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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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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Cold Weather Impacts on Electric School Bus Performance in Aurora, Colorado

This brief highlight details the key takeaways from a project that utilized NLR's Fleet Research, Energy Data, and Insights (FleetREDI) data analysis pipeline related to electric school bus (ESB) operation. ESBs using battery energy as their primary heating source have a higher energy consumption rate in cold weather, which fleet managers can account for when planning ESB purchases and making dispatching and charging decisions. Researchers found that electric school buses operate 2-5 times more efficiently than conventional buses, on average. Cold weather can double electric school bus energy demands, but strategies such as thermal pre-conditioning significantly reduce this effect. Understanding these impacts can help fleets plan charging, dispatching, and purchase decisions.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Scalable GPS Data Logging To Support Advanced Fleet Analysis

This highlight details the key takeaways from a project that utilized NLR's Fleet Research, Energy Data, and Insights (FleetREDI) data analysis pipeline. National Laboratory of the Rockies researchers developed and demonstrated low-cost, open-source Arduino data loggers with 3D-printed cases that are compatible with global navigational systems and built with components available ubiquitously worldwide, enabling cost-effective collection and analysis of fleet operational data. Validated on an overseas transit bus fleet, NLR analysis showed that, with sufficient charging opportunities, 90% of observed duty cycles could be accomplished by electric buses with no modifications to operations.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Agent-Based, Bottom-Up Medium- and Heavy-duty Electric Vehicle Economics, Operation, Charging and Adoption (Research Performance Final Report)

This is the research performance final report for the project entitled: Agent-Based, Bottom-Up Medium- and Heavy-duty Electric Vehicle Economics, Operation, Charging and Adoption This project was able to achieve the DOE’s goals of developing new modeling tools to understand MDHD vehicle operation and adoption. The first modeling tool is a fleet-level techno-economic analysis model capable of estimating energy use and associated environmental and cost impacts for electrified and conventional vehicles of any MDHD vocation, using real-world cost and operations data, including approaches to optimizing schedules for charging and/or vehicle dispatch. The second modeling tool is a system-level, bottom-up, agent-based adoption model capable of generating geographically-resolved estimates of market projections for MDHD vehicles and charging infrastructure. These tools will be developed and published to serve dual purposes as analysis tools for researchers, and decision-support tools for decision makers within the MDHD system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Launch Alaska Transportation and Energy Accelerator (LATEA)

The Launch Alaska Transportation and Energy Accelerator (LATEA), funded through the U.S. Department of Energy Office of Technology Commercialization’s Energy Program for Innovation Clusters (EPIC),advanced deployment of innovative and efficient transportation and energy technology in Alaska from October 2021 through June 2025. The project was designed to leverage Launch Alaska’s accelerator model to identify, recruit, and support transportation technology companies with novel solutions to market needs while building the stakeholder networks, demonstration opportunities, and institutional capacity necessary to accelerate commercialization in one of the most challenging operating environments in the United States.

08 HYDROGEN↗
DOE OSTI2026

Natural Language Processing to Inform Agent-Based Modeling: With Application to Modeling Adoption of Medium-Duty Electric Vehicles

Agent-based socio-technical modeling of medium- and heavy-duty (MDHD) electric vehicle (EV) adoption has the potential to provide analysis, prediction, and gui. This paper describes new applications of text analysis developed through machine learning (ML) to build and understand relevant topics and their saliency in the published discourse on adoption of MDHD EVs. This work contributes to the state of the art in topic mining models by defining a new metric of topic ranking (START) that quantifies the importance of predefined topics within the corpus using weighted results for predefined topics from two topic modeling approaches: Latent Dirichlet Allocation (LDA) and BERTopic. The START metric is then demonstrated in practice to model how academia and industry view the EV adoption process based on the respective texts published by these groups. Results show that academic literature places more emphasis on categories of interests such as norms/attitudes and adopter knowledge, while trade journals tend to emphasize long-term cost more than academia. The two bodies of literature agree on the importance of policy and incentives in MDHD EV adoption. Together these results illustrate the potential to use ML-based text analysis to populate the characteristics of agent-based socio-technical models.

