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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2024

Implications of stop-and-go traffic on training learning-based car-following control

Learning-based car-following control (LCC) of connected and autonomous vehicles (CAVs) is gaining significant attention with the advancement of computing power and data accessibility. While the flexibility and large model capacity of model-free architecture enable LCC to potentially outperform the model-based car-following (CF) model in improving traffic efficiency and mitigating congestion, the generalizability of LCC for traffic conditions different from the training environment/dataset is not well-understood. Herein, this study seeks to explore the impact of stop-and-go traffic in the training dataset on the generalizability of LCC. It uses the characteristics of lead vehicle trajectories to describe stop-and-go traffic, and links the theory of identifiability (i.e., obtaining a unique parameter estimation result using sensor measurements) to the generalizability of behavior cloning (BC) and policy-based deep reinforcement learning (DRL). Correspondingly, the study shows theoretically that: (i) stop-and-go traffic can enable the property of identifiability and enhance the control performance of BC-based LCC in different traffic conditions; (ii) stop-and-go traffic is not necessary for DRL-based LCC to generalize to different traffic conditions; (iii) DRL-based LCC trained with only constant-speed lead vehicle trajectories (not sufficient to ensure identifiability) can be generalized to different traffic conditions; and (iv) stop-and-go traffic increases variance in the training dataset, which improves the convergence of parameter estimation while negatively impacting the convergence of DRL to the optimal control policy. Numerical experiments validate the above findings, illustrating that BC-based LCC entails comprehensive training datasets for generalizing to different traffic conditions, while DRL-based LCC can achieve generalization with simple free-flow traffic training environments. This further suggests DRL as a more promising and cost-effective LCC approach to reduce operational costs, mitigate traffic congestion, and enhance safety and mobility, which can accelerate the deployment and acceptance of CAVs.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRSDate not supplied

The Stop-and-Go Mechanism: Towards an Integrated Approach to Model Seismicity, Outgassing, Deformation, and Thermal Unrest at Active Volcanoes

Connecting the geophysical and geochemical signals recorded at and above the surface of volcanoes with source mechanisms is fundamental to understand transitions from quiescence to eruption, and to integrate the behavior of volcanoes with their regional seismotectonic and hydrological context. Some of these signals include shallow volcanic tremor, a long-lasting (minutes-to-years) ground vibration detected in volcanic areas during unrest; volcanic outgassing, which shows intriguing periodicities over multiple scales, as revealed by space-borne instruments and by ground-based, high-frequency (~1 Hz), sampling methods; inflation and deflation of volcanic edifices, commonly associated with subsurface volume changes; and large-scale (from a few to tens of km2), low-temperature, thermal anomalies, a newly-discovered signal that has been observed to emerge on volcanic flanks from months-to-years prior to gas- and magma-driven eruptions. Traditionally, these geophysical and geochemical signals have been modelled independently, which limits our interpretation of subsurface processes and thus our assessment of unrest. In this work, we show through lumped-parameter models, numerical simulations, and preliminary laboratory experiments that many of the signals recorded around volcanoes can be integrated into a common “stop-and-go” mechanism. In particular, we found that they can arise spontaneously when taking into account the feedbacks between the temporary accumulation of gas and/or magma in the crust (“stop”), and the diffusive transfer of gas and heat towards the surface (“go”). For example, we find that the “stop-and-go” mechanism can explain: (i) the emergence of monochromatic, broadband, and harmonic tremor; (ii) the periodic components commonly observed in volcanic outgassing time series; (iii) the link between large-scale thermal anomalies and shallow hydrothermal systems; (iv) the reported time lags between large-scale thermal anomalies and deformation; and (v) the breathing of potentially destructive volcanic calderas. The “stop-and-go” mechanism provides a consistent and realistic framework to link multiple geophysical and geochemical signals with the processes leading to volcanic unrest and eruption.

volcanoes↗

DOE OSTI2025

Traffic Smoothing Using Explicit Local Controllers: Experimental Evidence for Dissipating Stop-and-go Waves with a Single Automated Vehicle in Dense Traffic

