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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

EVs@Scale: NextGen Profiles EVSE Characterization 2025

As part of the U.S. DOE EVs@Scale consortium, the Next-Generation Profiles (NextGen Profiles [NGP]) project presents analysis and results from the characterization of high-power conductive and wireless charging infrastructure. High Power Charging equipment is capable of recharging electric vehicle traction batteries at power levels of 200KW and above. Electric Vehicle Service Equipment (EVSE) characterization involves testing over a wide range of DC charging currents and voltages during nominal and off-nominal conditions. This testing allows for a better understanding of the impact that high-power charging will have on the electric grid. A common set of standard test plans, procedures, and data requirements were applied to the characterization in this document with minor updates and improvements. This report covers all conductive characterization activities performed between October 2024 and September 2025 on the Delta Electronics 350KW Electric Vehicle Charging System, consisting of power cabinet model EIDN-U350KTA01 and dispenser model EIDD-U350SSUUAEG-350.Key Findings include: Output regulation, Efficiency and power factor, Load management, Grid Resilience, Smart Charge Management (SCM) performance, Thermal control system performance, Multi-port simultaneous charging performance, and Selected performance comparisons with other EVSEs characterized in the NextGen Profiles project. Hot and cold temperature testing was not conducted on the Delta 350KW due to laboratory limitations. Future research could include continued testing the Delta hardware under off-nominal temperature conditions including multi-port/multi-session simultaneous charge testing, in addition to collecting data on other high-power conductive chargers to augment.

25 ENERGY STORAGE↗
DOE OSTI2025

Understanding EV Charging Pain Points Through Deep Learning Analysis

Current and potential electric vehicle (EV) owners express concerns about the charging infrastructure, mentioning non-functional chargers, prolonged charging times, inconvenient charger locations, long wait times, and high costs as major barriers. Addressing these issues often requires analyzing actual vehicle charging data, which is typically proprietary and inconsistent due to diverse standards and protocols. To understand and improve the EV charging experience, customer reviews are typically used to identify common customer pain points (CPPs). However, there is not a comprehensive method to map customer reviews to a standardized set of CPPs. In collaboration with the National Charging Experience (ChargeX) Consortium, this study bridges these gaps by proposing a Systematic Categorization and Analysis of Large-scale EV-charging Reviews (SCALER) framework. SCALER is an integrated, deep learning framework that segments, actively labels, analyzes, and classifies EV charging customer reviews into six CPP categories. To test its effectiveness, we used SCALER to analyze over 72,000 reviews from customers charging various EV models on different networks across the United States. SCALER achieves a classification accuracy of 92.5%, with an F1 score exceeding 85.7%. By demonstrating real-world applications of SCALER, we enhance the industry’s ability to understand and address CPPs to improve the EV charging experience.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗
DOE OSTI2024

Behavior, Energy, Autonomy, Mobility Modeling Framework (BEAM) v1.0

The Behavior, Energy, Autonomy, and Mobility (BEAM) model is an integrated, agent-based travel demand simulation framework. Individual agents express preferences through a utility- maximizing evolutionary algorithm that minimizes each individual’s cost and time spent traveling via diverse modal options, including the competition for scarce supply resources such as parking spaces and charging infrastructure. BEAM simulates the essential elements that compose a dynamic transportation system. From the road network, parking and charging infrastructure, to the transit system and a synthetic population with plans and preferences, the virtual system is an amalgamation of multiple spatially resolved layers that together represent an integrated transportation system. BEAM is an extension to the MATSim (Multi-Agent Transportation Simulation) model, where agents employ reinforcement learning across successive simulated days to maximize their personal utility through plan mutation (exploration) and selecting between previously executed plans (exploitation). The BEAM model shifts some of the behavioral emphasis in MATSim from across-day planning to within- day planning, where agents dynamically respond to the state of the system during the mobility simulation. In BEAM, agents can plan across all major modes of travel including driving, walking, biking, transit, and demand-responsive ride hailing. It is designed to integrate with other open source transportation models, such as ActivitySim.