Electric vehicle adoption, fleet electrification, ↗
DOE OSTI2024

Electrification Analysis: Manhattan Beer

This one-page highlight details the key takeaways from a project that utilized NREL's Fleet Research, Energy Data, and Insights (FleetREDI) data analysis pipeline, the Manhattan Beer Electrification Project. This project determined that Class-8 beverage distribution trucks operating in Manhattan show substantial electrification potential due to daily driving distances below 50 miles and low average speeds of 22mph or less. Their duty cycle needs can often be met by even modestly sized batteries and charging infrastructure. Vulnerable communities near their routes would benefit from fleet electrification.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Hourly Load Profile Dataset for Federal, State, and Municipal Electric Vehicle Fleets in the United States

The electrification of U.S. federal, state, and municipal fleets is accelerating rapidly, driven by an increased availability of competitive electric vehicle (EV) options and supportive policies and targets. The dataset described in this report, accessible at data.nrel.gov/submissions/280, provides a critical foundation for identifying fleet electricity demand, projecting these future demands, and developing actionable strategies to support the widespread electrification of government fleets. The dataset incorporates available fleet data, including 54% of federal agency vehicles approved for analysis (notably, the U.S. Postal Service is absent). Additionally, it includes data from 50,000 state government vehicles and 94,000 local government vehicles. While this represents a small fraction of the 4.4 million vehicles owned by state and local governments reported by the Federal Highway Administration (2022), the framework supports future expansion as more fleet inventory data become available.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Electrification Analysis: Container Ports' Cargo Handling Equipment

This one-page highlight details the key takeaways from a project that utilized NREL's Fleet Research, Energy Data, and Insights (FleetREDI) data analysis pipeline, the Electrification Analysis of Container Ports' Cargo Handling Equipment project. This project created a scalable solution to model energy demand per shipping container moved (kWh/TEU) for an all-electric cargo handling equipment fleet located at a maritime port. The model allows stakeholders to understand energy demand at each electric vehicle (EV) equipment level and is easily scalable to container demand and EV adoption rate projections.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Electrifying Terminal Trucks To optimize freight yards

The objectives of this project were to demonstrate the feasibility of electrification for freight yards’ diesel terminal fleets through pilot projects with two or more fleets, and to generate outreach material that can be used regionally and nationally to promote electrification in other terminal fleets. We planned to leverage the data into a model program for adoption of zero emissions freight handling and make information about this project, its outcomes, and the business case for zero emission deployments available to the larger freight marketplace.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Estimating Electrification Potential for Class 8 Regional-Haul Trucks

This one-page highlight details the key takeaways from a project that utilized NREL's Fleet Research, Energy Data, and Insights (FleetREDI) data analysis pipeline. As part of the North American Council for Freight Efficiency's (NACFE's) Run on Less Depot data workshop, NREL sought to understand how Tesla semi-trucks would perform in real-world regional haul applications. Analysis reveals that the modeled Tesla trucks, with an average efficiency of 1.78 kWh/mi, struggle to achieve full operational coverage using current battery and charging configurations assuming operations remain unchanged. However, in an extreme case where ubiquitous charging exists, 100% EV coverage is possible for the given drive cycles. These findings highlight the trade-off between battery size and charge rate in electrification potential and emphasize the necessity for advancements in charging infrastructure to enable electric trucks for regional haul operations.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Hourly Load Profile Dataset for Electric Transit Bus Depots in the United States

Transit buses operate primarily in dense urban areas, where nearby populations face increased exposure to fine particulates, nitrogen oxides, and other harmful pollutants. Electrifying transit buses presents a clear opportunity to reduce greenhouse gas emissions and improve urban air quality. However, widespread adoption may pose significant energy and infrastructure challenges, which can be mitigated through proactive planning and investment. This report presents a robust modeling framework and an initial estimation of the hourly electricity demand at transit bus depots across the United States. The resulting depot-level dataset, available at data.nrel.gov/submissions/282, provides valuable insights for infrastructure planning and electricity demand forecasting, supporting the scalable electrification of transit bus fleets nationwide.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Predicting U.S. federal fleet electric vehicle charging patterns using internal combustion engine vehicle fueling transaction statistics