This article presents experimental evidence of the ability of a single automated vehicle acting as a controller to effectively dissipate stop-and-go waves in real traffic. The automated vehicle succeeded in stabilizing the speed profile by reducing oscillations in time and speed variations between vehicles during rush hour on I-24 in the Nashville area. Here, we detail the control design, deployment and results obtained in this experiment, conducted as part of the CIRCLES consortium’s “MegaVanderTest” 2022, which involved a total of 100 automated vehicles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Traffic Control via Connected and Automated Vehicles (CAVs): An Open-Road Field Experiment with 100 CAVs

The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. Also called “phantom jams” or “stop-and-go waves,” these instabilities are a significant source of wasted energy. Toward this goal, the CIRCLES project designed a control system, referred to as the MegaController by the CIRCLES team, that could be deployed in real traffic. Our field experiment, the MegaVanderTest (MVT), leveraged a heterogeneous fleet of 100 longitudinally controlled vehicles as Lagrangian traffic actuators, each of which ran a controller with the architecture described in this article. The MegaController is a hierarchical control architecture that consists of two main layers. The upper layer is called the Speed Planner and is a centralized optimal control algorithm. It assigns speed targets to the vehicles, conveyed through the LTE cellular network. The lower layer is a control layer, running on each vehicle. It performs local actuation by overriding the stock adaptive cruise controller, using the stock onboard sensors. The Speed Planner ingests live data feeds provided by third parties as well as data from our own control vehicles and uses both to perform the speed assignment. The architecture of the Speed Planner allows for the modular use of standard control techniques, such as optimal control, model predictive control (MPC), kernel methods, and others. The architecture of the local controller allows for the flexible implementation of local controllers. Corresponding techniques include deep reinforcement learning (RL), MPC, and explicit controllers. Depending on the vehicle architecture, all onboard sensing data can be accessed by the local controllers or only some. Likewise, control inputs vary across different automakers, with inputs ranging from torque or acceleration requests for some cars to electronic selection of adaptive cruise control (ACC) setpoints in others. The proposed architecture technically allows for the combination of all possible settings proposed previously, that is {Speed Planner algorithms} × {local Vehicle Controller algorithms} × {full or partial sensing} × {torque or speed control}. As a result, most configurations were tested throughout the ramp up to the MegaVandertest (MVT).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Design, Preparation, and Execution of the 100-AV Field Test for the CIRCLES Consortium: Methodology and Implementation of the Largest Mobile Traffic Control Experiment to Date

This article presents the comprehensive design, setup, execution, and evaluation of the MegaVanderTest (MVT) experiment conducted by the Congestion Impacts Reduction via CAV-in-the-Loop Lagrangian Energy Smoothing (CIRCLES) Consortium, which aimed to mitigate traffic congestion using partially autonomous vehicles (AVs) (see “Summary”). The experiment involved 100 vehicles on Nashville’s Interstate 24 (I-24) highway, utilizing various control algorithms to smooth stop-and-go traffic waves. The execution of the MVT experiment required a coordinated effort from multiple teams. This article details the meticulous planning process, the coordinated efforts of multiple teams, and the innovative use of a dynamic agent-based simulation framework for traffic evaluation. Here, the contributions of this work include demonstrating and providing a detailed roadmap for large-scale live traffic experiments, illustrating the lessons learned from the MVT experiment, and introducing the other articles in this issue and their complementary relationship in the MVT experiment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

From Sim to Real: A Pipeline for Training and Deploying Traffic Smoothing Cruise Controllers

Designing and validating controllers for connected and automated vehicles to enhance traffic flow presents significant challenges, from the complexity of replicating real-world stop-and-go traffic dynamics in simulation, to the intricacies involved in transitioning from simulation to actual deployment. In this work, we present a full pipeline from data collection to controller deployment. Specifically, we collect 772 km of driving data from the I-24 in Tennessee, and use it to build a one-lane simulator, placing simulated vehicles behind real-world trajectories. Using policy-gradient methods with an asymmetric critic, we improve fuel efficiency by over 10% when simulating congested scenarios. Our comprehensive approach includes reinforcement learning for controller training, software verification, hardware validation and setup, and navigating various sim-to-real challenges. Furthermore, we analyze the controller's behavior and wave-smoothing properties, and deploy it on four Toyota Rav4’s in a real-world validation experiment on the I-24. Lastly, we release the driving dataset, the simulator and the trained controller, to enable future benchmarking and controller design.

