Search NASA⌕ Search

SEARCH · Search NASA

Results for “driving behavior”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Quantitative Evaluation of MD/HD Vehicle Electrification using Statistical Data

This paper presents a wide-ranging analysis of Class 3-8 commercial vehicle electrification by means of developing a framework tool which uses a quantitative method of estimating electric vehicle energy consumption and appropriate charging considerations. The Fleet DNA composite statistics on real-world driving behavior is used to evaluate feasible or market-ready battery electric vehicle (BEV) technologies in medium- and heavy-duty (MD/HD) applications. In the paper, ten representative Class 3-8 commercial vehicle electrifications have been evaluated as a function of various service coverages, including applications in port drayage tractors, refuse trucks, delivery trucks, buses, and bucket trucks. The results indicate significant energy savings and fuel cost savings across all MD/HD vehicle electrifications. The majority of MD BEVs, with the exception of Class 3 bucket trucks, achieve better than a 5-year payback with 50–75% service coverage. For HD BEVs, with the exception of the Class 8 port drayage tractors, the 90% service coverage results in a 10-year or longer payback time, while the 50% service coverage yields a 7–8 year payback. Class 8 port drayage tractors should achieve payback in no more than a 3.5 years with 50–75% service coverage. Thus, the analysis indicates a highly feasible potential for Class 3-6 MD vehicles to be electrified, and feasible opportunities for electrification in Class 7-8 HD short-distance applications.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Data-Driven Forgetting and Discount Factors for Vehicle Speed Forecasting in Ecological Adaptive Cruise Control

Abstract This paper investigates temporal correlations in human driving behavior using real-world driving to improve speed forecasting accuracy. These correlations can point to a measurement weighting function with two parameters: a forgetting factor for past speed measurements that the vehicle itself drove with, and a discount factor for the speeds of vehicles ahead based on information from vehicle-to-vehicle communication. The developed weighting approach is applied to a vehicle speed predictor using polynomial regression, a prediction method well-known in the literature. The performance of the developed approach is then assessed in both real-world and simulated traffic scenarios for accuracy and robustness. The new weighting method is applied to an ecological adaptive cruise control system, and its influence is analyzed on the prediction accuracy and the performance of the ecological adaptive cruise control in an electric vehicle powertrain model. The results show that the new prediction method improves energy saving from the eco-driving by up to 4.7% compared to a baseline least-square-based polynomial regression. This is a 10% improvement over the constant speed/acceleration model, a conventional speed predictor.

Automation & Control Systems↗
DOE OSTI2019

On-Road Fuel Cell Electric Vehicles Evaluation: Overview

This report presents an overview of an evaluation of on-road fuel cell electric vehicles (FCEVs) by the National Renewable Energy Laboratory (NREL). The project addressed the need for current, on-road FCEV data and sought to validate improved performance and longer durability from comprehensive sets of early FCEVs, including early market vehicles. This report provides an overview of the evaluation project and partners, describes NREL's evaluation approach, and presents a summary of the results. Detailed results for durability, fuel economy, deployment and driving behavior, and specifications are published in separate reports.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Whole nervous system expression of glutamate receptors reveals distinct receptor roles in sensorimotor circuits

A goal of connectomics is to reveal the links between neural circuits and behavior. Larvae of the primitive chordateCionaare well-suited to make contributions in this area. In addition to having a described connectome,Cionalarvae have a range of readily-quantified behaviors. Moreover, the small number of neurons in the larval CNS (∼180) holds the promise of a comprehensive characterization of individual neurons. We present single-neuron predictions for glutamate receptor (GlutR) expression based onin situhybridization. Included are both ionotropic receptors (AMPA, NMDA, and Kainate), and metabotropic receptors. The predicted glutamate receptor expression dataset is discussed in the context of known circuits driving behaviors such as phototaxis, mechanosensation, and looming shadow response. The predicted expression of AMPA and NMDA receptors may help to resolve issues regarding the co-production of GABA and glutamate by a subset of photoreceptors. The targets of these photoreceptors in the midbrain appear to express NMDA receptors, but not AMPA receptors. This is in agreement with previous results indicating that GABA is the primary neurotransmitter from the photoreceptors evoking a swimming response through a disinhibition mechanism, and that glutamate may, therefore, have only a modulatory action in this circuit. Other findings reported here are more unexpected. For example, many of the targets of glutamatergic epidermal sensory neurons (ESNs) do not express any of the ionotropic receptors, yet the ESNs themselves express metabotropic receptors. Thus, we speculate that their production of glutamate may be for communication with neighboring ESNs, rather than to their interneuron targets. Significance StatementSimple invertebrates offer a tractable alternative to complex vertebrate brains, facilitating holistic understanding of brain function. One such invertebrate is the marine chordateCiona, which has the benefit of a complete synaptic wiring diagram for its swimming larva. This “connectome” allowed identification of putative neural circuits driving defined behaviors. Fuller understanding of neural circuits, however, requires a description of the attributes of individual neurons. This study focuses on the excitatory neurotransmitter glutamate, which signals via a complex set of both ionotropic and metabotropic receptors. Here, we present a nervous system-wide prediction of GlutR expression inCionaat the individual neuron level, considered in the context of neural circuits, with emphasis on how GlutR expression accounts for function of neural circuits.

