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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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18 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Airport Ground Support Equipment Analysis

Ground support equipment (GSE) plays a critical role in providing services to aircraft at arrival and departure gates, ensuring smooth and efficient airport operations. As the airline industry seeks to reduce costs and expand energy options, electric GSE (eGSE) is emerging as a promising solution. This project examines the integration of eGSE into airport operations, addressing key considerations such as vehicle deployment, charging infrastructure, and grid impacts. We developed a flexible, bottom-up modeling framework to evaluate energy demands and infrastructure requirements across more than 300 U.S. airports, providing actionable insights to support electrification planning and implementation. The study focuses on eight major types of GSE with commercially available electric counterparts: aircraft tractors, ground power units, baggage tractors, belt loaders, cargo loaders, catering trucks, lavatory trucks, and water trucks. Project results include annual charging load profiles, total energy consumption, and the charger and fleet requirements for each airport.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Flight Arrival Data for Each Airport

This dataset contains flight arrival data for each airport, which serves as the input for generating load profiles and estimating energy needs and fleet and charger requirements. ![image](distribution-flight-arrivals.png) Distribution of annual flight arrivals across different airport categories.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Data-Driven Simulation-Based Planning for Electric Airport Shuttle Systems: A Real-World Case Study

Many airports are adopting battery electric buses in their shuttle fleets due to concerns over air quality and regulations. This study proposes a simulation-based optimization modeling framework to help airport shuttle operators effectively deploy electric buses. We evaluated a planned airport electric shuttle system with an event-driven simulator. Empirical data collected from existing systems were used to drive the simulations. We then proposed a simulation-based optimization model to determine the battery capacity, charging power, and number of chargers so that predefined objective(s) (e.g., minimizing total capital cost, minimizing emissions) are optimized. Compared to existing studies, the primary contribution of the proposed method is that it can model the real-world stochastic nature of operations in an electric bus system with much higher fidelity. To demonstrate the proposed modeling framework, we study a real-world shuttle system at the Dallas-Fort Worth International Airport, and present extensive numerical studies. When considering partial fleet electrification, the model can provide a set of Pareto optimal solutions. When considering full fleet electrification, the optimal solution requires a 50-kWh battery capacity and four 210-kW chargers, resulting in a total capital cost of $26,744,000. The results demonstrate that the proposed modeling framework can effectively optimize the planning of electric airport shuttle systems with partial or full fleet electrification.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2025

EVI-Rental: A Scalable Model to Quantify the Impact of Rental Car Electrification

This fact sheet describes the Electric Vehicle Infrastructure - Rental Car (EVI-Rental) tool, a flexible and comprehensive simulation tool that addresses key questions about the charging demand, infrastructure needs, and business impacts of adding growing numbers of electric vehicles (EVs) to rental fleets. To validate the EVI-Rental model, the Athena research team conducted a case study at Dallas-Fort Worth International Airport (DFW) to understand the impact of state-of-charge requirements, different charger types and charging schedules, solar power generation, and behind-the-meter storage on a fully electrified rental car fleet.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Development of a Heavy-Duty Electric Vehicle Integration and Implementation (HEVII) Tool

As demand for consumer electric vehicles (EVs) has drastically increased in recent years, manufacturers have been working to bring heavy-duty EVs to market to compete with Class 6-8 diesel-powered trucks. Many high-profile companies have committed to begin electrifying their fleet operations, but have yet to implement EVs at scale due to their limited range, long charging times, sparse charging infrastructure, and lack of data from in-use operation. Thus far, EVs have been disproportionately implemented by larger fleets with more resources. To aid fleet operators, it is imperative to develop tools to evaluate the electrification potential of heavy-duty fleets. However, commercially available tools, designed mostly for light-duty vehicles, are inadequate for making electrification recommendations tailored to a fleet of heavy-duty vehicles. The main challenge is that light-duty tools do not estimate real-time vehicle mass, a factor that has a disproportionate impact on the energy consumption of large commercial vehicles. The Heavy-Duty Electric Vehicle Integration and Implementation (HEVII) tool advances the state of the art in evaluating electrification potential and infrastructure requirements for fleets of commercial vehicles. In this work, the HEVII tool is demonstrated with non-uniformly sampled telematics data from an existing fleet to assess the suitability for electrification of each individual vehicle, determine optimal locations for charging infrastructure to support a fleet of EVs and analyze associated costs. Payload mass is predicted using sparse ground-truth data for all input drive cycles and an initial data analysis is conducted to assess the characteristics driving behaviors and energy consumption of the fleet using an adaptable vehicle model. Battery size requirements are determined by applying a novel charger placement algorithm to maximize routes that are viable for EVs and balance time delays with infrastructure development costs. This work details and demonstrates the different aspects of the HEVII tool, presenting preliminary results from an example use case.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

