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Modeled Electricity Demand Profiles for Federal, State, and Municipal Electric Vehicle Fleets in the United States

Federal, state, and municipal electric vehicle fleet hourly load datasets at the Uber H3 hex, county, and city resolutions, as described in Singer et al. (2025). Please cite as: Singer, Mark, Cabell Hodge, Kara Podkaminer, and Brennan Borlaug. 2025. Hourly Load Profile Dataset for Federal, State, and Municipal Electric Vehicle Fleets in the United States. Golden, CO: National Renewable Energy Laboratory. NREL/TP-5400-92142. https://www.nlr.gov/docs/fy25osti/92142.pdf

24 POWER TRANSMISSION AND DISTRIBUTION

Usability Evaluation of Fleet Management Interface for High Density Vertiplex Environments

To meet the rising demand for an Advanced Air Mobility (AAM) (i.e. urban and rural unmanned aircraft systems) ecosystem, the NASA Aeronautics Research Mission Directorate (ARMD) is hosting a series of simulations and flight tests under the High Density Vertiplex sub-project (HDV) to prototype and study the effectiveness AAM capabilities under various operational contexts. HDV aims to develop an integrated automation architecture to support terminal area flight operations. The HDV simulations and flight tests address safety, integration, and operational challenges, while integrated systems and software demonstrate design readiness, robustness, and interoperability. During the initial HDV simulation in 2021, a prototype traffic management tool developed by NASA called the Fleet Management Interface (FMI) was tested. FMI was designed to introduce an advanced level of human-automation interaction to aid both Ground Control Station Operators (GCSOs) and Fleet Managers (FMs) in remotely managing flights under their ownership. In a human-in-the-loop simulation, a usability study was conducted with the FMI to identify optimal approaches for displaying information to human operators using subjective measures of usability, workload, situation awareness, risk, and trust, along with qualitative feedback. This study consisted of task analysis in which GCSO and FM subjects used an Urban Air Mobility (UAM) environment to develop and execute a plan for two different traffic scenarios of remotely controlled vehicles. In each scenario, a remotely controlled vehicle completed a takeoff, active flight, and landing sequence while simulated traffic flew in the background at a rate of 20 operations per hour. In the first scenario, the controlled vehicle flew a nominal route with takeoff and landing at the same vertiport. In the second scenario, the controlled vehicle started on the nominal route, then diverted to an unplanned location mid-flight. Results showed that self- reported performance, usability, trust, and situation awareness ratings of FMI were moderately to strongly high. There were small differences between scenarios, with Scenario 2 being perceived as more unstable, complex, variable, risky, and potentially harmful than Scenario 1. Furthermore, participants described improvements that could be made to create a better user experience. For example, users suggested customizable interfaces to accommodate information display preferences, and the ability to review routes before assigning them. The results from this study will inform future development of the FMI with the end goal of creating a reference automation tool for airspace management procedures in AAM. The FMI could serve to reduce dependency on traditional air navigation services through increased automation in high density vertiplex environments.

Fleet manager

sUAS Ground Control Station Capabilities Impact on Fleet Management

Future Small Uncrewed Aerial Systems (sUAS) missions will require multiple operators to collaborate and manage large fleets of highly automated vehicles. It is critical that these operators maintain the necessary situational awareness to modify vehicle missions if/when unexpected situations arise in the operating environment. It has been suggested that highly automated vehicle capabilities can lead to reducing the capabilities given to operators to modify/direct vehicle missions. This paper explores various Ground Control Station (GCS) capabilities configurations and their impact on sUAS fleet management. Results show that a GCS with a combination of manual and automated capabilities allowed participants to make more effective decisions while maintaining workload and response times similar to that of a GCS with only automated capabilities.

sUAS

Progressive Development of Fleet Management Capabilities for a High Density Vertiplex Environment

The High Density Vertiplex (HDV) Sub-Project, as part of NASA’s Advanced Air Mobility (AAM) Project, has been developing a reference automation architecture with a far-term view of scalable, high-density operations in and around vertiport terminal areas. One of the components of that architecture under development has been focused on fleet management capabilities to support the management of multiple AAM operations from a supervisory role of a fleet manager. This capability relies on connectivity and information exchanges with other services for airspace and vertiport management as well as with flight crews responsible for operation execution. This paper will present this capability with a focus on its user interface developments as well as its integration into the simulation and flight testing performed as part of the HDV research roadmap.