Neurosciences & Neurology↗
DOE OSTI2026

Automated Vehicle Feasibility Study

This study collected automated vehicle (AV) performance data on public roadways in Athens, Ohio. The route for the study contained a combination of roads with different functional classifications, conditions, annual average daily traffic, and ownership responsibilities for maintenance and repair. Preparation for the public road deployment was done in a controlled environment at Transportation Research Center’s SMARTCenter, a dedicated AV test facility in East Liberty, Ohio. Researchers analyzed data and extracted insights relevant for both AV developers and infrastructure owners and operators. The study found that rural environments offer a unique set of roadway features such as hills and curves, which can challenge the driving behavior of an AV. Rural regions can also contain a large number of low-traffic gravel roads that lack pavement markings, which appear to be a crucial infrastructure element for operation of current generation AVs. Similarly, the presence of well-maintained lane lines along curves can influence the AV’s roadway departure tendencies. The study found that curvature-related behavior of an AV is also influenced by driving speed on the roadway segment. Such findings were consistent regardless of the time of day along the route or season of data collection. However, commentary about AV performance in active adverse weather cannot be made, as this is still an area of active research.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Exploring Microsimulation Process for Energy Impact Evaluation of Connected and Automated Vehicles

In this paper, the authors present a microsimulation-based methodological approach for evaluating the energy impact of connected and automated vehicles (CAVs). They use an open-source micro-simulator, SUMO, and provide a way to set up a simulation environment that emulates real-world traffic dynamics. They also employ the Intelligent Driver Model to represent human drivers and calibrate its driving behavior using real-world traffic data and driving statistics. The authors conduct extensive simulation studies considering different penetration rates of CAVs, different car-following models, and varying car-following model parameters. Using the state-of-the-art Future Automotive System Technology Simulator (FASTSim), they estimate the fuel economy of each vehicle and analyze the energy impact of the given CAV implementation. Finally, the authors analyze the possible factors affecting the simulation results, and also discuss limitations and future work.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2017

Name that tune: Mitigation of driver fatigue via a song naming game

Fatigued driving contributes to a substantial number of motor vehicle accidents each year. Music listening is often employed as a countermeasure during driving in order to mitigate the effects of fatigue. And though music listening has been established as a distractor in the sense that it increases cognitive load during driving, it is possible that increased cognitive load is desirable under particular circumstances. For instance, during situations that typically result in cognitive underload, such as driving in a low-traffic monotonous stretch of highway, it may be beneficial for cognitive load to increase, thereby necessitating allocation of greater cognitive resources to the task of driving and attenuating fatigue. Here, we employed a song-naming game as a countermeasure to fatigued driving in a simulated monotonous environment. During the first driving session, we established that driving performance deteriorates in the absence of an intervention following 30 min of simulated driving. During the second session, we found that a song-naming game employed at the point of fatigue onset was an effective countermeasure, as reflected by simulated driving performance that met or exceeded fresh driving behavior and was significantly better relative to fatigued performance during the first driving session.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

The Development of Vocational Vehicle Drive Cycles and Segmentation

Under a collaborative interagency agreement between the U.S. Environmental Protection Agency and the U.S Department of Energy (DOE), the National Renewable Energy Laboratory (NREL) performed a series of in-depth analyses to characterize the on-road driving behavior including distributions of vehicle speed, idle time, accelerations and decelerations, and other driving metrics of medium- and heavy-duty vocational vehicles operating within the United States. As part of this effort, NREL researchers segmented U.S. medium- and heavy-duty vocational vehicle driving characteristics into three distinct operating groups or clusters using real world drive cycle data collected at 1 Hz and stored in NREL's Fleet DNA database. The Fleet DNA database contains millions of miles of historical real-world drive cycle data captured from medium- and heavy vehicles operating across the United States. The data encompass data from existing DOE activities as well as contributions from valued industry stakeholder participants. For this project, data captured from 913 unique vehicles comprising 16,250 days of operation were drawn from the Fleet DNA database and examined. The Fleet DNA data used as a source for this analysis has been collected from a total of 30 unique fleets/data providers operating across 22 unique geographic locations spread across the United States. This includes locations with topology ranging from the foothills of Denver, Colorado, to the flats of Miami, Florida. The range of fleets, geographic locations, and total number of vehicles analyzed ensures results that include the influence of these factors. While no analysis will be perfect without unlimited resources and data, it is the researchers understanding that the Fleet DNA database is the largest and most thorough publicly accessible vocational vehicle usage database currently in operation. This report includes an introduction to the Fleet DNA database and the data contained within, a presentation of the results of the statistical analysis performed by NREL, review of the logistic model developed to predict cluster membership, and a discussion and detailed summary of the development of the vocational drive cycle weights and representative transient drive cycles for testing and simulation. Additional discussion of known limitations and potential future work are also included in the report content.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Leveraging Big Data Analysis Techniques for U.S. Vocational Vehicle Drive Cycle Characterization, Segmentation, and Development

