Results for “Connected vehicles” — Page 8

DOE OSTI2022

Evaluating an Eco-Cooperative Automated Control System

The paper evaluates an Eco-Cooperative Automated Control (Eco-CAC) system on a large-scale network considering a combination of internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), and battery-only electric vehicles (BEVs) in a microscopic traffic simulation environment. We used a novel integrated control system that: (1) routes ICEVs, HEVs, and BEVs in a fuel/energy-efficient manner; (2) selects vehicle speeds based on anticipated traffic network evolution; (3) minimizes vehicle fuel/energy consumption near signalized intersections; and (4) intelligently modulates the longitudinal motion of vehicles along freeways within a cooperative platoon to minimize fuel/energy consumption. The study tested the system using the INTEGRATION software on the Los Angeles (LA), U.S., downtown network for three different demand levels: no congestion, mild congestion, and heavy congestion. The results demonstrated that the Eco-CAC system effectively reduces vehicle fuel and energy consumption, travel time, total delay, and stopped delay in heavily congested conditions. However, different vehicle compositions produced different results. In particular, the maximum energy consumption savings for BEVs (36.9%) for a current vehicle composition occurred at a 10% market penetration rate (MPR) of connected automated vehicles (CAVs) in mild congestion, while the maximum savings for a future vehicle composition (35.5%) occurred at a 50% CAV MPR in no congestion. The system reduced fuel consumption for ICEVs and HEVs by up to 5.4% and 6.3% at a 25% CAV MPR in heavy congestion for current and future vehicle compositions, respectively. However, the system increased total fuel consumption by up to 4.6% at a 50% CAV MPR in no congestion for a current vehicle composition. The study demonstrates that the effectiveness of the Eco-CAC system depends on traffic conditions, including congestion level, network configuration, CAV MPR, and vehicle composition.

Engineering↗

DOE OSTI2024

"Hierarchical Speed Planner for Automated Vehicles: A Framework for Lagrangian Variable Speed Limit in Mixed Autonomy Traffic"

This paper introduces a novel control framework for Lagrangian variable speed limits in hybrid traffic flow environments utilizing automated vehicles (AVs). The framework was validated using a fleet of 100 connected automated vehicles as part of the largest coordinated open-road test designed to smooth traffic flow. The framework includes two main components: a high-level controller deployed on the server side, named Speed Planner, and low-level controllers called vehicle controllers deployed on the vehicle side. The Speed Planner designs and updates target speeds for the vehicle controllers based on real-time Traffic State Estimation (TSE) [1]. The Speed Planner comprises two modules: a TSE enhancement module and a target speed design module. The TSE enhancement module is designed to minimize the effects of inherent latency in the received traffic information and to improve the spatial and temporal resolution of the input traffic data. The target speed design module generates target speed profiles with the goal of improving traffic flow. The vehicle controllers are designed to track the target speed meanwhile responding to the surrounding situation. The numerical simulation indicates the performance of the proposed method: the bottleneck throughput has increased by 5.01%, and the speed standard deviation has been reduced by a significant 34.36%. We further showcase an operational study with a description of how the controller was implemented on a field-test with 100 AVs and its comprehensive effects on the traffic flow.

Wang, Han↗

DOE OSTI2020

Modifying Network Guided Vehicle for Repeatable EcoCAR ACC Energy Testing

The newest iteration of the Department of Energy (DOE) Advanced Vehicle Technology Competitions is the EcoCAR Mobility Challenge. In this paper, ANL and HORIBA MIRA collaborate to propose and pilot a test method that enables controlled, repeatable track-based energy consumption testing of semi-autonomous features. The testing was performed at MIRA’s proving ground facility in Nuneaton, UK, using their Network Guided Vehicle (NGV) connectivity and automation test platform. The NGV served as a repeatable lead vehicle following prescribed drive traces around two different track circuits while the host vehicle under test, a Honda Civic with full speed range Adaptive Cruise Control, followed behind. Over the course of a two day test, multiple iterations of certification and custom EPA drive traces were performed on track, demonstrating the viability of a two vehicle energy test. Results were analyzed to assess NGV lead vehicle performance in following the prescribed cycle using SAE J2951 standard calculations in an effort to quantify how repeatably and accurately the lead vehicle followed the trace. Host vehicle energy consumption was assessed using both vehicle CAN bus monitoring and an on-board fuel flow meter.

