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Private enterprises, transit agencies, and governments around the world rely on NREL to evaluate their fleet electrification needs. From testing vehicle charging equipment in the lab, to working with fleets to adopt new types of vehicles, to helping agencies develop electric vehicle (EV) plans, NREL transportation analysts address a range of electrification challenges.
In 2022, the U.S. transportation sector was the largest source of greenhouse gas emissions in the country, with the combination of passenger and commercial vehicles contributing 80% of these emissions. As adoption of passenger electric vehicles continues to climb, sights are being set on the electrification of heavy-duty commercial vehicle (HDCV) fleets. The sustainability of these shifts relies in part on the addition of significant renewable energy generation resources to both bolster the grid in the face of increased demand, and to prevent a shift in the source of greenhouse gas (GHG) emissions to the grid, as opposed to a true net reduction. Additionally, it is necessary to quantify the variations in economic viability across the country for these technologies as it pertains to their productive capabilities. Doing so will encourage investment and ensure that the transition to electrified HDCV fleets is commercially viable, as well as sustainable. In an effort to meet these goals, multiple computational frameworks are used to locate suitable land for renewable infrastructure development, and to quantify spatiotemporal variations in the potential energy generation and financial viability of development sites across the Unites States. First, the Oak Ridge Siting Analysis for power Generation Expansion tool (OR-SAGE) is used to assess the suitability of land for potential wind and solar energy development across the contiguous U.S. From there, resource data from the National Solar Radiation Database (NSRDB) and the Wind Integration National Dataset (WIND) are used in concert with the National Renewable Energy Laboratory (NREL) Renewable Energy Potential (ReV) model to calculate the variation in potential generation capacity for each resource. Additionally, the capital and operational expenditures are calculated for an example configuration of each renewable technology. These measures are then used to calculate the levelized cost of energy (LCOE) of potential sites. All of these results are then processed and analyzed to determine where in the U.S. solar and wind energy are most viable. This viability is based on available generation potential, consistency and stability of energy generation over time, and economic viability with respect to LCOE.
Energy consumption impact of vehicle powertrain is traditionally assessed using standard and regulatory drive cycles. In this paper, energy consumption is determined by studying all the trips occurring in the Chicago Metropolitan Area in a 24 hour period. The data is generated by combining Polaris, an agent based traffic flow model with SVTrip, a stochastic model that generates 1 Hz vehicle speed profiles and Autonomie, a detailed vehicle level modeling tool used to predict fuel consumption. Different scenarios are constructed to define trips for different timeframes and assumptions. Vehicle fleet distribution, vehicle technologies and automation levels are applied to represent an appropriate vehicle fleet for each scenario. Analysis of energy consumption is then discussed with a particular emphasis on the impact of powertrain technology and vehicle types.
EEMS Program activities during FY 2020 focused on analytical research and large-scale modeling and simulation to understand the impacts that new mobility technologies and services will have at the vehicle-, traveler-, and overall transportation system-level. This research included the development of a multi-fidelity, end-to-end transportation system models and tools to evaluate the complex interactions among the various actors within the mobility landscape, analysis of empirical data to characterize which solutions may provide the largest benefits, and development of new control systems and algorithms that use vehicle connectivity and automation to improve the performance and efficiency of individual vehicles as well as the overall traffic system. This document presents a brief overview of the EEMS Program and documents progress and results from projects within each of the EEMS activity areas. The Computational Modeling and Simulation key activity area summarizes work within the sub-areas of (1) the SMART (Systems and Modeling for Accelerated Research in Transportation) Mobility Lab Consortium, (2) Artificial Intelligence, High-Performance Computing, and Data Analytics, and (3) Core Simulation and Evaluation Tools. Additionally, the program’s advanced R&D projects are summarized within (4) the Connectivity and Automation Technology key activity area. Each of the individual progress reports provide a project overview and highlights of the technical results.
Autonomous Vehicles (AVs) are revolutionizing transportation, but their reliance on interconnected cyber-physical systems exposes them to unprecedented cybersecurity risks. This study addresses the critical challenge of detecting real-time cyber intrusions in self-driving vehicles by leveraging a dataset from the Udacity self-driving car project. We simulate four high-impact attack vectors, Denial of Service (DoS), spoofing, replay, and fuzzy attacks, by injecting noise into spatial features (e.g., bounding box coordinates) to replicate adversarial scenarios. We develop and evaluate two lightweight neural network architectures (NN-1 and NN-2) alongside a logistic regression baseline (LG-1) for intrusion detection. The models achieve exceptional performance, with NN-2 attaining an AUC score of 93.15% and 93.15% accuracy, demonstrating their suitability for edge deployment in AV environments. Through explainable AI techniques, we uncover unique forensic fingerprints of each attack type, such as spatial corruption in fuzzy attacks and temporal anomalies in replay attacks, offering actionable insights for feature engineering and proactive defense. Visual analytics, including confusion matrices, ROC curves, and feature importance plots, validate the models' robustness and interpretability. This research sets a new benchmark for AV cybersecurity, delivering a scalable, field-ready toolkit for Original Equipment Manufacturers (OEMs) and policymakers. By aligning intrusion fingerprints with SAE J3061 automotive security standards, we provide a pathway for integrating machine learning into safety-critical AV systems. Our findings underscore the urgent need for security-by-design AI, ensuring that AVs not only drive autonomously but also defend autonomously. This work bridges the gap between theoretical cybersecurity and life-preserving engineering, offering a leap toward safer, more secure autonomous transportation.