42 ENGINEERING↗

NASA NTRS1976

The measurement of carbon monoxide and methane in the national capital air quality control region. III - Correlation interferometer results

Two types of experiments were performed with a correlation interferometer on-board a Bell Jet Ranger 206 Helicopter. The first consisted of simultaneous ground- and air-truth measurements as the instrumented helicopter passed over the Cheverly site. The second consisted of several measurement flights in and around the national capital air quality control region (Washington, D.C.). The correlation interferometer data, the infrared Fourier spectrometer data, and the integrated altitude sampling data showed agreement within the errors of the individual measurements. High values for CO were found from the D.C. flight data to be reproducible and concentrated in areas of stop-and-go traffic. It is concluded, that pollutants at low altitudes are detectable from an air-borne platform by remote correlation interferometry and that the correlation interferometer measurements agree with ground- and air-truth data.

Goldstein, H. W.↗

NASA NTRS1977

Baseline tests of the power-train electric delivery van

Vehicle maximum speed, range at constant speed, range over stop-and-go driving schedules, maximum acceleration, gradeability, gradeability limit, road energy consumption, road power, indicated energy consumption, braking capability, battery charger efficiency, and battery characteristics were determined for a modified utility van powered by sixteen 6-volt batteries connected in series. A chopper controller actuated by a foot accelerator pedal changes the voltage applied to the 22-kilowatt (30-hp) series-wound drive motor. In addition to the conventional hydraulic braking system, the vehicle has hydraulic regenerative braking. Cycle tests and acceleration tests were conducted with and without hydraulic regeneration.

Lumannick, S.↗

NASA NTRS1977

Baseline tests of the battronic Minivan electric delivery van

An electric passenger vehicle was tested to develop data characterizing the state of the art of electric and hybrid vehicles. The test measured vehicle maximum speed, range at constant speed, range over stop-and-go driving schedules, maximum acceleration, gradeability and limit, road energy consumption, road power, indicated energy consumption, braking capability and battery charge efficiency. The data obtained are to serve as a baseline to compare improvements in electric and hybrid vehicle technologies and to assist in establishing performance standards.

Dustin, M. O.↗

NASA NTRS1977

Baseline tests of the EPC Hummingbird electric passenger vehicle

The rear-mounted internal combustion engine in a four-passenger Volkswagen Thing was replaced with an electric motor made by modifying an aircraft generator and powered by 12 heavy-duty, lead-acid battery modules. Vehicle performance tests were conducted to measure vehicle maximum speed, range at constant speed, range over stop-and-go driving schedules, maximum acceleration, gradeability limit, road energy consumption, road power, indicated energy consumption, braking capability, battery charger efficiency, and battery characteristics. Test results are presented in tables and charts.

Slavik, R. J.↗

NASA NTRS1978

A cycle timer for testing electric vehicles

A cycle timer was developed to assist the driver of an electric vehicle in more accurately following and repeating SAE driving schedules. These schedules require operating an electric vehicle in a selected stop-and-go driving cycle and repeating this cycle pattern until the vehicle ceases to meet the requirements of the cycle. The heart of the system is a programmable read-only memory (PROM) that has the required test profiles permanently recorded on plug-in cards, one card for each different driving schedule. The PROM generates a direct current analog signal that drives a speedometer displayed on one scale of a dual movement meter. The second scale of the dual movement meter displays the actual speed of the vehicle as recorded by the fifth wheel. The vehicle operator controls vehicle speed to match the desired profile speed. The PROM controls the recycle start time as well as the buzzer activation. The cycle programmer is powered by the test vehicle's 12-volt accessory battery, through a 5-volt regulator and a 12-volt dc-to-dc converter.