Lazarus, Jessica↗
DOE OSTI2026

County Electric Vehicle Home Charging Access Shares From the 2030 National Charging Network Study

This file contains modeled county-level home electric vehicle charging access shares from the study The 2030 National Charging Network: Estimating U.S. Light-Duty Demand for Electric Vehicle Charging Infrastructure by Wood et al. (2023). These are based on modeling in There's No Place Like Home: Residential Parking, Electrical Access, and Implications for the Future of Electric Vehicle Charging Infrastructure by Ge et al. (2021).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Strategic Energy Plan: City of Key West, Florida

This Strategic Energy Plan for the city of Key West, Florida—developed through the U.S. Department of Energy’s Energy Technology Innovation Partnership Project (ETIPP)—outlines a comprehensive strategy to advance the city’s energy vision: to improve energy efficiency and independence using local energy resources to foster long-term resilience. To guide this effort, the plan is structured around four focus areas: • Energy efficiency: Reduce overall energy consumption and utility costs across municipal, residential, and commercial buildings. • Local energy generation: Increase the share of energy produced from local sources to enhance energy independence. • Resilience: Strengthen critical infrastructure and community preparedness for flooding, hurricanes, and other natural weather hazards. • Electric transportation: Support the addition of new electric vehicles (EVs) and develop reliable charging infrastructure to reduce reliance on imported fuels. ETIPP provides strategic energy planning, technical assistance, and direct funding to U.S. coastal, remote, and island communities to improve energy resilience. Key West was part of ETIPP’s fourth cohort. As a low-lying island community vulnerable to infrastructure damage due to natural weather hazards, Key West seeks to reduce its dependence on external energy and build long-term sustainability. The Strategic Energy Plan identifies specific challenges, sets clear goals, and proposes actionable opportunities with implementation timelines and key stakeholders. A baseline assessment reveals significant energy consumption in both city-owned and residential/commercial buildings, limited local energy generation, and an early-stage EV market with vulnerable charging infrastructure. The proposed solutions emphasize a multifaceted approach, leveraging both established and innovative technologies, while addressing financial, technical, and community engagement challenges. The baseline assessment provided a snapshot of current energy conditions in Key West, evidencing key challenges and opportunities across the city’s energy priorities. Building on the baseline assessment and extensive input from city staff, local stakeholders, and community partners, a set of targeted opportunities was identified to address Key West’s energy goals. Each was evaluated in terms of its potential benefits, key implementation steps, associated challenges and mitigation strategies, and the city departments and stakeholders best positioned to lead or support progress.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Electric Vehicle Charging Analytics and Reporting Tool (EV-ChART): Data Format and Preparation Guidance (V.5.0)

The Joint Office of Energy and Transportation maintains the Electric Vehicle Charging Analytics and Reporting Tool (EV-ChART), which provides a centralized hub for submitting electric vehicle (EV) charging infrastructure data directed by the Federal Highway Administration (23 CFR 680.112(a)-(c)). EV-ChART provides a streamlined data submission process and an integrated set of analytic tools, connects to other data sources, and empowers data sharing and access across stakeholders, including the public. Any data shared publicly will be aggregated and anonymized to stay in accordance with 23 CFR 680. This EV-ChART Data Format and Preparation Guidance provides a comprehensive overview of the data reporting requirements as authorized under 23 CFR 680.112(a)-(c)). The guidance is intended to be used alongside the EV-ChART Data Input Template, which defines the tabular data structure that these data submissions must follow. Per 23 CFR 680.112(a)-(c), the annual and quarterly data submissions are required of all National Electric Vehicle Infrastructure (NEVI) Formula Program projects, as well as projects for the construction of publicly accessible EV chargers that are funded with funds made available under Title 23, United States Code, including any EV charging infrastructure project funded with federal funds that is treated as a project on a federal-aid highway. One-time data submissions are required of both the NEVI Formula Program projects and grants awarded under 23 U.S.C. 151(f) for projects that are for EV charging stations located along and designed to serve the users of designated Alternative Fuel Corridors (AFCs). Other information and data required in 23 CFR 680, such as 23 CFR 680.112(d), 23 CFR 680.116(c), and 23 CFR 680.106(a), are not discussed in this guidance.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Strategic Pathways for Indigenous Resilient and Innovative Transportation