ACC↗

NASA NTRS1991

Experimental validation of docking and capture using space robotics testbeds

Docking concepts include capture, berthing, and docking. The definitions of these terms, consistent with AIAA, are as follows: (1) capture (grasping)--the use of a manipulator to make initial contact and attachment between transfer vehicle and a platform; (2) berthing--positioning of a transfer vehicle or payload into platform restraints using a manipulator; and (3) docking--propulsive mechanical connection between vehicle and platform. The combination of the capture and berthing operations is effectively the same as docking; i.e., capture (grasping) + berthing = docking. These concepts are discussed in terms of Martin Marietta's ability to develop validation methods using robotics testbeds.

Spofford, John↗

NASA NTRS2009

Application of CFE/POST2 for Simulation of Launch Vehicle Stage Separation

The constraint force equation (CFE) methodology provides a framework for modeling constraint forces and moments acting at joints that connect multiple vehicles. With implementation in Program to Optimize Simulated Trajectories II (POST 2), the CFE provides a capability to simulate end-to-end trajectories of launch vehicles, including stage separation. In this paper, the CFE/POST2 methodology is applied to the Shuttle-SRB separation problem as a test and validation case. The CFE/POST2 results are compared with STS-1 flight test data.

Pamadi, Bandu N.↗

DOE OSTI2020

Microsimulation of Energy and Flow Effects from Optimal Automated Driving in Mixed Traffic

In this paper we study the energy and traffic impact of a proposed Anticipative Cruise Controller in a PTV VISSIM microsimulation environment. We dissect our controller into two parts: 1. the unconnected mode, active when following a human-driven vehicle, and 2. the connected mode, active when following another automated vehicle equipped with connectivity. Probabilistic constraints balance safety considerations with inter-vehicle compactness, and vehicle constraints for acceleration capabilities are expressed through the use of powertrain maps. Emergent highway traffic scenarios are then modeled using time headway distributions from empirical traffic data. To study the impact of automation over a range of demands of free-flow to stop-and-go, we vary vehicle flux from low to high and vary automated vehicle penetration from low to high. When examining all-human driving scenarios, network capacity failed to meet demand in high-volume scenarios, such as rush-hour traffic. We further find that with connected automated vehicles introduced, network capacity was improved to support the high-volume scenarios. Finally, we examine energy efficiencies of the fleet for conventional, electric, and hybrid vehicles. We find that automated vehicles perform at a 10%-20% higher energy efficiency over human drivers when considering conventional powertrains, and find that automated vehicles perform at a 3%-9% higher energy efficiency over human drivers when considering electric and hybrid powertrains. Due to secondary effects of smoothing traffic flow and reducing unnecessary braking, energy benefits also apply to human-driven vehicles that interact with automated ones. Such simulated humans were found to drive up to 10% more energy-efficiently than they did in the baseline all-human scenario.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Operation and Control of Electric Vehicle Charger with Enhanced Dynamic Performance Under Non-Ideal Grid Voltage Condition

This paper presents a three-phase electric vehicle charger connected to the grid, featuring multiple boost converters on the DC side, specifically designed to ensure smooth, oscillation-free power transfer during unsymmetrical voltage sags. Precise control mechanisms are implemented on the boost converter side to regulate both voltage and current on the electric vehicle side, thereby maintaining optimal charging conditions. The control architecture for both the grid-connected and boost converter components is based on the Lyapunov energy function, which is employed to achieve superior dynamic performance and stability. The system's robustness and reliability are demonstrated through its ability to maintain stable operation and efficient power transfer despite fluctuations in grid conditions. Furthermore, the implementation of Lyapunov-based control ensures rapid response and minimal energy loss, enhancing the overall efficiency of the system. To validate the effectiveness of this approach, a comprehensive model of the system was developed and tested using MATLAB/Simulink, with detailed computer simulations conducted across various significant case studies.