Utilizing fueling transactions from internal combustion engine vehicles (ICEVs), the authors estimated how frequently midday public charging would be required for U.S. federal fleet battery electric vehicles (BEVs). Fueling transaction summary statistics are more widely available than trip-level telematics data, making this methodology more accessible and transferable to other researchers and fleet managers considering BEV replacements. For example, readers can easily apply a linear model using only the count of back-to-back fueling events at gas stations over 57 straight-line miles apart to predict days exceeding range. This linear regression predicted binned days exceeding 250 miles at 80% accuracy on a hold-out test set from the same fleet as the training data and 66 % accuracy on a new fleet displaying different driving behaviors. The authors additionally provide linear equations for days exceeding 200 and 300 miles as alternative range estimates to account for differences in BEV range and temperature impacts. Beyond the single-feature linear models which readers can apply, the authors tuned and trained other machine learning models on a variety of fueling transaction statistics including consecutive transaction distances, transaction distance from garage, estimated miles traveled from fuel economy and fuel quantity, and transaction periodicity. Utilizing a subset of 1678 light-duty federal fleet vehicles which contained daily vehicle miles traveled (VMT) in addition to fueling statistics, the authors determined which fueling transaction statistics were most relevant in predicting driving days exceeding 250 miles (an approximation of BEV rated driving range). In support of the U.S. federal fleet transition to zero-emission vehicles (ZEVs), the authors used these statistics and machine learning models to predict the frequency of BEV midday charging. After training models on the subset with VMT, the authors predicted days exceeding rated range for 112,902 light-duty vehicles operating in similar circumstances in the federal fleet using a Support Vector Regressor (SVR). In conclusion, they then used the projections as part of the ZEV Planning and Charging (ZPAC) tool to identify optimal candidates for BEVs for the federal fleet. An anonymized version of ZPAC is included in the supplementary materials.

25 ENERGY STORAGE↗
DOE OSTI2024

The health, climate, and equity benefits of freight truck electrification in the United States

Abstract Long-haul freight shipment in the United States relies on diesel trucks and constitutes ∼3% of U.S. greenhouse gas emissions and a significant share of local air pollution. Here, we compare the climate and air pollution-related health damages from electric versus diesel long-haul truck fleets. We use truck commodity flows to estimate tailpipe emissions from diesel trucks and regional grid emissions intensities to estimate charging emissions from electric trucks under various grid scenarios. We use a reduced complexity air quality model combined with valuation of air pollution-related premature deaths (using two hazard ratios (HRs)) and quantify the distributional health impacts in different scenarios. We find that annual health and climate costs of the current diesel fleet are $195–$249/capita compared to $174–$205/capita for a new diesel fleet, and $156–$177/capita for an electric fleet, depending on the HR. We find that freight electrification could avoid $6.2–8.5 billion in health and climate damages annually when compared to a fleet of new diesel vehicles (with even higher benefits when compared to the current diesel fleet). However, the Midwest and parts of the Gulf Coast would experience an increase in health damages due to vehicles charging using electricity from coal power plants. If old coal power plants (operating in 1980 or earlier) are replaced with zero-emission generation, electrification of all U.S. freight would result in $32.3–39.2 billion in avoided damages annually and health benefits throughout the U.S. Electrifying transport of consumer manufacturing goods (including electronics, transport equipment, and precision instruments) and food, beverage, and tobacco products would provide the largest absolute health and climate benefits, whereas mixed freight and manufacturing goods would result in the largest benefits per tonne-km. We find small variations in health damages across race and income. These results will help policymakers prioritize electrification and charging investment strategies for the freight transportation sub-sector.

Hennessy, Eleanor M. (ORCID:0000000294715765)↗
DOE OSTI2025

Viability Assessment of Wind and Solar Renewable Energy Generation in Support of Nationwide Vehicle Electrification

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.

Miller, Brandon [ORNL] (ORCID:0009000300169201)↗
DOE OSTI2025

Airport Ground Support Equipment Infrastructure & Logistics Electrification Assessment Tool: 2025 Data Development, Modeling and Analysis for DFW