This project advances the development and application of the TEMPO transportation energy systems model to support DOE analysis of evolving mobility futures. Recent work has focused on improving model transparency, performance, and fidelity through a redesigned software architecture and updated technology adoption scenarios aligned with the latest data and assumptions. Ongoing efforts aim to expand TEMPO's ability to assess transportation affordability and consumer decision-making, including vehicle ownership dynamics and distributional impacts across households. These enhancements position TEMPO to better inform policy and R&D decisions by providing more behaviorally realistic and policy-relevant insights into transportation energy use, technology adoption, and system-level outcomes.
Under the umbrella of EERE’s Office of Sustainable Transportation, the U.S. Department of Energy’s (DOE) Vehicle Technologies Office (VTO) and Hydrogen and Fuel Cell Technologies Office (HFTO) seek to develop sustainable, affordable, and efficient technologies for transportation of goods and people. Translating investments in advanced transportation component technologies and powertrains to estimate the potential for vehicle-level fuel savings is critical to understanding DOE’s impact and success in this mission For this study, Argonne National Laboratory (Argonne) simulated technologies funded by VTO and HFTO for light-duty vehicles across the following: Powertrain configurations (conventional, power-split hybrid electric vehicle, extended-range electric vehicle, battery electric drive, and fuel-cell vehicles); Vehicle classes (compact car, mid-size car, small sport utility vehicle [SUV], mid-size SUV, and pickup truck); Fuels (gasoline, diesel, natural gas, hydrogen, and battery electricity). We assessed each technology for five different timeframes: laboratory years 2015 (reference), 2020, 2025, 2030, and 2045. We assumed a delay of 5 years between laboratory year and model year (i.e., the year the technology is introduced into production). Finally, we included uncertainties for both technology performance and cost by considering two cases (note that these cases are not intended as predictions of future performance): Low case , aligned with DOE technology manager estimates of expected original equipment manufacturer (OEM) improvements based on business as usual regulatory and market environments; High case , aligned with aggressive technology advancements based on research and development (R&D) targets developed through support by VTO and HFTO. We estimated the energy and cost impact of different technologies using Autonomie (Argonne undated), a state-of-the-art vehicle system simulation tool developed by Argonne and used to assess the energy consumption, performance, and cost of multiple advanced vehicle technologies. The tool comprises a complete set of vehicle models to assess impacts across a wide range of classes (from light- to heavy-duty), powertrain configurations (from conventional to hybrid electric vehicles [HEVs], fuel cell electric vehicles [FCEVs], plug-in hybrid electric vehicles [PHEVs], and battery electric vehicles [BEVs]), components, and control strategies, including vehicle-level and component-level controls developed and calibrated using dynamometer test data. Autonomie has been used to support a wide range of studies: analyzing various component technologies, sizing powertrain components to meet different vehicle requirements, comparing the benefits of powertrain configurations, optimizing both heuristic and route-based vehicle energy control, and predicting transportation energy use when paired with a traffic modeling tool such as POLARIS. This report documents the assumptions made and the vehicle-level energy consumption benefits and associated technology costs estimated for various types of light-duty vehicles. Details regarding vehicle assumptions and simulation results are available in the spreadsheets accompanying this report.
The transition to electric road transport technologies requires electric traction drive systems to offer improved performances and capabilities, such as fuel efficiency (in terms of MPGe, i.e., miles per gallon of gasoline-equivalent), extended range, and fast-charging options. The enhanced electrification and transformed mobility are translating to a demand for higher power and more efficient electric traction drive systems that lead to better fuel economy for a given battery charge. To accelerate the mass-market adoption of electrified transportation, the U.S. Department of Energy (DOE), in collaboration with the automotive industry, has announced the technical targets for light-duty electric vehicles (EVs) for 2025. This article discusses the electric drive technology trends for passenger electric and hybrid EVs with commercially available solutions in terms of materials, electric machine and inverter designs, maximum speed, component cooling, power density, and performance. Additionally, the emerging materials and technologies for power electronics and electric motors are presented, identifying the challenges and opportunities for even more aggressive designs to meet the need for next-generation EVs. Some innovative drive and motor designs with the potential to meet the DOE 2025 targets are also discussed.