DC-DC boost converter↗

DOE OSTI2021

Optimal Sizing of an Electric Vehicle Charging Station with Integration of PV and Energy Storage

This paper proposes an optimization model for the optimal configuration of an grid-connected electric vehicle (EV) extreme fast charging station considering integration of photovoltaic (PV) and energy storage. The proposed model minimizes the annualized net cost (i.e., maximizes the annualized net profit) of the extreme fast charging station, including investment and maintenance cost of charging ports, PV and energy storage, net cost of purchasing energy from utility and selling energy to EV customers, degradation cost of energy storage and demand charge. The decision variables are number of charging ports, capacity of invested PV and the power and energy ratings of invested energy storage. The Erlang-loss system is adopted to model the EV mobility. Results of numerical simulations indicate that investment of PV and energy storage could increase the annualized profit of the extreme fast charging station. In addition, the impacts of various parameters on the optimal solution are investigated by sensitivity analysis.

Liu, Guodong↗

NASA NTRS2003

Solar Powered Automobile Interior Climate Control System

There is provided a climate control system for a parked vehicle that includes a solar panel, thermostatic switch, fans, and thermoelectric coolers. The solar panel can serve as the sole source of electricity for the system. The system affords convenient installation and removal by including solar panels that are removably attached to the exterior of a vehicle. A connecting wire electrically connects the solar panels to a housing that is removably mounted to a partially opened window on the vehicle. The thermostatic switch, fans, and thermoelectric coolers are included within the housing. The thermostatic switch alternates the direction of the current flow through the thermoelectric coolers to selectively heat or cool the interior of the vehicle. The interior surface of the thermoelectric coolers are in contact with interior heat sinks that have air circulated across them by an interior fan. Similarly, the exterior surface of the thermoelectric coolers are in contact with exterior heat sinks that have air circulated across them by an exterior fan.

Richard T Howard↗

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↗

DOE OSTI2021

Developing an Eco-Cooperative Automated Control System (Eco-CAC)

The goal of the project was to develop a novel Eco-Cooperative Automated Control (Eco-CAC) system that integrates vehicle dynamics (VD) control with connected and automated vehicle (CAV) applications. In particular, the team developed a novel integrated control system that (1) routes vehicles in a fuel/energy-efficient manner for internal combustion engine vehicles (ICEVs), battery-only electric vehicles (BEVs), and hybrid electric vehicles (HEVs); (2) selects vehicle speeds based on anticipated traffic network evolution; (3) minimizes vehicle fuel/energy consumption near signalized intersections; and (4) intelligently modulates the longitudinal motion of vehicles within a cooperative platoon to minimize its fuel/energy consumption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Cooperative Automated Cohort Driving on Connected Infrastructure, Arterial Roadways, and Highways: Final Project Demonstration and System-of-Systems Model Correlation

This project seeks to synergize vehicle automated driving and connectivity data to improve mobility and energy efficiency of groups of mixed vehicles operating in close proximity (vehicle cohort) on various infrastructure. A custom cellular communication network links vehicles operating as a cohort with infrastructure to a centralized system-of-systems digital twin with an AI-based optimal behavior planner. The data contained in this set are from final testing and technology demonstrations to U.S. Department of Energy staff at the American Center for Mobility. The data contain single-lane, single-light scenarios; multi-lane, multi-light arterial scenarios; and limited-access highway scenarios. All test cases were derived from simulations and replicated on the test track. The project employed two and four light-duty vehicles with connectivity and drive automation for the testing. The baseline scenario without connectivity was run under the control of the system-of-systems centralized planner but operating each vehicle with an intelligent driver model controlling the velocity, lane utilization, and vehicle gap. This was to ensure the highest compatibility with the simulation in terms of dynamic behavior. The connected cohort case utilized AI optimization to perform coordinated and cooperative control for energy, as well as safe, comfortable behavior for the cohort. The dataset is appropriately named with unconnected and connected designations, with comparisons sharing the same run index number. The included PowerPoint and PDF files describe the test setup and provide an overview of results from the project. ![image](de-EE0009209_March_2023_Data_Arterial_Scenario_Results.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Hierarchical Speed Planner for Automated Vehicles: A Framework for Lagrangian Variable Speed Limit in Mixed-Autonomy Traffic

