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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 19 records
All sourcesNASA NTRSDOE OSTI
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↗
NASA NTRS2002

Bird's Eye View - A 3-D Situational Awareness Tool for the Space Station

Even as space-qualified computer hardware lags well behind the latest home computers, the possibility of using high-fidelity interactive 3-D graphics for displaying important on board information has finally arrived, and is being used on board the International Space Station (ISS). With the quantity and complexity of space-flight telemetry, 3-D displays can greatly enhance the ability of users, both onboard and on the ground, to interpret data quickly and accurately. This is particularly true for data related to vehicle attitude, position, configuration, and relation to other objects on the ground or in-orbit Bird's Eye View (BEV) is a 3-D real-time application that provides a high degree of Situational Awareness for the crew. Its purpose is to instantly convey important motion-related parameters to the crew and mission controllers by presenting 3-D simulated camera views of the International Space Station (ISS) in its actual environment Driven by actual telemetry, and running on board, as well as on the ground, the user can visualize the Space Station relative to the Earth, Sun, stars, various reference frames, and selected targets, such as ground-sites or communication satellites. Since the actual ISS configuration (geometry) is also modeled accurately, everything from the alignment of the solar panels to the expected view from a selected window can be visualized accurately. A virtual representation of the Space Station in real time has many useful applications. By selecting different cameras, the crew or mission control can monitor the station's orientation in space, position over the Earth, transition from day to night, direction to the Sun, the view from a particular window, or the motion of the robotic arm. By viewing the vehicle attitude and solar panel orientations relative to the Sun, the power status of the ISS can be easily visualized and understood. Similarly, the thermal impacts of vehicle attitude can be analyzed and visually confirmed. Communication opportunities can be displayed, and line-of-sight blockage due to interference by the vehicle structure (or the Earth) can be seen easily. Additional features in BEV display targets on the ground and in-orbit, including cities, communication sites, landmarks, satellites, and special sites of scientific interest for Earth observation and photography. Any target can be selected and tracked. This gives the user a continual line-of-sight to the target of current interest, and real-time knowledge about its visibility. Similarly, the vehicle ground-track, and an option to show "visibility circles" around displayed ground sites, provide continuous insight regarding current and future visibility to any target BEV was designed with inputs from many disciplines in the flight control and operations community both at NASA and from the International Partners. As such, BEV is setting the standards for interactive 3-D graphics for spacecraft applications. One important contribution of BEV is a generic graphical interface for camera control that can be used for any 3-D applications. This interface has become part of the International Display and Graphics Standards for the 16-nation ISS partnership. Many other standards related to camera properties, and the display of 3-D data, also have been defined by BEV. Future enhancements to BEV will include capabilities related to simulating ahead of the current time. This will give the user tools for analyzing off-nominal and future scenarios, as well as for planning future operations.

Dershowitz, Adam↗
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

Electric Vehicle Charging Demand in the Chicago Metropolitan Area through 2030

This report outlines the collaborative efforts between Argonne National Laboratory and Exelon in advancing the Agent-Based Transportation Energy Analysis Model (ATEAM). Aligning with ComEd’s beneficial electrification plan, this study developed eight scenarios to access the temporal and spatial distribution of charging load and demand stemming from the widespread adoption of battery electric vehicles (BEV) adoption, augmented public charging infrastructure deployment, and increased multi-unit dwelling (MUD) charging availability. Enhancements to the ATEAM model encompassed the simulation of multiple days of travel behavior, estimation of total public charging infrastructure needs, user interface refinements, and output tracking at both vehicle and charging station levels. The total electricity consumption for residential and public charging to support over 800,000 BEVs in Chicago in 2029 is projected at approximately 10.2 GWh. Enhanced MUD home charging accessibility (70%) amplifies the home charging load in the study area by 1.5% compared to the baseline scenario (10%). The widespread adoption of BEVs reduces peak charging loads, owing to their inclusion across households with diverse income levels, thus fostering a more dispersed charging activity pattern. However, widespread BEV adoption increases the peak home charging load in areas with lower median household incomes, reflecting a higher BEV concentration in these locales and, subsequently, heightened peak charging demands. In the Widespread BEV adoption scenario, fewer census tracts exhibit elevated peak loads for combined home and public charging, indicating a more even distribution of charging demand across the study area. Predominantly, peak loads for combined charging—both home and public— occur between 2 p.m. and 10 p.m. across all scenarios, encompassing the majority of census tracts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Total cost of ownership of vehicle electrification and fuel switching options for light-duty and heavy-duty vehicles