A novel method for co-optimizing battery sizing and charging strategy of battery electric bus fleets: An application to the city of Paris

Battery-electric buses (BEBs) are a promising technology for replacing diesel buses and reducing their environmental burden. However, their charging process can take several hours, depending on the charging technique and strategy, making them susceptible to schedule disruptions. Furthermore, the selection of the charging strategy and battery size can increase the total capital investment and operational expenses of a BEB fleet, which is a major obstacle to its adoption. Therefore, to minimize the total cost of ownership (TCO) and prevent schedule disruptions, it is essential to establish a well-defined approach to determine an appropriate battery size and charging strategy for BEB fleets. This paper presents a method for reducing the TCO of BEB by determining the optimal battery size and charging strategy for each bus while satisfying operating constraints. The method involves a two-step optimization algorithm that uses Dynamic Programming and Genetic Algorithm. Further, the study applies this approach to the bus fleet serving line 21 in Paris and generates the optimal battery sizing, charging strategy, and required charging infrastructure. The results indicate that 100 kWh batteries offer the best trade-off between capital and operational expenditure for the fleet deployment if used with 65–85 kW chargers at bus terminals.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Fleet Size Requirements for Each GSE Type at Each Airport

This dataset contains the required number of vehicles for each GSE type at each airport under six charging scenarios: (1) charging when the battery state of charge is insufficient for the next service using 40-kW chargers (S1); (2) charging when the battery state of charge is insufficient for the next service using 20-kW chargers (S2); (3) charging starts immediately after each GSE completes a service task using 40-kW chargers (S3); (4) charging starts immediately after each GSE completes a service task using 20-kW chargers (S4); (5) charging during off-peak hours using 40-kW chargers (S5); and (6) charging during off-peak hours using 20-kW chargers (S6). ![image](gse-vehicles-chargers.png) Number of GSE vehicles per GSE type and chargers required for different airport categories under charging scenario 1.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Modeling Future Demand for EV Charging Infrastructure [Slides]

U.S. climate goals for economy-wide net-zero greenhouse gas emissions by 2050 require rapid decarbonization of the light-duty vehicle fleet and plug-in electric vehicles (EVs) are poised to become the preferred technology for achieving this end. Considerable investments in public and private EV charging infrastructure will be needed to support widespread adoption, however, guidance is lacking on when, where, and what types of chargers will be needed. In this PLMA Load Management Dialogue session, researchers from the National Renewable Energy Laboratory (NREL) discuss findings from a recent quantitative assessment of the charging network requirements to support high penetrations of light-duty EVs by 2030. The study produced multiple detailed network growth trajectories at the national, state, and local levels that serve as a guidepost for future planning. In addition, an overview of NREL's publicly accessible EV infrastructure tools and data sets, designed to support planning and decision-making for EV infrastructure stakeholders, is provided.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

A Unified Design Theory for Multi-Port Polyphase Transformers Enabling Scalable Power-Multiplexed EV Fleet Charging Systems

This paper presents a unified analytical design theory for multi-port polyphase transformers, targeting scalable and isolated high-power Electric Vehicle (EV) fleet charging systems with power multiplexing capability. As fleet electrification accelerates, conventional one-to-one charger architectures face significant challenges in infrastructure cost, peak power demand, and low utilization of installed power electronics. Power-multiplexed charging architectures, which dynamically distribute power from a shared pool of converter modules across multiple vehicles, have emerged as a promising solution. However, such architectures require scalable, isolated multi-port power interfaces capable of routing energy among multiple inputs and outputs, whose design remains complex and dependent on iterative modeling. To address this gap, the proposed theory provides closed-form expressions for self-inductance, leakage inductance, and mutual coupling terms for arbitrary multi-phase, multi-port transformer structures. The formulation enables direct synthesis of isolated multi-input and multi-output resonant converter systems without reliance on geometry-specific finite-element analysis or extensive parameter extraction. This capability is particularly critical for power-multiplexed systems, where modular converter structures must interface with multiple vehicles while maintaining galvanic isolation and flexible power allocation. The effectiveness of the proposed framework is demonstrated through the design of a 360 kW multi-phase system operating over a 700–900 VDC input and 400–1250 VDC output range. PLECS simulation results confirm accurate prediction of system behavior and validate the applicability of the approach to multi-port, power-multiplexed charging scenarios. The proposed method significantly reduces design complexity while enabling scalable, cost-effective, and fully utilized EV fleet charging infrastructure.