The aviation industry is increasingly turning to modernize freight facilities by integrating electric Ground Support Equipment (eGSE) to enhance operational efficiency of freight facility moving vehicles and equipment. Airports worldwide are adopting eGSE to streamline cargo movement, reduce fuel and maintenance costs, and improve logistics coordination.1 North America, with its advanced aviation infrastructure, leads this transition, leveraging Internet of things (IoT)-enabled automation and zero emission technologies to boost reliability and reduce human errors.2 Electrification of freight facility moving vehicles and equipment boosts turnaround times, improves equipment reliability, and optimizes logistics coordination, giving operators a competitive advantage. With rising fuel price volatility and the pressure to meet stringent performance benchmarks, airports are focusing on cost-effective, scalable solutions for long-term financial and operational gains. To further accelerate electrification, airports are integrating Zero Emission Vehicles (ZEVs) into rental car fleets and deploying electric baggage carts, requiring strategic investments in charging infrastructure. 3 The shift, however, presents challenges, such as limited technical expertise, high capital costs, and complex procurement processes. By forging strategic partnerships, leveraging advanced technologies, and optimizing infrastructure investments, airports can create a resilient, future-ready ecosystem that enhances the movement of people and goods through electrification-driven efficiency. Supported by the U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO), this electrification effort provides a scalable, cost-effective solution to improve airport freight operations. Through targeted investments and innovation, airports enhance efficiency, reduce costs, and meet performance benchmarks while advancing toward a resilient, electrified future.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Vehicle-Cycle Inventory for Type C School Buses & Intra-City Transit Buses

This report documents the new inventory incorporated into the Research and Development version of Greenhouse gases, Regulated Emissions, and Energy use in Technologies (R&D GREET) 2025 model for the vehicle cycle of Type C school buses and intracity transit buses. The transportation sector contributes significantly to the United States’ energy consumption and resultant emissions (EPA, 2025a). However, public transit plays an important role in mitigating these impacts because it consumes a relatively low amount of energy per passenger (Congressional Budget Office, 2022). Public transit is widely used in the United States; more than 500,000 school buses (EPA, 2025b) and ~75,000 service buses (American Public Transportation Association, 2025) operate in the nation. These are primarily internal combustion engine vehicles (ICEVs) powered by diesel. Original equipment manufacturers (OEMs) are making efforts to electrify U.S. bus fleets by using batteries as a propulsion system to replace internal combustion engines. Electrification can reduce tailpipe emissions, such as particulate matter (with a diameter ≤10 µm [PM 10 ] and with a diameter ≤2.5 µm [PM 2.5 ]) and nitrogen oxides (Jonas et al., 2025; Martinez and Samaras, 2024; EPA, 2025b; Wayne et al., 2009). Hence, any energy and emission impact analysis of public transit must consider both conventional ICEVs and upcoming electric vehicle (EV) options for the school and transit buses that dominate this landscape. To understand the detailed environmental impact profiles of ICEV and EV school and transit buses, it is necessary to conduct a thorough analysis covering both vehicle manufacturing and vehicle use stages. The current literature lacks a detailed vehicle-cycle inventory for school and transit buses, which makes this kind of comparison difficult. To overcome this gap, we developed a comprehensive vehicle-cycle model for school and transit buses in Argonne’s R&D GREET 2025 model. The model is flexible in handling user inputs for key assumptions, such as component weights and material compositions, upstream energy sources for material processing, and vehicle operating parameters, to understand their impacts on energy use and emissions for both school and transit buses. This report is organized as follows: Section 2 provides details on the modeling approach and vehicle specifications (weights and composition of different vehicle components, and vehicle operating parameters) for both school and transit buses. Section 3 provides details on vehicle assembly, disposal, and recycling (ADR) approaches for the two buses. Section 4 includes details about their incorporation into the R&D GREET model.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Overview of NLR Automated Mobility District Implementation Research - Phases I, II, and III. The Convergence of Automation, Electrification, and On-Demand Services: Enabling Resilient Automated Mobility Districts

A research program by the National Renewable Energy Laboratory has been investigating the implementation prospects for fully automated passenger transport systems that are deployed to operate within dense urban settings, referred to as Automated Mobility Districts (AMDs). An AMD emphasizes the deployment of automated vehicles (AV) passenger transport services within a dense urban setting and other major activity centers with intense passenger origin-destination demand patterns, such as those found in large business districts, airports, and university and medical campuses. Phase I and II surveyed 10 early deployment sites and subsequently collected and evaluated the lessons learned from these early deployment sites, with particular attention to fleet operations, impacts of service reliability, and vehicle technology evolution as the field of companies was being progressively winnowed by the challenges of full automation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