Here, this article presents a novel hierarchical speed planning framework for variable speed limits in mixed-autonomy traffic environments, leveraging server-side macroscopic control and vehicle-side microscopic execution. The framework integrates real-time traffic state estimation (TSE) and reinforcement learning (RL)-based control to mitigate congestion and improve traffic flow. A TSE enhancement module combines macroscopic data from sources like INRIX with high-resolution observations from connected autonomous vehicles (CAVs), enabling predictive modeling to address latency and noise. The target speed design module employs kernel smoothing and a buffer zone strategy to optimize traffic density and flow around bottlenecks. The proposed system was validated in the largest open-road test to date with 100 CAVs, demonstrating an overall 8% traffic density decrease, with a specific decrease of 7% upstream, 10% downstream, and a 52% decrease during the congestion formation phase at bottlenecks.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Formal methods approach to the charging facility location problem for battery electric vehicles

Battery electric vehicles (BEVs) are becoming more prevalent as improvements in battery technology and energy management continue to be made. As the number of electric vehicles grows, the demand for fast-charging stations is expected to increase dramatically. Thus, building new charging station infrastructure efficiently will be key to reducing upfront costs while meeting consumer demands. In this work, we propose a method for choosing a set of charging station locations that are optimized based on a set of given common vehicle demand points. As part of this solution, we also offer a novel abstraction of the road network on which energy-efficient paths that account for charge-time delays may be found. The current algorithm chooses the optimal charging locations for a single agent which has a route objective specified using temporal logic. To demonstrate the proposed method, the running example shows how a charging station could be chosen for an electric delivery vehicle. Simulations were run on sample road networks with a given set of demand points to service and potential charging station locations to compare. The method is shown to successfully rank potential charging stations in terms of their expected average charging time cost.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

RouteE: A Vehicle Energy Consumption Prediction Engine

The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are established using NREL's Future Automotive Systems Technology Simulator paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center, resulting in energy consumption behavior estimates over a representative sample of driving conditions for the United States. Validations have been performed using on-road fuel consumption data for conventional and electrified vehicle powertrains. Transferring the results of the on-road validation to a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology in a green-routing application would accurately select the route that consumes the least fuel 90% of the time. The novel machine learning techniques used in RouteE make it a flexible and robust tool for a variety of transportation applications.

47 OTHER INSTRUMENTATION↗

NASA NTRS2019

NASA's Space Launch System: Payload Opportunities for Lunar Exploration, Science Missions