This presentation details the results of the project, titled Strategic Pathways for Indigenous Resilient and Innovative Transportation. The project culminated in a guidebook for Tribes interested in pursuing public EV charging infrastructure. The guidebook is designed to serve Tribal planners, Tribal departments of transportation, environmental offices, Tribal enterprises, Tribal utility authorities, Tribal leadership, and any other interested parties. The conference presentation will provide details on the engagement conducted during the project to inform the project publications. The presentation will also discuss Tribal sovereignty-related considerations for EV charging infrastructure topics including O&M and working with the electric utility.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

OR-AGENT framework – Architecting electrified heavy-duty drayage applications

The widespread adoption of zero-emission vehicles in heavy-duty (HD) commercial freight transportation faces considerable technoeconomic challenges. For heavy-duty trucks, ensuring high uptime, cost parity with diesel, and safety standards is especially critical as these vehicles operate over long distances with heavy loads, where any downtime or off-nominal behaviors significantly impacts logistics, productivity, and the total cost of ownership. Unlike traditional diesel refueling, BEV charging infrastructure must be co-optimized with vehicle deployment, operational demands, and grid capacity to ensure cost-effective and reliable freight operations. However, the lack of a standardized ownership and service model has led to a fragmented approach—where commercial vehicle operators may invest in, own, and maintain both vehicle/batteries and charging/energy infrastructure. This disconnect may exclude energy service providers from the equation, forcing fleet operators to explore ‘behind-the-fence’ energy solutions that increase capital investment, operational downtime, overhead costs, and, in some cases, net carbon emissions. To address these issues, this study introduces OR-AGENT (Optimal Regional Architecture Generation for Efficient National Transport), a comprehensive modeling framework that integrates powertrain architectures, charging infrastructures, and energy backbone systems into a cohesive strategy. In this paper, OR-AGENT is applied to develop an interconnected systems architecture for energy efficiency and resiliency enhancement of heavy-duty drayage vehicles at the Port of Savannah, GA. This framework showcases an interconnected systems approach to electrifying heavy-duty drayage vehicles at the Port of Savannah, GA. The study assessed BEVs with 400–1200 kWh battery capacities, accounting for seasonal variations in weather and freight routing. A diverse charging mix (150 kW–1250 kW) was evaluated alongside grid capacity constraints, cost, and carbon intensity analysis, leading to the development of a strategic microgrid/Distributed Energy Resources (DER) deployment architecture to ensure a reliable and sustainable transition. However, the findings also highlight the need for alternative zero-emission solutions for remaining trips, such as larger batteries, electrified roadways, hydrogen powertrains, or net-zero emission fuels. In conclusion, the findings are incorporated into a Total Cost of Ownership (TCO) model to identify optimal architectures for an interconnected electrified ecosystem.

Commercial vehicles↗
DOE OSTI2024

Coupling of the Electricity and Transportation Sectors - Part II: Risk Assessment

This comprehensive report explores the intricate interplay through “sector coupling” between the Transportation Sector, Energy Sector, and the critical infrastructure challenges faced by the United States. Aligned with the ambitious federal goal of reducing greenhouse gas emissions, the report analyzes the present vulnerabilities in the fossil fuel supply chain, highlighting instances where the dependence on this industry has been susceptible to local and global disruptions. With a focus on risk assessment, this report establishes a framework to compare the fossil fuel-based transportation sector with the burgeoning electrified sector, considering the evolving technologies in electric vehicles (EVs) and the nascent charging infrastructure. This study delves into the historical reliability of fossil fuel-based transportation and identifies opportunities for strengthening sector coupling between transportation and the electric sector. Assessments of the gasoline-diesel supply chain provide insights into both opportunities and threats, offering a roadmap for creating a more reliable and resilient electrified transportation system. We modeled sectoral coupling scenarios for the current internal combustion engine (ICE)-based and future EV-dominant landscapes, leveraging qualitative sector attributes to identify relative strengths and weaknesses in Part 1 of the report. In this report (Part 2) we continue to understand the role of the electricity sector on transportation and develop a semi-quantitative methodology is employed to evaluate and compare the risks associated with the fossil fueled and electric transportation sector during emergency scenarios, drawing from real-world events like hurricanes and geopolitical disruptions. Different operational scenarios for the fossil fuel and electrified sectors have been developed to understand the risks associated with them. This report concludes with recommendations for the architectural designs of electric charging infrastructure, underlining the need for informed decisions in the transition towards a sustainable and resilient transportation future.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