Asa, Erdem [ORNL] (ORCID:0000000190884812)↗
DOE OSTI2025

EVI-LOCATE User Manual

One of the longest stages in the deployment of electric vehicle supply equipment (EVSE) is the initial planning of the infrastructure itself. Engineers and fleet experts from the National Renewable Energy Laboratory (NREL) have supported dozens of charging infrastructure site plans over the past couple decades, including the generation of site schematics, determinations of electric capacity, and estimates for likely costs. As the market for electric vehicles (EVs) has matured, this approach should no longer require a time and personnel intensive process. In order to shorten the time taken to develop site plans and cost estimates, NREL developed a tool that fleet managers, facility managers, electricians, EVSE installers, and members of the public can use to develop initial schematics and ballpark pricing for charging station installations. The Electric Vehicle Infrastructure - Locally Optimized Charger Assessment Tool and Estimator (EVI-LOCATE) provides a structured and consistent way for users to enter information about their planned EVSE project in a relatively simple web-based format. EVI-LOCATE then calculates electrical equipment capacity, wiring runs, and project costs. It produces a site diagram optimized around surface characteristics with differential trenching costs for softscape such as grass compared to hardscape such as asphalt that can be adjusted by users in the tool. It also stores the resulting site plans and costs in a dashboard for access at a later date, including plan revisions if necessary. This document guides users through the EVI-LOCATE screens and associated questions. It contains tip text boxes throughout on how best to interface with the tool and find additional information or context. The appendices contain the assumptions and calculations underpinning the tool. Much of the information for EVI-LOCATE was gathered through industry engagements with EVSE installers, invoices from completed EVSE installations, Gordian's RS Means construction data, and the General Services Administration blanket purchase agreement for EVSE. For a visual tutorial of the tool, users can watch EVI-LOCATE Step-by-Step Video. The tool itself is available at https://evi-locate.nrel.gov.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Advancing Federal Infrastructure Through Innovation: Selecting Electric Vehicle Charging Infrastructure Wisely

The Biden Administration's issuance of Executive Orders directly addressing and accelerating Federal fleet electrification places an onus on both fleet and facility managers. This panel will help fleet and facility personnel understand how to select EVSE infrastructure in terms of implementation, energy management, and costs of procurement, installation/deployment, and electricity. The panel will cover technology aspects to include managed EVSE, bi-directional charging, implications on local electrical systems and storage needs in certain cases to buffer power demand. Discussion will include various considerations relevant to ensuring adequate EVSE, balancing infrastructure needs, and upgrades with fleet needs. The panelists will discuss how to provide sufficient infrastructure support, including which stakeholders to involve, procurement impediments, easement locations, and requirements that arise during the design and implementation process.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2021

Grid-Integrated Electric Mobility Model (GEM) v1.0

Transportation is the fastest-growing source of greenhouse gas (GHG) emissions and energy consumption globally. The convergence of shared mobility, vehicle automation, and electrification has the potential to drastically reduce transportation impacts, but requires careful integration with rapidly evolving electricity systems. We have developed the GEM Model (Grid-Integrated Electric Mobility) to examine these interactions with a U.S.-wide simulation framework encompassing private electric vehicles (EVs); shared automated EVs (SAEVs); charging infrastructure; controlled EV charging; and a grid economic dispatch model to simulate mobility futures exclusively using EVs. We find that an SAEV fleet 9% the size of today's active vehicles can satisfy trip demand with only 2.6 million chargers (0.2 per EV). Controlled EV charging can also reduce electricity demand variability, significantly reducing GHG emissions and decreasing solar curtailment by about one-third. While private EVs with uncontrolled charging would reduce GHG emissions by 53% compared to gasoline vehicles, SAEVs could achieve a 70% reduction.

