. Vehicle-to-vehicle connectivity combined with anticipative control could address these challenges via improved lane change decisions by automated vehicles. In a move toward this objective, receding horizon control cast as a mixed-integer quadratic program is used to plan lane changing and acceleration in a coupled optimization. A long-term pacing module, based on Pontryagin’s minimum principle from optimal control theory, sets terminal and input references for receding horizon control to target a user’s expected travel time. To remove nonlinear vehicle dynamics from the receding horizon controller, lane change commands are passed to a pure pursuit steering module whose response is approximated by a second-order linear model. Here, comparison against a rule-based reactive algorithm in arterial and highway scenarios shows an 8.9%–13.7% reduction in energy consumption and a 5.2%–10.3% reduction in the travel time, along with navigational improvements.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

Human-automated vehicle interactions

This dissertation is proposed to answer the question: how can the interactions between human and automated vehicles be used to improve the overall performance of automated driving technology? Multiple different modules in automated vehicles such as the perception, motion plan and motion control modules can potentially be benefitted from human-automated vehicle interactions. For perception module, the self-correction of faulty sensors can be achieved using human demonstration data. For motion plan and motion control modules, the performance of the low-level motion controller can be improved with the help of human demonstration, and the behavior of the motion planner can be improved using human intervention data during automated driving. Moreover, a better model for a human driver could improve the overall efficiency and comfort of vehicles in connected mixed traffic. In this dissertation, the technical research toward these goals has been completed and has resulted in several peer-reviewed publications. Optimization methods and model predictive control are used extensively to improve energy efficiency while maintaining safe and comfort driving. An inverse model predictive control (IMPC) method has been developed and it has been proven to be effective in modeling the motion of human driven vehicles. The proposed method has demonstrated its benefits in both connected automated highway driving and the bilateral adaptation of human driver and automated driving controller in human-in-the loop simulations. The proposed future research seeks to broaden the application of IMPC by considering a more comprehensive cost function design and applying it to more complex driving situations.

Guo, Longxiang↗

DOE OSTI2021

HUMAN-AUTOMATED VEHICLE INTERACTIONS

This dissertation is proposed to answer the question: how can the interactions between human and automated vehicles be used to improve the overall performance of automated driving technology? Multiple different modules in automated vehicles such as the perception, motion plan and motion control modules can potentially be benefitted from human-automated vehicle interactions. For perception module, the self-correction of faulty sensors can be achieved using human demonstration data. For motion plan and motion control modules, the performance of the low-level motion controller can be improved with the help of human demonstration, and the behavior of the motion planner can be improved using human intervention data during automated driving. Moreover, a better model for a human driver could improve the overall efficiency and comfort of vehicles in connected mixed traffic. In this dissertation, the technical research toward these goals has been completed and has resulted in several peer-reviewed publications. Optimization methods and model predictive control are used extensively to improve energy efficiency while maintaining safe and comfort driving. An inverse model predictive control (IMPC) method has been developed and it has been proven to be effective in modeling the motion of human driven vehicles. The proposed method has demonstrated its benefits in both connected automated highway driving and the bilateral adaptation of human driver and automated driving controller in human-in-the loop simulations. The proposed future research seeks to broaden the application of IMPC by considering a more comprehensive cost function design and applying it to more complex driving situations.

Guo, Longxiang↗

NASA NTRS2020

New Flight Rules to Enable the Era of Aerial Mobility in the National Airspace System

In the 21st Century, new aviation markets, vehicle types, and technologies are fast emerging, inspiring new operational concepts for the National Airspace System such as Unmanned Aircraft Systems Traffic Management and Urban Air Mobility. These novel operations envision a dramatic increase in aerial mobility, or the ability to navigate freely through the airspace with unprecedented access and operational flexibility. Implementing these concepts presents a major challenge to the existing operational modes of Visual Flight Rules (VFR) and Instrument Flight Rules (IFR), developed under the limitations of early 20th Century technology and procedures to ensure safe navigation and separation from traffic. To meet this challenge and to support the needs of operators in the 21st Century and beyond, this paper proposes that VFR and IFR be augmented by new flight rules – Digital Flight Rules (DFR) – that leverage modern and emerging technologies and are not bound by restrictions borne of the state of technology 75-100 years ago. The objective of DFR is to provide safe and unfettered access to the airspace to all participating vehicle operators under all visibility conditions without incurring the limitations in operational flexibility inherent to IFR and even VFR. Advancements in communications, navigation, surveillance, aircraft connectivity, information access, automation technology, and supporting ground infrastructure provide the opportunity for the vehicle operator to engage at an unprecedented level in managing their flights regardless of flight visibility. Under DFR, these advancements enable the vehicle operator to assume full responsibility for traffic separation and therefore full trajectory management authority in all visibility conditions and airspace regions. The changes in roles and responsibilities are expected to enable greater airspace access and operational flexibility than afforded by IFR and VFR, thus enabling the emergence of new operations and a new era of advanced aerial mobility.

Mobility↗