Nearing its first launch, the Space Launch System (SLS), NASA’s new super heavy-lift launch vehicle, offers highly energetic launches that deliver more mass and provide more volume in 8.4 m-diameter and potentially larger fairings to make a new generation of deep space missions possible. An evolvable launcher available in crew, crew with a co-manifested payload (CPL) and cargo-only configurations, SLS is a crucial capability to enable astronauts to return to the Moon, but it also offers key benefits for science missions. NASA’s 21st-century return to the Moon recently received a formal name: the Artemis program. In addition to the core enabling capabilities of SLS and the Orion crew spacecraft, NASA will also enlist international and commercial partnerships for Artemis. The Agency intends to build a scientific outpost in lunar orbit, the Gateway, from which human and robotic missions to and from the surface can rendezvous. SLS will launch Orion on a series of missions leading to landing the first woman and the next man on the Moon as part of Artemis. SLS uses proven propulsion systems: two solid rocket boosters and four RS-25s engines that have been upgraded to provide more thrust and operate in the SLS environment. SLS Block 1 uses a modified Delta IV Heavy upper stage, called the Interim Cryogenic Propulsion Stage (ICPS) and lifts at least 26 metric tons (t) to trans-lunar injection (TLI). The Block 1 vehicle can also be outfitted with a 5 m-diameter fairing. Block 1B, the next major variant, also uses solid rocket boosters and RS-25 engines to achieve Earth escape velocity, but replaces the single-engine liquid hydrogen (LH2)/liquid oxygen (LOX) ICPS with a four-engine LH2/LOX Exploration Upper Stage (EUS) to increase mass to TLI to 34-37 t, depending on crew or cargo configuration. In the Block 1B crew configuration, a 10 m-tall Universal Stage Adapter (USA) connects the vehicle to Orion and can carry a CPL up to 10 t. The USA provides 286 m3 of unpressurized volume for payloads. For large payloads, 8.4 m- and 10 m-diameter cargo fairings in 19.1 m and 27.4 m lengths are possible. The ultimate SLS vehicle, Block 2, incorporates evolved boosters to reach a lift capacity of more than 45 t to TLI. The capabilities of SLS not only make new missions to the Moon possible, but also game-changing science missions, such as deployment of large-aperture space telescopes, spacecraft to the ice giants or even probes to the interstellar medium. This paper will discuss the capabilities of SLS, the vehicle’s planned evolution, missions that can effectively utilize the vehicle and manufacturing status of the vehicle.

Creech, Stephen D.↗

NASA NTRS2019

NASA's Space Launch System: Payload Opportunities for Lunar Exploration, Science Missions

Nearing its first launch, the Space Launch System (SLS), NASA’s new super heavy-lift launch vehicle, offers highly energetic launches that deliver more mass and provide more volume in 8.4 m-diameter and potentially larger fairings to make a new generation of deep space missions possible. An evolvable launcher available in crew, crew with a co-manifested payload (CPL) and cargo-only configurations, SLS is a crucial capability to enable astronauts to return to the Moon, but it also offers key benefits for science missions. NASA’s 21st-century return to the Moon recently received a formal name: the Artemis program. In addition to the core enabling capabilities of SLS and the Orion crew spacecraft, NASA will also enlist international and commercial partnerships for Artemis. The Agency intends to build a scientific outpost in lunar orbit, the Gateway, from which human and robotic missions to and from the surface can rendezvous. SLS will launch Orion on a series of missions leading to landing the first woman and the next man on the Moon as part of Artemis. SLS uses proven propulsion systems: two solid rocket boosters and four RS-25s engines that have been upgraded to provide more thrust and operate in the SLS environment. SLS Block 1 uses a modified Delta IV Heavy upper stage, called the Interim Cryogenic Propulsion Stage (ICPS) and lifts at least 26 metric tons (t) to trans-lunar injection (TLI). The Block 1 vehicle can also be outfitted with a 5 m-diameter fairing. Block 1B, the next major variant, also uses solid rocket boosters and RS-25 engines to achieve Earth escape velocity, but replaces the single-engine liquid hydrogen (LH2)/liquid oxygen (LOX) ICPS with a four-engine LH2/LOX Exploration Upper Stage (EUS) to increase mass to TLI to 34-37 t, depending on crew or cargo configuration. In the Block 1B crew configuration, a 10 m-tall Universal Stage Adapter (USA) connects the vehicle to Orion and can carry a CPL up to 10 t. The USA provides 286 m3 of unpressurized volume for payloads. For large payloads, 8.4 m- and 10 m-diameter cargo fairings in 19.1 m and 27.4 m lengths are possible. The ultimate SLS vehicle, Block 2, incorporates evolved boosters to reach a lift capacity of more than 45 t to TLI. The capabilities of SLS not only make new missions to the Moon possible, but also game-changing science missions, such as deployment of large-aperture space telescopes, spacecraft to the ice giants or even probes to the interstellar medium. This paper will discuss the capabilities of SLS, the vehicle’s planned evolution, missions that can effectively utilize the vehicle and manufacturing status of the vehicle.

Creech, Steve↗