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.
This project aimed to enhance the range and reduce the operating costs of battery electric Class 8 trucks traveling over 250 miles daily. This was achieved through the development and implementation of an intelligent-Energy Management System (i-EMS) that leverages vehicle and operations data, physics-aware machine learning algorithms, and vehicle-to-cloud (V2C) connectivity. The project hypothesized that advanced machine learning algorithms and real-time data analytics could significantly improve the energy efficiency and range of these trucks. Key objectives included developing a physics-aware machine learning algorithm, implementing an i-EMS with V2C connectivity and physics-aware spatial data analytics (PSDA), and validating the system’s effectiveness with fleet partners HEB Companies and Murphy Logistics. Extensive data collection from vehicle operations, including vehicle characteristics, road conditions, and payload, was conducted. A machine learning algorithm was developed to predict energy consumption and enable proactive decision-making. The i-EMS was implemented on two Volvo VNR BEVs, with operators receiving charging and routing recommendations. Charging stations were installed at depot locations in Texas and Minnesota, with an additional on-route charger in Minnesota. Significant findings included a 14% range improvement for Murphy Logistics on a highway-driving eco-route and a 22% range improvement for HEB Companies on a city-driving eco-route. The i-EMS utilized rule-based methods and physics-based algorithms to predict and reduce energy consumption, with real-time monitoring and analysis through V2C connectivity enabling proactive decision-making. The project demonstrated the feasibility and economic viability of battery electric Class 8 trucks for long-haul operations, showcasing the potential of physics-aware machine learning in optimizing energy management. The successful implementation of the i-EMS in real-world scenarios validates its practical application and effectiveness, paving the way for the widespread adoption of battery electric vehicles in the freight transportation industry.
Battery electric vehicles (BEVs) are widely considered a pathway to achieve low carbon mobility. BEVs emit zero emissions from the tailpipe, but their life cycle carbon reduction compared to gasoline vehicles varies based on primary energy sources, electricity generation, and use efficiency. The Middle East and North Africa (MENA) region is an area rich in fossil fuels, meriting a detailed comparison between the emissions from BEV and other powertrains. We developed a MENA‐specific life cycle model that estimates well‐to‐wheel (WTW) greenhouse gas (GHG) emissions from passenger transport with internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), plug‐in hybrid electric vehicles, and BEVs. MENA's average WTW GHG emissions for all supply chain steps including combustion emissions from vehicle operation are 767 g/kWh and 84 g CO 2 eq/MJ for electricity and gasoline, respectively, but are highly variable due to heterogeneity in upstream supply chains. The use of hybrid gasoline ICEVs provides the largest emission reduction opportunity for existing vehicle fleets in 9 of the 16 MENA countries. For these nine countries, replacing gasoline ICEVs with HEVs could, on average, reduce country‐level life cycle GHG emissions by 47%. There is a similar emission reduction opportunity for 14 of the 16 MENA countries when normalizing vehicle efficiencies irrespective of the powertrain shares and other trends in existing vehicle fleets. Future scenario analysis shows that BEVs would have the lowest WTW GHG emissions among all powertrains in most MENA countries only if significantly reduced electricity transmission losses and cleaner grid mix are realized, although a high cost of infrastructure developments is expected.
The 2011 Tolling Impact Survey measured the impact of tolling on travel behavior in Seattle, Washington. The Volpe Center conducted the survey on behalf of the U.S. Department of Transportation and the Urban Partnership Agreement. The population surveyed included drivers, public transportation users, carpoolers, and vanpoolers using the SR-520 corridor in Seattle. The survey was conducted in two phases—before and after toll implementation—to evaluate related attitude changes. The survey assessed route changes, trip timing, trip purpose, and travel mode (i.e., single-occupancy vehicle, carpool, or public transportation).