Sheppard, Colin↗
DOE OSTI2022

Port of New York and New Jersey Drayage Electrification Analysis

The National Renewable Energy Laboratory (NREL) evaluated the potential for drayage electrification in the Port of New York and New Jersey (PoNYNJ), with a focus on operators: Harbor Freight Transport (HF), Safeway Trucking (SWT), and International Motor Freight Inc (IMF). This report summarizes the data collection and electrification evaluation of all three drayage operators, includes detailed operational data, and identifies the performance requirements for battery electric tractors (BETs) and corresponding infrastructure operated within the context of PoNYNJ drayage operation. This report also details a methodology to evaluate opportunities, strategies, and challenges associated with future expansions of BETs in meeting PANYNJ emissions goals. The Port Authority has established a goal of achieving Net Zero carbon emissions by 2050 across all facilities, including from tenant and stakeholder sources such as drayage trucks. NREL used real-world performance data collected on the three PoNYNJ drayage operations, along with modeling and analysis tools to compare BET to diesel trucks. From March to July 2021, NREL collected 1Hz vehicle and engine data from 46 drayage trucks at the three operators totaling nearly 121,000 miles of operation, providing enough information to assess vehicle operations for electrification potential. A Future Automotive Systems Technology Simulator (FASTSim) electric truck powertrain model was validated using PoNYNJ data and scenarios were run to evaluate drayage truck electrification requirements over the real-world cycles. The first scenario examined BET viability with minimal changes to existing operations. This assumes the trucks charge when stopped for two hours or longer, have a functional battery size of 375 kWh, and can charge at 270 kilowatts (kW) average which are the specification of the commercially available Freightliner eCascadia. The second scenario looked at what operational, charging infrastructure, and BET technology changes would be needed to fully electrify. Finally, detailed analysis was run on charging rate structure to understand operational costs to the fleets. The studied drayage trucks averaged 5.1 MPG, spent roughly 9% of their energy at idle, and drove an average of 140 miles per day with a maximum daily distance of 573 miles. The FASTSim model results indicate a comparable BET would use 417 kWh of energy per day on average accounting for cargo weight, which is close to the full usable capacity of the eCascadia currently available on the market. Based on the daily average operating data, partial fleet electrification is possible with current technology. However, some specific days of operation would require over 1,600 kWh of energy due to longer distances traveled by the trucks and more intense operation. Trucks used for long distance and intense operation cannot be readily electrified with current technology without operational changes. Full adoption of BETs could reduce CO 2 emissions from these fleets by roughly 75% today, eliminating 76 metric tons of CO 2 (MTCO 2 ) per vehicle each year, which equates to 24,100 MTCO 2 per year for all three operators. Commercially available direct current fast chargers (DCFC) have charge rates up to 350 kW. Based on the average daily modeled energy use for each operator, current industrial rate structures, and the assumption of 350 kW peak charging, full drayage electrification would increase electricity consumption. In addition, peak demand usage would increase with unmanaged charging along with cost of electricity having a direct impact on cost per mile for electric vehicles. The resulting cost per mile for BETs along with comparable cost per mile for conventional diesel trucks are also examined at $\$$4.00 per gallon of diesel. It will be important for PANYNJ and the drayage operators within the PoNYNJ to consider these load impacts to their existing electrical infrastructure and devise operational strategies that avoid coincident charging of vehicles to mitigate demand charges. Despite these electricity cost increases, savings from reductions in diesel consumption will help offset the costs of this increased electricity consumption. However, prices of both electricity and diesel are subject to change based on various factors meaning the realized savings will vary over time. This shows BETs could be cost-competitive on an energy cost per mile basis for all scenarios while diesel is above $\$$3.00/gal. Further, if diesel prices dropped to the 15-year low of $2.33/gal, it would still be cost competitive to operate the EVs with electricity costs of 16.3 ¢/kWh or less.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Electric Medium- and Heavy-Duty Vehicle Charging Infrastructure Attributes and Development