Soltis, R. F.↗

NASA NTRS1983

Vehicle test report: South Coast Technology electric Volkswagen Rabbit with developmental low-power armature chopper

Dynamometer performance of a South Coast Technology electric conversion of a Volkswagen (VW) Rabbit designated SCT-8 was tested. The SCT-8 vehicle was fitted with a transistorized chopper in the motor armature circuit to supplement the standard motor speed control via field weakening. The armature chopper allowed speed control below the motor base speed. This low speed control was intended to reduce energy loss at idle during stop-and-go traffic; to eliminate the need for using the clutch below base motor speed; and to improve the drivability. Test results indicate an improvement of about 3.5% in battery energy economy for the SAE J227a-D driving cycle and 6% for the C-cycle with only a minor reduction in acceleration performance. A further reduction of about 6% would be possible if provision were made for shutting down field power during the idle phases of the driving cycles. Drivability of the vehicle equipped with the armature chopper was significantly improved compared with the standard SCT Electric Rabbit.

Marte, J. E.↗

NASA NTRS1984

In-trail dynamics of multiple CDTI-equipped aircraft queues

One of the potential problems of in-trail self-spacing with a Cockpit Display of Traffic Information (CDTI) is whether dynamic oscillations would occur in a queue of aircraft flying an approach, similar to the ""accordion'' effect seem with the queue of automobiles in stop-and-go traffic. In order to gain some insight into this potential problem, a brief experiment was conducted with the Transport Systems Research Vehicle (TSRV) ground-based simulator equipped with CDTI which presented the position of other aircraft in the area. Three simulation sessions were conducted wherein queues of up to nine aircraft were built, each one self-spacing on the preceding aircraft. The aircraft crews were rotated to ensure that the pilots had no prior knowledge of the lead aircraft behavior they would be following. Two different spacing criteria were employed: a constant time predictor criterion and a constant time delay criterion. The experiment failed to uncover any dynamic oscillatory tendencies in queues of seven to nine aircraft.

Kelly, J. R.↗

NASA NTRS1993

Firmware Development Improves System Efficiency

Most manufacturing processes require physical pointwise positioning of the components or tools from one location to another. Typical mechanical systems utilize either stop-and-go or fixed feed-rate procession to accomplish the task. The first approach achieves positional accuracy but prolongs overall time and increases wear on the mechanical system. The second approach sustains the throughput but compromises positional accuracy. A computer firmware approach has been developed to optimize this point wise mechanism by utilizing programmable interrupt controls to synchronize engineering processes 'on the fly'. This principle has been implemented in an eddy current imaging system to demonstrate the improvement. Software programs were developed that enable a mechanical controller card to transmit interrupts to a system controller as a trigger signal to initiate an eddy current data acquisition routine. The advantages are: (1) optimized manufacturing processes, (2) increased throughput of the system, (3) improved positional accuracy, and (4) reduced wear and tear on the mechanical system.

Chern, E. James↗

NASA NTRS2010

Fuel Consumption and Emissions from Airport Taxi Operations

Developed a method to calculate fuel consumption and emissions of phases of taxi operations. Results at DFW showed that up to 18% of fuel can be saved by eliminating stop-and-go situations. Developed an energy efficient and environmentally friendly surface concept: Spot and Runway Departure Advisory (SARDA) tool. The SARDA tool has been identified as a potential candidate for a technology transfer to the FAA.

