UPLIFTS: Urban Platooning and Logistics in Future Transportation Systems — Phase I Final Report
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29 ENERGY PLANNING, POLICY, AND ECONOMY↗
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Results for “fleet managers”
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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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 and Emission Prediction for Mixed-Vehicle Transit Fleets Using Multi-task and Inductive Transfer Learning
Public transit agencies are focused on making their fixed-line bus systems more energy efficient by introducing electric (EV) and hybrid (HV) vehicles to their fleets. However, because of the high upfront cost of these vehicles, most agencies are tasked with managing a mixed-fleet of internal combustion vehicles (ICEVs), EVs, and HVs. In managing mixed-fleets, agencies require accurate predictions of energy use for optimizing the assignment of vehicles to transit routes, scheduling charging, and ensuring that emission standards are met. The current state-of-the-art is to develop separate neural network models to predict energy consumption for each vehicle class. Although different vehicle classes’ energy consumption depends on a varied set of covariates, we hypothesize that there are broader generalizable patterns that govern energy consumption and emissions. In this paper, we seek to extract these patterns to aid learning to address two problems faced by transit agencies. First, in the case of a transit agency which operates many ICEVs, HVs, and EVs, we use multi-task learning (MTL) to improve accuracy of forecasting energy consumption. Second, in the case where there is a significant variation in vehicles in each category, we use inductive transfer learning (ITL) to improve predictive accuracy for vehicle class models with insufficient data. As this work is to be deployed by our partner agency, we also provide an online pipeline for joining the various sensor streams for fixed-line transit energy prediction. Here, we find that our approach outperforms vehicle-specific baselines in both the MTL and ITL settings.
97 MATHEMATICS AND COMPUTING↗
Grid-Aware Charging and Operational Optimization for Mixed-Fleet Public Transit
The rapid growth of urban populations and the increasing need for sustainable transportation solutions have prompted a shift towards electric buses in public transit systems. However, the effective management of mixed fleets consisting of both electric and diesel buses poses significant operational challenges. One major challenge is coping with dynamic electricity pricing, where charging costs vary throughout the day. Transit agencies must optimize charging assignments in response to such dynamism while accounting for secondary considerations such as seating constraints. This paper presents a comprehensive mixed-integer linear programming (MILP) model to address these challenges by jointly optimizing charging schedules and trip assignments for mixed (electric and diesel bus) fleets while considering factors such as dynamic electricity pricing, vehicle capacity, and route constraints. We address the potential computational intractability of the MILP formulation, which can arise even with relatively small fleets, by employing a hierarchical approach tailored to the fleet composition. By using real-world data from the city of Chattanooga, Tennessee, USA, we show that our approach can result in significant savings in the operating costs of the mixed transit fleets.
Sen, Rishav↗
Advancing Federal Infrastructure Through Innovation
This presentation reviews NREL tools used to support federal fleet and facility managers. It also discusses cybersecurity considerations regarding EVSE.
ADVANCED PROPULSION SYSTEMS↗
Crew member and instructor evaluations of line oriented flight training
Results obtained from the NASA/UT/LOFT survey of 8300 crew members from four airlines is presented. As simulator training is very expensive and excellence in training is the objective, some effort is justified in evaluating LOFT and in determining what it is about the best scenarios that creates positive effects. Attention is given to the effects of different scenarios, self reports of crew resource management behaviors, organization, fleet and crew position differences.
Wilhelm, John↗
FY 2021 Site Sustainability Plan
The U.S. Department of Energy's (DOE's) National Renewable Energy Laboratory (NREL) Fiscal Year 2021 Site Sustainability Plan details the laboratory's current performance status relative federal mandates and goals pertaining to water, waste, fleet, and energy management; clean and renewable energy; green buildings; acquisition and procurement; measures, funding, and training; electronic stewardship; and organizational resilience. It also provides a road map for future site planning and development that support NREL's commitment to business operations that 1) demonstrate the incorporation of clean energy practices and 2) promote the development of a sustainable and resilient campus while growing greater technological capabilities to advance the national renewable energy marketplace.