Under a collaborative interagency agreement between the U.S. Environmental Protection Agency and the U.S. Department of Energy (DOE), the National Renewable Energy Laboratory (NREL) performed a series of in-depth analyses to characterize on-road driving behavior including distributions of vehicle speed, idle time, accelerations and decelerations, and other driving metrics of medium- and heavy-duty vocational vehicles operating within the United States. As part of this effort, NREL researchers segmented U.S. medium- and heavy-duty vocational vehicle driving characteristics into three distinct operating groups or clusters using real-world drive cycle data collected at 1 Hz and stored in NREL's Fleet DNA database. The Fleet DNA database contains millions of miles of historical drive cycle data captured from medium- and heavy-duty vehicles operating across the United States. The data encompass existing DOE activities as well as contributions from valued industry stakeholder participants. For this project, data captured from 913 unique vehicles comprising 16,250 days of operation were drawn from the Fleet DNA database and examined. The Fleet DNA data used as a source for this analysis has been collected from a total of 30 unique fleets/data providers operating across 22 unique geographic locations spread across the United States. This includes locations with topographies ranging from the foothills of Denver, Colorado, to the flats of Miami, Florida. This paper includes the results of the statistical analysis performed by NREL and a discussion and detailed summary of the development of the vocational drive cycle weights and representative transient drive cycles for testing and simulation. Additional discussion of known limitations and potential future work is also included.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2015

What Were the Driving and Charging Behaviors of High Mileage Accumulators?

During The EV Project, data was collected from approximately 8,000 Nissan Leaf and Chevrolet Volts that together accumulated over 124 million miles while generating data. There were significant differences in the technologies employed by the two primary vehicles in The EV Project; Nissan Leaf, and Chevrolet Volt. Because the Volt has an on-board range extending internal combustion engine (ICE) to recharge the battery while driving (extended range mode), the vehicle could be driven much further between charges. The Leaf’s range is limited by the battery capacity, but unlike the Volt can be “fast-charged” providing an opportunity to extend its daily travel distance. Many Plug-In Electric Vehicle (PEV) drivers in The EV Project accumulated miles at rates similar to what one would expect from an ICE vehicle. This paper examines some of the driving and charging characteristics of those PEV drivers who accumulated the most miles on their vehicles.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

A Human-Machine Shared Control Framework Considering Time-Varying Driver Characteristics

The uncertainties of driver's behavior seriously affect road safety and bring significant challenges to the human-machine cooperative control. Here, this paper proposes a human-machine shared control framework considering driver's time-varying characteristics to improve the co-driving cooperation performance. Firstly, the driving intention is introduced to describe the driver's involvement level through using Gauss-Bernoulli restricted Boltzmann machine method. And the index of driving ability is proposed to evaluate driver skills based on path-tracking errors. Then, a novel human-machine authority allocation strategy is designed by combining the two driving behavior characteristics and used to construct the driver-vehicle interaction system. Subsequently, a T-S fuzzy robust state-feedback shared control system is developed considering time-varying driver behaviors and vehicle states. Finally, the proposed shared steering system is validated by the driver-in-the-loop test bench. The results show that the proposed control method can reduce human-machine conflicts and has obvious superiority in improving performance of driving comfort, path tracking, and vehicle stability for the co-driving vehicles.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Safety evaluation of connected and automated vehicles in mixed traffic with conventional vehicles at intersections