Jung, Yoon↗

NASA NTRS2013

NextGen Flight Deck Surface Trajectory-Based Operations (STBO): Contingency Holds

The purpose of this pilot-in-the-loop taxi simulation was to investigate a NextGen Surface Trajectory-Based Operations (STBO) concept called "contingency holds." The contingency-hold concept parses a taxi route into segments, allowing an air traffic control (ATC) surface traffic management (STM) system to hold an aircraft when necessary for safety. Under nominal conditions, if the intersection or active runway crossing is clear, the hold is removed, allowing the aircraft to continue taxiing without slowing, thus improving taxi efficiency, while minimizing the excessive brake use, fuel burn, and emissions associated with stop-and-go taxi. However, when a potential traffic conflict exists, the hold remains in place as a fail-safe mechanism. In this departure operations simulation, the taxi clearance included a required time of arrival (RTA) to a specified intersection. The flight deck was equipped with speed-guidance avionics to aid the pilot in safely meeting the RTA. On two trials, the contingency hold was not released, and pilots were required to stop. On two trials the contingency hold was released 15 sec prior to the RTA, and on two trials the contingency hold was released 30 sec prior to the RTA. When the hold remained in place, all pilots complied with the hold. Results also showed that when the hold was released at 15-sec or 30-sec prior to the RTA, the 30-sec release allowed pilots to maintain nominal taxi speed, thus supporting continuous traffic flow; whereas, the 15-sec release did not. The contingency-hold concept, with at least a 30-sec release, allows pilots to improve taxiing efficiency by reducing braking, slowing, and stopping, but still maintains safety in that no pilots "busted" the clearance holds. Overall, the evidence suggests that the contingency-hold concept is a viable concept for optimizing efficiency while maintaining safety.

NextGen↗

DOE OSTI2024

"Traffic Control via Connected and Automated Vehicles: An Open-Road Field Experiment with 100 CAVs"

The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. These "phantom jams" or "stop-and-go waves,"are a significant source of wasted energy. Toward this goal, the CIRCLES project designed a control system referred to as the MegaController by the CIRCLES team, that could be deployed in real traffic. Our field experiment leveraged a heterogeneous fleet of 100 longitudinally-controlled vehicles as Lagrangian traffic actuators, each of which ran a controller with the architecture described in this paper. The MegaController is a hierarchical control architecture, which consists of two main layers. The upper layer is called Speed Planner, and is a centralized optimal control algorithm. It assigns speed targets to the vehicles, conveyed through the LTE cellular network. The lower layer is a control layer, running on each vehicle. It performs local actuation by overriding the stock adaptive cruise controller, using the stock on-board sensors. The Speed Planner ingests live data feeds provided by third parties, as well as data from our own control vehicles, and uses both to perform the speed assignment. The architecture of the speed planner allows for modular use of standard control techniques, such as optimal control, model predictive control, kernel methods and others, including Deep RL, model predictive control and explicit controllers. Depending on the vehicle architecture, all onboard sensing data can be accessed by the local controllers, or only some. Control inputs vary across different automakers, with inputs ranging from torque or acceleration requests for some cars, and electronic selection of ACC set points in others. The proposed architecture allows for the combination of all possible settings proposed above. Most configurations were tested throughout the ramp up to the MegaVandertest.

Lee, Jonathan↗

DOE OSTI2025

String instability mitigation of adaptive cruise control without modifying control laws: trajectory shaper and parameter estimation

Vehicle automation technologies equip vehicles with adaptive cruise control (ACC) systems, which relieve driving fatigue. However, recent studies have shown that the current ACC systems are string-unstable (i.e., exacerbate traffic congestion). To achieve string stability, most existing studies directly modify the control algorithms of ACC systems. Alternatively, this study proposes a trajectory shaper (TS)-based method, which only modifies the trajectory information of the predecessor vehicle, so that the ego vehicle driven by a string-unstable ACC system leverages the modified trajectory information to achieve string stability. To devise the TS-based method, an offline-online parameter estimation method integrating batch optimization and an extended Kalman filter is applied to estimate the parameters of an ACC system. The proposed TS-based method is cost-effective during implementation, as it avoids modifying existing ACC control algorithms (which entails a complex analysis of control systems and parameter tuning). In conclusion, the effectiveness of the proposed TS-based method is validated through extensive numerical experiments.

33 ADVANCED PROPULSION SYSTEMS↗