Projecting the transition from combustion engines to battery-based powertrains is complex becuase it involves numerous interdependent decisions. This study estimates total cost of ownership (TCO) to assess the economic viability of powertrain electrification, focusing exclusively on advances in vehicle and fuel technologies. Under two bounding technology-progress scenarios, we develop vehicle designs and fuel cost trajectories, which serve as inputs to TCO projections for selected classes from 2021 to 2050. We analyzed a small sport utility vehicle (SUV) to represent the light-duty vehicle (LDV) sector, and four medium- and heavy-duty vehicle (MHDV) classes: Class 6 box delivery, Class 8 drayage, Class 8 long-haul, and Class 8 transit bus. For each class, we compared the TCO of battery electric vehicles (BEVs) and fuel cell hybrid electric vehicles (FCHEVs) against conventional internal combustion engine vehicles (ICEVs). The results show that modern ICEVs generally have lower TCO; however, BEVs and FCHEVs could match or have lower TCOs than ICEVs over time, depending on technological progress. In LDVs, BEV300 is projected to deliver the lowest TCO by 2050, particularly under the high-progress scenario. In MHDVs, both BEVs and FCHEVs could become more cost-competitive than ICEVs by 2050 in the high-progress case. Beyond these results, the findings suggest further investigation is warranted for BEV charging infrastructure, FCHEV hydrogen refueling infrastructure, and MHDV charging strategies. In conclusion, these factors could reduce the fuel-cost share of TCO and enhance the competitiveness of BEVs and FCHEVs relative to ICEVs.

Battery electric vehicle↗
DOE OSTI2025

Impact of Regional and Seasonal Characteristics on Battery Electric Vehicle Operational Costs in the U.S.

This study investigates the operational cost competitiveness of battery electric vehicles (BEVs) in the United States, considering regional climates, energy prices, and driving patterns. By comparing BEVs with plug-in hybrid electric vehicles (PHEVs), hybrid electric vehicles (HEVs), and the alternative use of BEVs and conventional vehicles (Convs), the analysis incorporates thermal dynamometer tests, real-world vehicle miles traveled (VMT), and state-specific energy prices. Using detailed simulations, the study evaluates energy consumption across varying temperatures and driving distances. The findings reveal that, while BEVs remain cost-effective for short trips in moderate climates, PHEVs are more economical for long-range trips and cold environments, due to the excessive cost of using external direct current fast chargers (DCFCs) and reduced BEV efficiency at low temperatures. HEVs are identified as the most cost-efficient option in regions like New England, characterized by high residential electricity prices. These insights are critical for shaping vehicle electrification strategies, particularly under diverse regional and seasonal conditions, and for advancing policies on alternative energy and fuels.

Kim, Kyung-Ho (ORCID:0009000450851732)↗
DOE OSTI2024

TCO Analysis Approach and Regional Analysis of dWPT for Class 8 Tractors

Dynamic Wireless Power Transfer (dWPT) is a method by which battery electric vehicles (BEVs) can charge their battery while traveling on the road without the need for a physical conductive connection to the power source. dWPT has been proposed as a strategy to enable a reduction in vehicle battery capacity and associated mass and cost. In this slide deck presented at the EVs@Scale Consortium - Wireless Power Transfer Pillar Deep-Dive Meeting on November 11th, 2023, NREL provides results from an evaluation of dWPT using data from Class 8 tractors driving in the Atlanta Metro Area. NREL selected data for archetypal days representing local, regional, and long-haul trips, defined according to trip length, that included travel on primary roadways. EVI-InMotion (Electric Vehicle Infrastructure - InMotion), a systems planning and optimization tool developed at NREL, was used to evaluate dWPT performance assuming dWPT charging on 120 road segments for a total roadway lane distance of 2,365 miles. The EVI-InMotion results and representative day drive cycles were analyzed with NREL's T3CO (Transportation Technology Total Cost of Ownership) tool to estimate the total cost of ownership (TCO) for scenarios comprising two model years - 2030 and 2040 - and two technology progress cases. TCO was calculated for diesel, fuel cell electric, BEVs with batteries sized assuming no dWPT capabilities, and 200kWh BEVs with dWPT installed. This analysis finds that en-route stationary charging frequency and downtime when not on electrified roadways are the main contributors to TCO for the dWPT vehicles and that these vehicles can achieve cost parity with FCEVs at low electricity costs. Based on the scenario assumptions used here, low electricity costs would further help the cost parity with diesel vehicles in regional and long-haul cases due to stationary fueling downtime. This presentation also concludes that key factors affecting the parity potential of dWPT-capable vehicles include more extensive dWPT road coverage, higher en-route charging power, less expensive power batteries, and higher hydrogen or diesel fuel costs.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