AAM

Progressive Development of Fleet Management Capabilities for a High Density Vertiplex Environment

The High Density Vertiplex (HDV) Sub-Project, as part of NASA’s Advanced Air Mobility (AAM) Project, has been developing a reference automation architecture with a far-term view of scalable, high-density operations in and around vertiport terminal areas. One of the components of that architecture under development has been focused on fleet management capabilities to support the management of multiple AAM operations from a supervisory role of a fleet manager. This capability relies on connectivity and information exchanges with other services for airspace and vertiport management as well as with flight crews responsible for operation execution. This paper will present this capability with a focus on its user interface developments as well as its integration into the simulation and flight testing performed as part of the HDV research roadmap.

high density vertiport

Joint Optimization of Multimodal Transit Frequency and Shared Autonomous Vehicle Fleet Size with Hybrid Metaheuristic and Nonlinear Programming

Shared autonomous vehicles (SAVs) bring competition to traditional transit services but redesigning multimodal transit network can utilize SAVs as feeders to enhance service efficiency and coverage. This paper presents an optimization framework for the joint multimodal transit frequency and SAV fleet size problem, a variant of the transit network frequency setting problem. The objective is to maximize total transit ridership (including SAV-fed trips and subtracting boarding rejections) across multiple time periods under budget constraints, considering endogenous mode choice (transit, point-to-point SAVs, driving) and route selection, while allowing for strategic route removal by setting frequencies to zero. Due to the problem’s non-linear, non-convex nature and the computational challenges of large-scale networks, we develop a hybrid solution approach that combines a metaheuristic approach (particle swarm optimization) with nonlinear programming for local solution refinement. To ensure computational tractability, the framework integrates analytical approximation models for SAV waiting times based on fleet utilization, multimodal network assignment for route choice, and multinomial logit mode choice behavior, bypassing the need for computationally intensive simulations within the main optimization loop. Applied to the Chicago metropolitan area’s multimodal network, our method illustrates a 33.3% increase in transit ridership through optimized transit route frequencies and SAV integration, particularly enhancing off-peak service accessibility and strategically reallocating resources.

Ng, Max

Optimal Managed Fast-Charging Model for Electric Vehicle Fleets with High Utilization and Multiple Charge-Acceptance Curves

A predictive control/scheduling optimization model is proposed for managed charging of an electric vehicle (EV) fleet - under time-of-use energy and demand prices, high vehicle utilization frequency (short dwell times), multiple charge- acceptance curves (configurable charging rates), and flexible vehicle demand. This context is particularly relevant for flight schools (small electric aircraft) or other commercial facilities where an EV fleet performs multiple operating and fast-charging sessions on the same day. The proposed model performs both the operational and charging scheduling of the vehicles, which is not typically done for residential managed charging and significantly increases problem complexity. The problem is formulated as a MILP model and a case study of a small fast-charging station is presented. Results demonstrate a significant reduction in operating cost, mainly from peak shaving during high demand price periods, achieved by coordinating the operation of different vehicles, chargers and charging rates.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC

Energy-Transit Nexus Tools for Bus Fleet Electrification (NEXTBUS)

NEXTBUS is an open-source software project that integrates NLR's bus energy modeling and simulation tools with multi-objective optimization for fleet operations. NLR is collaborating with a transit technology startup, ReVolt, to commercialize these capabilities by deploying NEXTBUS in ReVolt's software platform. The goal is to manage the added complexities of running a heterogeneous fleet, encompassing battery electric and diesel buses, across a large, multi-depot transit network.

33 ADVANCED PROPULSION SYSTEMS

Fleet-Level Fuel Impact of Hybrid-Electric Aircraft in United States

Here, this paper examines the impact of two potential solutions for increasing fuel efficiency and reducing emissions in commercial aviation: hybrid-electric propulsion and the use of drop-in synthetic aviation fuels (SAFs). The authors modeled three representative aircraft employed in the U.S. domestic market: a 70-seat regional turboprop, a 100-seat airliner, and a 180-seat airliner. These aircraft were retrofitted with hybrid-electric propulsion systems that integrate batteries and electric motors to provide additional torque to the propeller or fan. The authors explored various technological scenarios involving different battery specific energies, electric motor specific powers, and other relevant parameters. Flight performance models were used to analyze the range–payload capabilities of these new hybrid-electric aircraft and to compare them with their conventional counterparts. Subsequently, the authors virtually deployed the hybrid-electric aircraft on 2019 U.S. domestic commercial flights to assess the types and lengths of flights that could be serviced by this new fleet. We then compared the resulting fuel consumption, energy use, and emissions with those of a conventional fleet using a combination of jet fuel and SAF.

