SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SEARCH · Engineering Papers
Results for “charging load profile”
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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SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SWS Building Electric Demand Profile
This dataset contains data on the electric use of the building in 15-minute increments (kilowatt-hours and average kilowatts).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
EV Profile Capture
NextGen Profiles' EV profile capture efforts aimed to explore the variance in performance and evaluate how different operational conditions influence production EV charging behavior. Data were collected at a frequency of 10 Hz from both the EV and EVSE during each charge session. These charge session parameters were then entered into a time-series database for further analysis. The data were gathered under different operational conditions to examine the effects of various factors such as battery state of charge, battery temperature, vehicle condition, smart charge management, and EVSE limitations. The EV profile capture dataset includes extensive high-power charging data from 16 different EVs—comprising light-, medium-, and heavy-duty vehicles—along with EVSE from various suppliers. To protect confidentiality, the EV and EVSE metadata are anonymized, and the publicly released datasets are aggregated to 0.1-Hz frequency.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Underlying Data for the EVI-RoadTrip Web Tool
The dataset contains simulation-based charging infrastructure outputs that are visualized on the EVI-RoadTrip webtool. The outputs are aggregated to lower spatial resolution (e.g., state-level, corridor-level).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
National Dataset of EV Charging Stations With Estimates of Load and Vehicle Throughput
Current data from the AFDC provide locations and many details about EV charging stations, but not estimates of their peak loads or the number of vehicles they can accommodate. This dataset will augment the AFDC charging station locations with estimates of transmission load and vehicle throughput based on engineering specifications of the chargers, charging patterns based on vehicle types, battery capacities, and user behavior.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
GSE Daily Energy Consumption for Each Airport
This dataset contains GSE daily energy consumption for each airport.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Charger Requirements for GSE at Each Airport
This dataset contains the required number of chargers for GSE 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).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
GPS Locations for Each Airport
This dataset contains GPS locations for each airport.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Airport Ground Support Equipment Analysis Data
Explore the source record for details and available documents.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
GridPIQ Reference Data
GridPIQ uses dozens of publicly available datasets to provide context for a user's grid project, as well as defaults for users to choose from. Users can choose to import their own data to better customize their analysis or use GridPIQ-supplied defaults. This allows users to get up and running with an analysis very quickly without having to spend significant time pulling together input data. To run an electric vehicle (EV) smart charging project, a user will need to provide or select from prepopulated values for the regional load profile shape and peak load, region of interest and closest weather station, EV charging profile, number of EVs to add for the analysis, maximum EV charging power, location of chargers relative to grid infrastructure, and allowable charging times (for coordinated charging mode). The outputs of the analysis are changes in air quality, EV energy consumption, EV peak demand, and EV hourly consumption profile—before and after project implementation." For a detailed description of the tool methodology, including all the publicly available datasets used by the tool, see the [GridPIQ documentation](https://gridpiq.pnnl.gov/v2-beta/doc/).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Seasonal Reconfiguration of Electrical Distribution Systems to Mitigate the Impact of Electric Vehicle Charging
Power grids face challenges in their infrastructure related to the integration of electric vehicles (EV). In particular, EV charging stations may induce instability in key system parameters such as substantial voltage drops, active power losses, and transformer overload due to high demand during charging periods. This article presents a seasonal reconfiguration strategy based on the differential evolution (DE) algorithm, aimed at enhancing system performance under highly variable and stochastic load profiles, particularly those driven by EV charging. The DEA algorithm is hybridized with the find-union (FU) algorithm to efficiently ensure network radiality throughout the optimization process. The proposed methodology is validated on a hybrid distribution system composed of the IEEE 33-bus network, a modified IEEE 13-bus system, and a specific 13-bus microgrid. Results have demonstrated that seasonal reconfiguration significantly reduces active power losses and mitigates transformer loading during critical demand hours, thereby quantifiably increasing the system’s performance. As an integral component of the proposed approach, an analysis of CO2 emissions associated with energy losses is included, allowing a contextualized assessment of the environmental benefits of seasonal reconfiguration in various geographical areas.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Advancing Grid Resilience through Smart Charge Management: Findings from Maryland’s Pilot