EVSE Characterization

NextGen Profiles' EVSE characterization efforts explored performance variability in production EVSE through the use of EV emulation equipment and assessed how different operational conditions influence charging behavior. Data were collected at a frequency of 10 Hz from both the EV emulator and EVSE during each charge session and stored in a time-series database for further analysis. As part of the NextGen Profiles project, characterization of high-power EVSE was performed on both conductive and wireless charging infrastructure; however, only conductive charging data are currently included in this repository. This EVSE characterization was performed over a range of DC output currents and voltages, covering both nominal and off-nominal test conditions. This EVSE characterization dataset includes high-power charging data from two types of 350-kW-capable EVSE using liquid-cooled Combined Charging System-1 (CCS1, North American version) cables and connectors. To protect confidentiality, all EVSE metadata are anonymized, and the publicly released datasets are metered at 10-Hz frequency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Economic Storage Size Optimization for Electric Vehicle Extreme-Fast Charging Stations

En-route charging infrastructure for electric vehicles is critical to support transportation needs. These charging stations are likely to have high loads and especially sharp peak loads given fast charging capabilities needed to meet transportation schedules. In order to reduce both strain on distribution grid infrastructure and charging station operational costs, many stations are likely to employ behind the meter storage. This paper demonstrates a behind the meter storage sizing optimization that employs an open-source agent-based vehicle behavior model (BEAM) to determine the best sizing across many scenarios. This optimization and analysis is novel in that it examines how storage size impacts not only charging station cost and peak load, but also vehicle queue times. The optimization is also applied across a wide analysis region with sufficient diversity and numbers to provide novel statistical analysis of optimal sizes.

Aka, Julius↗
DOE OSTI2025

Finding gaps in the national electric vehicle charging station coverage of the United States

Abstract The United States federal government has invested $7.5 billion into charging infrastructure, including the National Electric Vehicle Infrastructure Program, to build fast charging stations along designated highways for long-distance car travel. We develop a consecutive coverage metric to compute the percent of United States roads (traffic-weighted) that are consecutively accessible within 500 miles of each county. We answer (1) what the state of consecutive coverage is in each county and (2) what the increase in coverage is when designated highways receive fast chargers. In 2023, 10% of counties had at least 75% minimum viable coverage. We find that if all designated highways receive fast-charging stations, 94% of United States counties will reach at least 75% fast charger coverage. However, the remaining counties are rural. This demonstrates that federal funding for fast chargers will help connect most—but not all—counties to the national network of continuously accessible charging stations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

An Old Guys Perspective of Cyber - Journey Through INL Cyber Research

An overview of the history of cybersecurity at INL and how it has evolved with today's Critical Infrastructure, including the advancement of Electric Vehicles (EVs) and the EV charging infrastructure. Recent and future research efforts are included to demonstrate the current state of the art and where this technology might progress. With maybe a little Fear, Uncertainty, and Doubt (FUD) mixed in...

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Statistical Estimation of EV Driver Charging Behavior and Influential Factors

INL received data collected via telematics from battery electric vehicles (BEVs), and these vehicles were owned by retail customers who had entered into a telematics user agreement. The goal of analyzing these data was to develop mathematical models to characterize how different sets of BEV drivers use charging infrastructure at home and away from home (i.e., public charging) and quantify how various factors influence BEV drivers’ decision to charge and use available infrastructure. The data used in this analysis are unique because they provide real world BEV driving and charging behavior at the individual driving and parking event level. In this study we seek to leverage this data to quantify BEV charging and driving metrics to help inform models that predict quantities like the specific times when loads are imposed on the electrical grid due to BEV charging. Most models that have been developed to predict electrical grid load due to BEV charging, use simulations of BEV driving events and rely on assumptions such as every vehicle charges every night. Using a statistical modelling framework, we seek to investigate BEV charging behavior and quantitatively assess these common assumptions of BEV charging behavior.