SAF

Fleet-Level Energy and Emissions Analysis of the US Off-Road Sector with VISION: Off-Road

In the United States (US), the off-road sector (i.e., agriculture, construction, etc.) contributes to approximately 10% of the country’s transportation greenhouse gas (GHG) emissions, similar to the aviation sector. The off-road sector is extremely diverse; as the EPA MOVES model classifies it into 11 sub-sectors, which include 85 different types of equipment. These equipment types have horsepower ranging from 1 to greater than 3000 and have very different utilization, which makes decarbonization a complex endeavor. To address this, Argonne’s on-road vehicle fleet model, VISION, has been expanded to the off-road sector. The GHG emission factors for several energy carriers (biofuels, electricity, and hydrogen) have been incorporated from Argonne’s GREET model for a sector-wide well-to-wheel (WTW) GHG emissions analysis of the present and future fleet. Several technology adoption and energy decarbonization scenarios were modeled to better understand the appropriate actions required to drive towards net-zero emissions of the off-road sector. Results show that WTW decarbonization up to 67% can be achieved from 2023 to 2050 in a business-as-usual scenario. But with aggressive sales increases of electric and hydrogen powertrains, WTW decarbonization up to 77% can be achieved, which can further increase to 85% if electricity production is aggressively decarbonized by 2035.

lifecycle

NASA space transportation plans and roles of a mixed fleet

An account is given of the results of a September, 1987 NASA 'mixed fleet' launching resources/scheduling study that predicated capability projections on a fleet of available ELVs. The launch capability projections extended as far as 1995, and in their later phases encompassed the resumption of Space Shuttle operations and the availability of Orbital Maneuvering Vehicle, which could accomplish such things as the reboosting of the Hubble Space Telescope in 1991, and even the Shuttle-Derived Vehicle, of which two distinct concepts are presently being considered; both would heavily rely on Space Shuttle hardware, but would be unmanned.

Branscome, Darrell R.

Launch site integration for mixed fleet operations

Launch site impacts and integration planning issues are presented to support launch operations for a mixed vehicle fleet (manned and cargo). Proposed ground systems and launch site configurations are described. Prelaunch processing scenarios and schedules are developed for candidate launch vehicles. Earth-to-orbit (ETO) vehicle architectures are presented to meet future launch requirements, including the Space Exploration Initiative (SEI). Flight vehicle design recommendations to enhance launch processing are discussed. The significance of operational designs for future launch vehicles is shown to be a critical factor in planning for mixed fleet launch site operations.

Scott, L. P.

Options in the global change fleet architecture provided by the presence of an EOS-A and -B

The baseline architecture of the Global Change Technology Initiative (GCTI) fleet was established by selecting and designing spacecraft and instruments to meet the science requirements developed under the task 1 effort. While attempting to meet the temporal sampling portion of the science requirements, no consideration was given to the presence of the proposed Earth Observing System (EOS) Spacecraft that would be making many of the same measurements with many of the same instruments. After establishing the GCTI baseline independent of the EOS Spacecraft; however, it is now prudent to examine the impact of the presence of the EOS Spacecraft on the GCTI fleet. A small scope, GCTI study supplement was accomplished to assess the impact. The content and results of the supplementary study are presented.

Hypes, Warren D.

Lessons from cross-fleet/cross-airline observations - Evaluating the impact of CRM/LOFT training

A review is presented of the crew resource management/line oriented flight training (CRM/LOFT) program to help determine the level of standardization across fleets and airlines in the critical area of evaluating crew behavior and performance. One of the goals of the project is to verify that check airmen and LOFT instructors within organizations are evaluating CRM issues consistently and that differences observed between fleets are not a function of idiosyncracies on the part of observers. Attention is given to the research tools for crew evaluation.

Butler, Roy E.

Optimal selection of space transportation fleet to meet multi-mission space program needs

A space program that spans several decades will be comprised of a collection of missions such as low earth orbital space station, a polar platform, geosynchronous space station, lunar base, Mars astronaut mission, and Mars base. The optimal selection of a fleet of several recoverable and expendable launch vehicles, upper stages, and interplanetary spacecraft necessary to logistically establish and support these space missions can be examined by means of a linear integer programming optimization model. Such a selection must be made because the economies of scale which comes from producing large quantities of a few standard vehicle types, rather than many, will be needed to provide learning curve effects to reduce the overall cost of space transportation if these future missions are to be affordable. Optimization model inputs come from data and from vehicle designs. Each launch vehicle currently in existence has a launch history, giving rise to statistical estimates of launch reliability. For future, not-yet-developed launch vehicles, theoretical reliabilities corresponding to the maturity of the launch vehicles' technology and the degree of design redundancy must be estimated. Also, each such launch vehicle has a certain historical or estimated development cost, tooling cost, and a variable cost. The cost of a launch used in this paper includes the variable cost plus an amortized portion of the fixed and development costs. The integer linear programming model will have several constraint equations based on assumptions of mission mass requirements, volume requirements, and number of astronauts needed. The model will minimize launch vehicle logistic support cost and will select the most desirable launch vehicle fleet.