This report presents research findings from a four-year Smart Charge Management (SCM) pilot program conducted by Maryland’s largest electric utilities—Baltimore Gas and Electric (BGE), Potomac Electric Power Company (Pepco), and Delmarva Power & Light (DPL)—to evaluate strategies for optimizing electric vehicle (EV) charging loads and enhancing grid stability. Supported by the U.S. Department of Energy (DOE), Argonne National Laboratory collaborated with all project partners and examined the effectiveness of Time-of-Use (TOU) and Load Balancing (LB) strategies in managing peak demand, deferring costly infrastructure upgrades, and reducing grid constraints at the feeder level. Using charging data from over 4,600 EV drivers, the study analyzed SCM’s impact on the distribution systems of BGE and Pepco, which consists of over 2000 feeders. Unlike prior research that focused on system-wide trends or synthetic feeders, this analysis offers granular, feeder-level insights based on real-world operational data. It highlights how transformer density, load profiles, and infrastructure constraints influence smart charging performance. Results show feeder-level conditions play a crucial role in SCM effectiveness, with most feeders benefiting more from LB, while TOU-based SCM may be sufficient for others. By 2035, LB reduced peak charging loads by 27% on average, compared to 23% under TOU-based SCM, though some feeders saw reductions exceeding 35%, while others experienced minimal impact. Feeders with higher transformer utilization and limited capacity benefited more from LB, which more effectively distributed charging demand during off-peak hours. Beyond reducing grid constraints, SCM offers long-term operational and financial benefits. By shifting EV charging demand strategically, utilities can optimize asset utilization, delay infrastructure investments, and enhance grid performance. In terms of infrastructure upgrade deferrals, at the feeder level, LB consistently reduced peak charging loads and resulting infrastructure upgrade costs, particularly in high EV enrollment areas, decreasing the number of overloaded transformers by up to 35%, while TOU-based SCM achieved 20-30% reductions depending on feeder characteristics. At the system level, LB has the potential to defer total upgrade costs by $\$$186 million for BGE, compared to $\$$159 million under TOU-based SCM. For Pepco, TOU-based SCM performed slightly better, deferring upgrade costs by $\$$30 million, compared to $\$$29 million under LB. Section 4.5 reviews some of the system differences between BGE and Pepco. However, as EV adoption scales, TOU-based SCM will introduce secondary peak charging loads, reinforcing the need for more advanced, adaptive SCM approaches to prevent new grid challenges. As EV adoption continues to grow, feeder-level managed charging strategies will be essential for mitigating grid stress, improving infrastructure efficiency, and maintaining energy affordability for consumers. This report provides critical insights for utilities, Public Utility Commissions (PUCs), and state agencies on the role of feeder-specific smart charging in infrastructure planning, policy development, and grid modernization. The findings underscore the importance of tailored, data-driven SCM solutions that align with local grid conditions, ensuring a resilient, cost-effective transition to increasing EV adoption while safeguarding distribution system performance.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Exploring the Model Design Space for Battery Health Management
Battery Health Management (BHM) is a core enabling technology for the success and widespread adoption of the emerging electric vehicles of today. Although battery chemistries have been studied in detail in literature, an accurate run-time battery life prediction algorithm has eluded us. Current reliability-based techniques are insufficient to manage the use of such batteries when they are an active power source with frequently varying loads in uncertain environments. The amount of usable charge of a battery for a given discharge profile is not only dependent on the starting state-of-charge (SOC), but also other factors like battery health and the discharge or load profile imposed. This paper presents a Particle Filter (PF) based BHM framework with plug-and-play modules for battery models and uncertainty management. The batteries are modeled at three different levels of granularity with associated uncertainty distributions, encoding the basic electrochemical processes of a Lithium-polymer battery. The effects of different choices in the model design space are explored in the context of prediction performance in an electric unmanned aerial vehicle (UAV) application with emulated flight profiles.
Saha, Bhaskar↗