Although more established for light-duty vehicles (LDVs), advancements in electric vehicle (EV) charging technology are being made in the medium- and heavy-duty (MD/HD) sector. Progress is also being made with the electrification of MD/HD vehicles, including transit buses, school buses, MD trucks, and HD trucks. The diverse set of operational requirements and duty cycles for each vocation, as well as the range in the size of fleets, present unique charging and infrastructure requirements. This report focuses on charging requirements for MD/HD vehicles and synergies with LDV infrastructure. This analysis leans toward the qualitative rather than quantitative because relevant model inputs are in development and will not be established for a few years, as EV deployments are more mature in the LDV sectors than MD/HD. The report begins with an overview of MD/HD vehicle classes and types of charging, including depot and residential charging, among others (Section 2). Section 3 analyzes the home bases (overnight dwell locations) of existing MD/HD vehicles, with an emphasis on depot and residential home bases, and discusses implications for charging infrastructure. Section 4 discusses the key characteristics for determining if, when, and where MD/HD vehicles can leverage LDV charging infrastructure rather than requiring dedicated chargers. These considerations include electricity demand, connectors, physical space requirements, payment considerations, and impacts on the grid. Section 5 summarizes shared characteristics for MD/HD vehicles that are appropriate for near-term electrification and includes a summary of the outlook of the electric MD/HD vehicle market. The conclusion (Section 6) summarizes the report's findings and outlines areas for future research.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Connecting Electric Vehicle Charging Infrastructure to Commercial Buildings

Electric vehicles (EVs) are growing in popularity and gaining meaningful market share with record sales year over year in the last decade. EV charging equipment, also known as EV chargers (EVC) or EV supply equipment (EVSE), must proportionally match the growing number of new EVs on the road for a comparable experience to gas-powered vehicles. The majority of EV charging currently happens at residential buildings. However, demand for EV charging at commercial buildings will significantly increase with wider mainstream EV adoption and as businesses return to more normal operation following COVID-19 pandemic disruptions. Charging equipment can include various sub-systems like power conditioning module, control software, safety devices, metering, communication, cooling, connectors, and its wiring. EV charging at commercial buildings could be used for public, workplace, and commercial fleet charging. This document aims to describe how EVC can be connected to commercial buildings, including considerations for facility managers, and the effects that charging will have on the buildings electrical distribution system. More specifically, this resource provides an overview of: understanding EV charging basics: how charging equipment connects to the building and to EVs; required infrastructure updates needed at the building site to connect EVC to existing distribution systems; network strategies for cost-effective operation; metering and utility considerations for billing and incentives; charging equipment ownership options; future trends in EVC connection to buildings.

ADVANCED PROPULSION SYSTEMS,ENERGY CONSERVATION, C↗
DOE OSTI2025

Optimized V1G and V2G Electric Vehicle Fleet Management and Grid Transaction at Marine Corps Air Station Miramar in San Diego, CA

The overall technical goal of the project was to demonstrate an all-electric bi-directional non-tactical fleet at Marine Corps Air Station (MCAS) Miramar that was integrated and controlled with other distributed energy resources (DERs) (i.e., PV, stationary battery, and building loads) to provide resilience to critical electric loads in the event of grid outages, to minimize charging costs, and to provide economic energy resources to electricity markets. In this project, the specific, technical objectives were: 1. Demonstrate that bi-directional electric vehicles can provide critical complementary services to fixed storage batteries in microgrid applications while performing function as non-tactical vehicles. 2. Demonstrate participation of bi-directional (V2G) and unidirectional (V1G) PEVs for demand management and minimization of charging costs. 3. Demonstrate integration of multiple DERs for grid service participation. US Marine Corps Air Station (MCAS) Miramar in San Diego was the site of this electric vehicle-to-microgrid-utility grid test and demonstration project. Existing microgrid assets in this study included (1) a public works building; (2) a 30-kW rooftop photovoltaic (PV) system and (3) a separate 250 kW carport PV system. In this project, six bi-directional V2G vans were located at the MCAS Miramar’s showcase building-scale microgrid to develop and test technical capabilities that V2G can provide in microgrid applications (e.g., cost reduction and resiliency). These resources provided aggregated demand management and simulated participation in current retail DR programs. The vehicles used in this demonstration were selected because they provided functionality that MCAS Miramar needed, 15 passenger transport and facilities work cargo carrying capacity, and bi-directional charging capability that the research project required. All vehicles in this study were manufactured and distributed by VIA Motors, Inc. There were six vehicles total and each was VIA’s VTRUX eREV V2G model, a modified General Motors Chevrolet 2500 2WD van. Three of the vans were configured as passenger vans and the other three were configured as cargo vans. Each van had an on-board bi-direcrtional inverter/charger, Bel Power Solutions model 350INVCHGT150-120-240-8G nominally rated at +/-15 kW. The VIA van’s charging connector follows the J1772 charging protocol. The bi-directional EVSEs demonstrated in this study were manufactured by Coritech, Inc. Each VGI-80-AC charging station enabled enhanced V2G charging capability to a Clipper Creek CS-100 charging module. The enhanced capabilities included ethernet communication following the SEP2.0 protocol with a distributed energy resource function set and an operator screen displaying real-time SOC, voltage, and current. The VGI-80-AC charging stations are classified as level 2 with a maximum current output of 80 A or effectively 19 kW. The VIA van’s onboard charger limited the charging and discharging power to 15 kW in each direction. A control computer was installed in the EWOC and connected to an existing monitor. The V2G control communication network was a completely stand-alone closed system that did not have any connection to any other networks on the base. A cybersecure remote communication connection was created with a cellular modem, firewall hardware, and a virtual private network configuration.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