Morgenthaler, George W.

Mission Engineering of a Rapid Cycle Spacecraft Logistics Fleet

The requirement for logistics re-supply of the International Space Station has provided a unique opportunity for engineering the implementation of NASA's first dedicated pressurized logistics carrier fleet. The NASA fleet is comprised of three Multi-Purpose Logistics Modules (MPLM) provided to NASA by the Italian Space Agency in return for operations time aboard the International Space Station. Marshall Space Flight Center was responsible for oversight of the hardware development from preliminary design through acceptance of the third flight unit, and currently manages the flight hardware sustaining engineering and mission engineering activities. The actual MPLM Mission began prior to NASA acceptance of the first flight unit in 1999 and will continue until the de-commission of the International Space Station that is planned for 20xx. Mission engineering of the MPLM program requires a broad focus on three distinct yet inter-related operations processes: pre-flight, flight operations, and post-flight turn-around. Within each primary area exist several complex subsets of distinct and inter-related activities. Pre-flight processing includes the evaluation of carrier hardware readiness for space flight. This includes integration of payload into the carrier, integration of the carrier into the launch vehicle, and integration of the carrier onto the orbital platform. Flight operations include the actual carrier operations during flight and any required real-time ground support. Post-flight processing includes de-integration of the carrier hardware from the launch vehicle, de-integration of the payload, and preparation for returning the carrier to pre-flight staging. Typical space operations are engineered around the requirements and objectives of a dedicated mission on a dedicated operational platform (i.e. Launch or Orbiting Vehicle). The MPLM, however, has expanded this envelope by requiring operations with both vehicles during flight as well as pre-launch and post-landing operations. These unique requirements combined with a success-oriented schedule of four flights within a ten-month period have provided numerous opportunities for understanding and improving operations processes. Furthermore, it has increased the knowledge base of future Payload Carrier and Launch Vehicle hardware and requirement developments. Discussion of the process flows and target areas for process improvement are provided in the subject paper. Special emphasis is also placed on supplying guidelines for hardware development. The combination of process knowledge and hardware development knowledge will provide a comprehensive overview for future vehicle developments as related to integration and transportation of payloads.

Holladay, Jon

Aircraft Anomaly Detection Using Performance Models Trained on Fleet Data

This paper describes an application of data mining technology called Distributed Fleet Monitoring (DFM) to Flight Operational Quality Assurance (FOQA) data collected from a fleet of commercial aircraft. DFM transforms the data into aircraft performance models, flight-to-flight trends, and individual flight anomalies by fitting a multi-level regression model to the data. The model represents aircraft flight performance and takes into account fixed effects: flight-to-flight and vehicle-to-vehicle variability. The regression parameters include aerodynamic coefficients and other aircraft performance parameters that are usually identified by aircraft manufacturers in flight tests. Using DFM, the multi-terabyte FOQA data set with half-million flights was processed in a few hours. The anomalies found include wrong values of competed variables, (e.g., aircraft weight), sensor failures and baises, failures, biases, and trends in flight actuators. These anomalies were missed by the existing airline monitoring of FOQA data exceedances.

Gorinevsky, Dimitry

Three Component Velocity and Acceleration Measurement Using FLEET

The femtosecond laser electronic excitation and tagging (FLEET) method has been used to measure three components of velocity and acceleration for the first time. A jet of pure N2 issuing into atmospheric pressure air was probed by the FLEET system. The femtosecond laser was focused down to a point to create a small measurement volume in the flow. The long-lived lifetime of this fluorescence was used to measure the location of the tagged particles at different times. Simultaneous images of the flow were taken from two orthogonal views using a mirror assembly and a single intensified CCD camera, allowing two components of velocity to be measured in each view. These different velocity components were combined to determine three orthogonal velocity components. The differences between subsequent velocity components could be used to measure the acceleration. Velocity accuracy and precision were roughly estimated to be +/-4 m/s and +/-10 m/s respectively. These errors were small compared to the approx. 100 m/s velocity of the subsonic jet studied.

Danehy, Paul M.