ENERGY PLANNING, POLICY, AND ECONOMY↗
EVs@Scale Next-Gen Profiles - Fleet Utilization 2023
As U.S. fleet operators begin transitioning to electric vehicles (EVs), critical questions arise regarding how to manage this shift without disrupting fleet operations or placing undue stress on the electric grid. A major challenge for fleets is maintaining effective operational schedules while accommodating charging requirements, particularly with high-power charging (HPC) infrastructure, which presents grid stability concerns for utilities. Proposed solutions such as charging substations, megawatt charging systems (MCS), and smart charge management systems (SCMS) offer potential pathways forward, but their effectiveness depends on alignment with real-world fleet behavior and operational constraints. This report investigates the charging and utilization behavior of EV and EVSE fleets actively employing HPC technologies by conducting detailed case study analyses based on telematics data. A suite of predefined metrics—covering charging, routing, and other operational behaviors—is developed to evaluate the impact of fleet activities on grid infrastructure and identify opportunities for optimization. Results highlight variations in charging behavior across fleets, such as weekday versus weekend usage, diurnal charging trends, and the role of operational predictability in enabling SCMS effectiveness. While SCMS can help lower costs and improve energy efficiency for fleets with stable schedules, they may be insufficient for fleets with highly variable or long-haul operations, which may require more robust solutions like MCS. Visualization of aggregated hourly energy metrics reveals that while fleet behaviors are diverse, there are common temporal patterns that could inform infrastructure planning and energy management. These insights emphasize the need for fleet-specific charging strategies that minimize grid impact while supporting reliable fleet operations. Additionally, the report underscores the broader economic stakes of electrification, particularly in high-value markets such as freight, where misaligned transitions could stall EV adoption. By examining current EV and EVSE fleet deployments using predetermined standardized metrics, this study offers a foundation for developing technologies and operational frameworks that support scalable, grid-compatible electrification across a variety of fleet types while establishing a baseline understanding of operational behaviors. In doing so, we aim to ensure that future charging solutions reflect actual fleet needs and grid constraints—an essential step toward maintaining operational continuity and achieving a successful transition to electric fleet operations.
Charging↗
X-33/RLV System Health Management/Vehicle Health Management
To reduce operations costs, Reusable Launch Vehicles (RLVS) must include highly reliable robust subsystems which are designed for simple repair access with a simplified servicing infrastructure, and which incorporate expedited decision-making about faults and anomalies. A key component for the Single Stage To Orbit (SSTO) RLV system used to meet these objectives is System Health Management (SHM). SHM incorporates Vehicle Health Management (VHM), ground processing associated with the vehicle fleet (GVHM), and Ground Infrastructure Health Management (GIHM). The primary objective of SHM is to provide an automated and paperless health decision, maintenance, and logistics system. Sanders, a Lockheed Martin Company, is leading the design, development, and integration of the SHM system for RLV and for X-33 (a sub-scale, sub-orbit Advanced Technology Demonstrator). Many critical technologies are necessary to make SHM (and more specifically VHM) practical, reliable, and cost effective. This paper will present the X-33 SHM design which forms the baseline for the RLV SHM, and it will discuss applications of advanced technologies to future RLVs. In addition, this paper will describe a Virtual Design Environment (VDE) which is being developed for RLV. This VDE will allow for system design engineering, as well as program management teams, to accurately and efficiently evaluate system designs, analyze the behavior of current systems, and predict the feasibility of making smooth and cost-efficient transitions from older technologies to newer ones. The RLV SHM design methodology will reduce program costs, decrease total program life-cycle time, and ultimately increase mission success.