EVSE Characterization: V2G EVSE Comparison

As part of the U.S. Department of Energy EVs@Scale consortium Next-Generation Profiles project, results and analysis from the characterization of high-power conductive and wireless charging infrastructure are presented. This characterization is conducted over a wide range of direct current (DC) current and DC voltage operation for nominal test conditions and off-nominal test conditions. Test plans and procedures were developed to define the test configurations and requirements, measurement parameters, and test procedures used throughout testing. Results from a 2024 study conducted on electric vehicle supply equipment (EVSE) characterization by the Idaho National Laboratory (INL) include two bi-directional vehicle-to-grid (V2G) capable EVSEs. These EVSE are referred to as V2G-EVSE9 and V2G-EVSE10. Laboratory testing is conducted at nominal test conditions to characterize the power transfer capabilities, efficiency, power factor, and other power quality metrics of the two DC EVSEs capable of V2G bi-directional power transfer. Results from testing show the performance is consistent for V2G-EVSE9 and V2G-EVSE10 when comparing charging to discharging performance, except for V2G-EVSE10 for power transfer when operating above 70% of the rated DC current. V2G-EVSE10 efficiency is >98% while charging and <91% while discharging at the same operating conditions, near maximum-rated current, at 300VDC. In contrast, V2G-EVSE9 results are consistent for charging and discharging. This EVSE is nearly 96% efficient while charging or discharging when operating over 50% of rated AC power. V2G EVSE performance is also characterized during off-nominal AC grid conditions involving AC voltage deviation (426 VAC to 518 VAC), AC frequency deviation of +2% (58.8 Hz to 61.2 Hz), and AC voltage harmonics injection. Many test conditions have little-to-no impact on performance characteristics of the two EVSEs; however, there are a few notable findings with significant power transfer capability impacts. AC voltage harmonics injection resulted in negative impacts on power quality attributes for both EVSEs, but with no impact on power transfer capability. Off-nominal AC voltage and frequency conditions resulted in unstable or lack of power transfer capability for both EVSEs. V2G-EVSE9 is unable to transfer power when AC voltage is >300V L-N. V2G-EVSE10 is unable to transfer power when AC frequency deviation exceeds +0.8%. V2G energy management system transient response and latency are quantified during laboratory testing. V2G-EVSE9 and V2G-EVSE10 utilize cloud-based V2G energy management systems that command the power transfer level between the EVSE and EV. The latency and response characteristics of the entire systems (web-based user interface, V2G energy management system, cellular communications, and EVSE response) are quantified through laboratory testing for V2G-EVSE9 and V2G-EVSE10. V2G-EVSE9 latency ranges from 0.8 to 1.8 seconds, whereas V2G-EVSE10 latency ranges from 3.4 to 8.8 seconds. The ramp rate to a change in power transfer request also differs between the two EVSEs. V2G-EVSE10 ramp rate ranges from 50% to -250% of rated AC power per second, whereas V2G-EVSE9 rate ranges from 95% to -95% of rated AC power per second. At the highest rate of change in power transfer, V2G-EVSE10 can change from full charge power to full discharge power in less than one second. The V2G EVSE characterization presented in this report provides valuable insights and results for use by numerous entities. This includes modeling and simulation organizations, decision makers, fleet planning, industry stakeholders, and many others involved with the development and deployment of electrified transportation technologies. Additional high-power DC chargers, bidirectional chargers, and inductive power transfer EVSE characterization results are anticipated from additional EVSE brands and models, which will be detailed in future publications in support of the U.S. Department of Energy EVs@Scale consortium Next-Gen Profiles project.

25 ENERGY STORAGE↗