33 - ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Revenue-Maximizing Shared Parking and Electric Vehicle Charging Management in Multi-Unit Dwellings

In urban areas, searching for parking and electric vehicle (EV) charging can result in cruising, congestion, and environmental externalities. Recognizing the business opportunity of offering private parking and charging infrastructure access within multi-unit dwellings (MUDs) during daytime, we model a shared parking and EV charging management system. We maximize the revenue of MUD charging hubs in mixed land use, catering to public demand. Our approach accounts for the objectives of the two stakeholders involved: a demand model is fitted on the choices of EV charging users, and the supply model optimizes the allocation of parking and charging requests in an MUD parking lot. A binary integer linear programming model for the allocation of parking and charging spaces with a rolling horizon is integrated with matching rules that handle both parking and charging requests. In our numerical experiments in a neighborhood of Chicago, Illinois, we estimate the performance of the MUD parking and charging system with metrics that include revenue, number of matchings, and utilization rates. At any given time, MUDs with lower prices attract more charging requests, particularly those of longer duration, resulting in higher revenue and greater charging utilization. Dynamic pricing facilitates a more equitable distribution of requests; as MUD parking lots reach capacity and their fees increase, other MUDs become more competitive, attracting additional requests. Comparing our method against first-come-first-served and optimal-solution benchmarks, we demonstrate our model’s effectiveness in dynamically managing mixed parking and charging demand in MUD charging hubs.

electric vehicle, multi-unit dwelling, charging in↗
DOE OSTI2025

Improving Dynamic Wireless Charging System Performance For Electric Vehicles Through Variable Speed Limit Control Integration

Electric Vehicle (EV) charging has been a significant barrier to the widespread use of EVs. Traditional EV charging methods depend on cables, and there are concerns about safety, accessibility, convenience, and weather. A recent development, dynamic (or in-motion) wireless charging, enables EVs to charge wirelessly by incorporating charging infrastructure into roadways, allowing EVs to charge while moving. However, the energy transferred relies heavily on vehicle speed and time spent in the charging lane. This paper proposes an innovative solution that combines dynamic wire-less charging with Variable Speed Limit (VSL) control. This dynamic traffic control strategy adjusts speed limits based on real-time traffic, weather, and incidents. This integration of dynamic wireless charging and VSL has two potential benefits. First, it can motivate driver compliance with VSL through the incentive of charging. Second, it can promote smoother traffic flow and improve traffic safety by implementing lower speed limits at certain times. To verify these benefits, microscopic traffic simulations in SUMO were conducted under different EV penetration rates and VSL compliance rates. Simulation results reveal that the proposed approach can enhance dynamic wireless charging system performance while improving traffic flow and safety.

Xu, Guanhao [ORNL] (ORCID:0000000214326357)↗
DOE OSTI2026

TSDC: Transportation Secure Data Center: Real-World Data for Planning, Modeling, and Analysis

The Transportation Secure Data Center is a centralized repository for high-resolution transportation data from hundreds of travel and transit surveys and studies. It makes vital transportation data broadly available to users while preserving the privacy of survey participants. It houses surveys and studies conducted by state departments of transportation, metropolitan planning organizations, transit agencies, cities, and other public agencies. Meanwhile, the Livewire Data Platform empowers research, industry, and academic partners to easily and securely preserve, maintain, share, discover, and gain access to transportation and mobility data. Livewire accommodates a range of datasets, including behavioral, experimental, model, analytical, and raw data at the vehicle, traveler, and system levels. Datasets support mobility research and planning spanning urban science, connected and automated vehicles, fueling and charging infrastructure, mobility decision science, multimodal transportation, vehicle efficiency, and more.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

National Fire Protection Association Spurs the Safe Adoption of EVs Through Education and Outreach

As electric vehicles (EV) and their charging infrastructure steadily increase in popularity across the U.S., growing pains still exist, impeding their true potential for growth. The barriers include a general lack of knowledge around EV systems, range anxiety, electrical workplace safety practices, code compliance and inspection for charging stations, maintenance garages and workers, insurance concerns, and the potential risks associated with damaged lithium-ion batteries (fires, re-ignition, water immersion, and stranded energy).NFPA partnered with SMEs, training development organizations, and Clean City Coalitions to create a series of common online trainings for each EV ecosystem sector, educating communities on the realities of EV proliferation, myths, facts, statistics, codes, and basic information for each sector to become versed in EV knowledge. We also constructed a workshop program for the many EV stakeholders who should be involved in setting up a strategic, cohesive outlook for the participating towns and cities.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