Connected and Automated Vehicles (CAVs) can potentially improve the performance of the transportation system by reducing human errors. This paper investigates the safety impact of CAVs in a mixed traffic with conventional vehicles at intersections. Analyzing real-world AV crashes in California revealed that rear-end crashes at intersections are the dominant crash type. Therefore, to enhance our understanding of the future interactions between human-driven vehicles with CAVs at intersections, a simulation framework was developed to model the mixed traffic environment of Automated Vehicles (AV), cooperative AVs, and conventional human-driven vehicles. In order to model AVs driving behavior, Adaptive Cruise Control (ACC) and cooperative ACC (CACC) models are utilized. Particularly, this study explores system improvements due to automation and connectivity across varying CAV market penetration scenarios. ACC and CACC car following models are used to mimic the behavior of AVs and cooperative AVs. Real-world connected vehicle data are utilized to modify and tune the acceleration/deceleration regimes of the Wiedemann model. Next, the driving volatility concept capturing variability in vehicle speeds was utilized to calibrate the simulation to represent the safety performance of a real-world environment. Two surrogate safety measures are used to evaluate the safety performance of a representative intersection under different market penetration rate of CAVs: the number of longitudinal conflicts and driving volatility. At low levels of ACC market penetration, the safety improvements were found to be marginal, but safety improved substantially with more than 40% ACC penetration. Additional safety improvements can be achieved more quickly through the addition of cooperation and connectivity through CACC. Furthermore, ACC/CACC vehicles were found to improve mobility performance in terms of average speed and travel time at intersections.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Fuel Cell Electric Vehicle Driving and Fueling Behavior

The objectives of this project are to validate hydrogen fuel cell electric vehicles in real-world settings and to identify the current status and evolution of the technology. The analysis objectively assesses progress toward targets and market needs defined by the U.S. Department of Energy and stakeholders, provides feedback to hydrogen research and development, and publishes results for key stakeholder use and investment decisions. Fiscal year 2018 objectives focused on analysis and reporting of fuel cell electric vehicle driving range, fuel economy, drive and fill behaviors, durability, fill performance, and fuel cell performance. This report specifically addresses the topics of driving range, fuel economy, drive and fill behaviors, and fill performance.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Automated vehicle microscopic energy consumption study (AV-Micro): Data collection and model development

While the Adaptive Cruise Control (ACC) system in automated vehicles (AVs) is expected to impact transportation energy significantly, existing AV energy consumption models only directly adopt those developed with Human-driven Vehicle (HV) data without even slight adaptation or calibration to accommodate unique AV energy consumption features. This study will investigate how accurately HV data-based models can predict the energy consumption of AVs. Empirical trajectory data and corresponding instantaneous energy consumption rates from both AVs and HVs were collected. We adopted two classical HV data-based models to fit these data. The calibration results indicated that these models yield around 20 30% prediction errors for AVs. To further improve the prediction accuracy, this study designed an AV-Micro model by incorporating components of multiple classic energy consumption models that better capture ACC energy consumption features, including piecewise driving behavior. With this, the AV-Micro model achieves lower than 10% prediction errors. The AV-Micro model’s high consistency across different test runs was verified with statistical significance tests, demonstrating its adaptability in different driving profiles. To confirm the discrepancies between the energy consumption features of AVs and HVs, more statistical significance tests were conducted to show that the AV-Micro model cannot be directly applied to HV data. The findings by calibrated AV-Micro models revealed that AVs consume approximately 80.5–146.4 J more energy than HVs for each meter traveled. Furthermore, the frequency analysis of energy consumption indicates that there is still some room for AVs to improve energy efficiency, particularly given their larger amplitude high-frequency fluctuations.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Medium- and Heavy-Duty Truck Duty Cycles

This dataset provides second-by-second duty cycle data for Class 6 and Class 8 diesel trucks in Texas, including key vehicle metrics, engine-related data, and GPS data (excluding GPS latitude and longitude to ensure confidentiality). The data were collected via tablets installed on the trucks and organized into daily datasets, each associated with a unique vehicle ID and date. There are 12 daily datasets for Class 6 diesel trucks (three unique vehicle IDs) and 43 daily datasets for Class 8 diesel trucks (six unique vehicle IDs). The units associated with each column are included in the name. The engine performance data include columns such as engine speed, engine percent torque, and engine fuel rate. Road grade (%/100) was estimated using the GPS altitude and wheel-based vehicle speed, which is used as an input for FASTSim. Cumulative distance was also calculated using the wheel-based vehicle speed. Additional columns include: - Engine Speed (RPM): Removed inaccurate readings and used to calculate angular velocity (radians/second). - Torque (N·m): Calculated using engine percent torque, nominal friction percent torque, and engine reference torque values (those columns were removed from dataset), then normalized to express as torque (%). - Flywheel Power (%): Calculated using the angular velocity and torque (in kW), then normalized as a percentage of the maximum value. - Engine Fuel Rate (%) and Torque (%): Both metrics were normalized by dividing by their respective maximum values within each dataset to express them as percentages. The datasets were analyzed to assess the energy impact of various driving behaviors, simulate energy efficiency, and recommend optimal routes for diesel trucks using NLR’s tool called RouteE. For driver coaching, factors like speed and acceleration limits were considered, and idle periods were reduced (assuming the engine was off during idling) to adjust each drive cycle. These adjusted drive cycles were then simulated in FASTSim to evaluate their effect on fleet energy consumption and estimate potential energy savings. The original cycles are available for download on this page ![image](CoVaR_Image_for_Data_Page_Kenworth_Truck.jpg)

1Hz↗