Connected and Automated Vehicles (CAVs) can be considered to be a disruptive transportation technology, with the potential to significantly improve overall transportation system efficiency; however, CAVs may increase induce vehicle miles traveled (VMT) and bring on greater energy consumption. Further, shared mobility is another disruptive transportation event that is reshaping our travel patterns. The primary goal of this project was to extensively collect data from vehicles and associated infrastructure equipped with CAV technologies from both real-world experiments and simulation studies mainly deployed in California, and develop a comprehensive framework for evaluating energy efficiency opportunities from large-scale (e.g., statewide) introduction of CAVs and a wide deployment of shared mobility systems in a variety of scenarios. To quantify the combined impact of CAV and shared mobility on travel behavior, traffic performance, and energy efficiency, a unique mesoscopic simulation-based model was developed for mobility and energy efficiency evaluation considering these disruptive transportation technologies. As a complement to existing studies on nationwide evaluation of CAVs’ energy impacts, this project was focused on data collection efforts and CAV applications under congested traffic environments that are frequently experienced on a massive scale across the major metropolitan areas in California. Extensive real-world data collection supplemented with simulation studies were conducted to cover a variety of CAV and shared mobility scenarios, particularly on scenarios less-explored in the existing research. Another key component of this project was to consider the interaction between different CAV technologies and shared mobility models, and the compound effect on energy efficiency. A comprehensive modeling suite was developed to quantify the impact of new mobility technologies on travel behavior and traffic performance. The developed modeling framework includes an energy intensity module, mode choice module and activity generation module that are integrated into an agent-based BEAM simulation platform to perform impact analysis based on a variety of scenarios. In addition, the RouteE model has been upgraded to incorporate the impact of CAVs on traffic flow, VMT and energy intensity, using micro-simulation data collected from both freeways and urban arterials. A novel fundamental influencing factor (FIF) mode choice model was developed to link CAV and shared mobility components with travel behaviors, and adapted into the BEAM-centered model framework. A statewide energy inventory was constructed under various CAV technology deployment scenarios by incorporating datasets and models for predicting vehicle market share and vehicle usage, which are tightly associated with the penetration of shared mobility systems. Based applying this modeling suite to a calibrated network in Riverside California, it was found that cooperative automated driving in general will improve mobility, but automated vehicles, even when deployed in a shared autonomous fleet, will likely bring an increase of VMT (up to 36%) due to mode shifts and deadheading. Ride-hailing vehicles typically have better energy efficiency and a higher share of electric vehicles, which helps offset the negative impact from VMT increases when estimating the system-level energy consumption. In general, simulation results show a 6% increase in energy consumption for the scenarios with an increasing shift to ride-hailing modes. The statewide analysis based on the National Household Travel Survey (NHTS) sample data is consistent with the findings from the Riverside network and validate the developed clustering-prediction modeling methodology. The outcomes from this project will help close the knowledge gap on recognizing the potential performance and energy impacts of a broad deployment of CAV and shared mobility technologies across a wide range of roadway infrastructure with varying levels of congestion. Results from this project: 1) will support policymakers in steering CAV development and deployment towards an energy favorable direction; 2) reduce uncertainties in estimating energy saving opportunities from new mobility technologies and services; 3) increase the confidence of CAV technology investors both on the infrastructure side (i.e., transportation agencies) and on the vehicle side (i.e., OEMs); and 4) expedite the deployment of energy-efficient CAV and shared mobility applications.
The 2011 Tolling Impact Survey measured the impact of tolling on travel behavior in Atlanta, Georgia. The Volpe Center conducted the survey on behalf of the U.S. Department of Transportation and the Urban Partnership Agreement. The population surveyed included drivers, public transportation users, carpoolers, and vanpoolers using the I-85 corridor in Atlanta, Georgia. The survey was conducted in two phases—before and after toll implementation—to evaluate related attitude changes. The survey assessed route changes, trip timing, trip purpose, and travel mode (i.e., single-occupancy vehicle, carpool, or public transportation).
We estimate the effects of transportation network companies (TNCs) Uber and Lyft on vehicle ownership, fleet average fuel economy, and transit use in U.S. urban areas using a set of difference-in-difference propensity score-weighted regression models that exploit staggered market entry across the U.S. from 2011 to 2017. We find evidence that TNC entry into urban areas causes an average 0.7% increase in vehicle registrations with significant heterogeneity in these effects across urban areas: TNC entry produces larger vehicle ownership increases in urban areas with higher initial ownership (car-dependent cities) and in urban areas with lower population growth (where TNC-induced vehicle adoption outpaces population growth). We also find no statistically significant average effect of TNC entry on fuel economy or transit use but find evidence of heterogeneity in these effects across urban areas, including larger transit ridership reductions after TNC entry in areas with higher income and more childless households.
This report summarizes the implementation, outcomes, and key learnings of Expanding Access to Charging at Homes (EACH), a nationwide multifamily housing charging initiative focused on accelerating deployment of electric vehicle charging infrastructure at multifamily housing properties. The program combined education, outreach, and technical assistance to support property owners, managers, residents, and industry stakeholders in overcoming barriers to EV charging adoption. The report highlights program strategies, stakeholder engagement, resources developed, and recommendations to help scale charging access at multifamily housing nationwide.
Millions of vehicles are on the road with RADAR sensors in use for adaptive cruise control (ACC), and RADAR sen- sors are not tracking all of the objects in the field of view. This work shows a work-in-progress tool to improve track- ing from RADAR and controller area network (CAN) which should be vitally useful for safety of transportation systems and automated vehicle development. The CAN data provides object detections, but there is a lingering data association problem. The contribution of this work in progress is the solution to the data association problem by posing the data association as a minimum cost network flow problem, and doing it at low cost with an eye toward scalable CPS research.