Mouyos, William↗
Lithium Battery Power Delivers Electric Vehicles to Market
Hybrid Technologies Inc., a manufacturer and marketer of lithium-ion battery electric vehicles, based in Las Vegas, Nevada, and with research and manufacturing facilities in Mooresville, North Carolina, entered into a Space Act Agreement with Kennedy Space Center to determine the utility of lithium-powered fleet vehicles. NASA contributed engineering expertise for the car's advanced battery management system and tested a fleet of zero-emission vehicles on the Kennedy campus. Hybrid Technologies now offers a series of purpose-built lithium electric vehicles dubbed the LiV series, aimed at the urban and commuter environments.
Source record↗
Management of reliability and maintainability; a disciplined approach to fleet readiness
Material acquisition fundamentals were reviewed and include: mission profile definition, stress analysis, derating criteria, circuit reliability, failure modes, and worst case analysis. Military system reliability was examined with emphasis on the sparing of equipment. The Navy's organizational strategy for 1980 is presented.
Willoughby, W. J., Jr.↗
X-33/RLV System Health Management/ Vehicle Health Management
To reduce operations cost, the RLV must include the following elements: highly reliable, robust subsystems designed for simple repair access with a simplified servicing infrastructure and incorporating expedited decision making about faults and anomalies. A key component for the Single Stage to Orbit (SSTO) RLV System used to meet these objectives is System Health Management (SHM). SHM deals with the vehicle component- Vehicle Health Management (VHM), the ground processing associated with the fleet (GVHM) and the Ground Infrastructure Health Management (GIHM). The objective is to provide an automated collection and paperless health decision, maintenance and logistics system. Many critical technologies are necessary to make the SHM (and more specifically VHM) practical, reliable and cost effective. Sanders is leading the design, development and integration of the SHM system for RLV and X-33 SHM (a sub-scale, sub-orbit Advanced Technology Demonstrator). This paper will present the X-33 SHM design which forms the baseline for RLV SHM. This paper will also discuss other applications of these technologies.
Garbos, Raymond J.↗
Collaborative Arrival Planning: Data Sharing and User Preference Tools
Air traffic growth and air carrier economic pressures have motivated efforts to increase the flexibility of the air traffic management process and change the relationship between the air traffic control service provider and the system user. One of the most visible of these efforts is the U.S. government/industry "free flight" initiative, in which the service provider concentrates on safety and cross-airline fairness, and the user on their business objectives and operating preferences, including selecting their own path and speed in real-time. In the terminal arrival phase of flight, severe restrictions and rigid control are currently placed on system users, typically without regard for individual user operational preferences. Airborne delays applied to arriving aircraft into capacity constrained airports are imposed on a first-come, first-serve basis, and thus do not allow the system user to plan for or prioritize late arrivals, or to economically optimize their arrival sequence. A central tenant of the free-flight operating paradigm is collaboration between service providers and users in reaching air traffic management decisions. Such collaboration would be particularly beneficial to an airline's "hub" operation, where off-schedule arrival aircraft are a consistent problem, as they cause serious air-port ramp difficulties, rippling airline scheduling effects, and result in large economic inefficiencies. Greater collaboration can also lead to increased airport capacity and decrease the severity of over-capacity rush periods. In the NASA Collaborative Arrival Planning (CAP) project, both independent exchange of real-time data between the service provider and system user and collaborative decision support tools are addressed. Data exchange of real-time arrival scheduling, airspace management, and air carrier fleet data between the FAA service provider and an air carrier is being conducted and evaluated. Collaborative arrival decision support tools to allow intra-airline arrival preferences are being developed and simulated. The CAP project is part of and leveraged from the NASA/FAA Center TRACON Automation System (CTAS), a fielded set of decision support tools that provide computer generated advisories for both enroute and terminal area controllers to manage and control arrival traffic more efficiently. In this paper, the NASA Collaborative Arrival Planning project is outlined and recent results detailed, including the real-time use of CTAS arrival scheduling data by a major air carrier and simulations of tactical and strategic user preference decision support tools.
Zelenka, Richard E.↗
PHYSICS-BASED AUTOMATED REASONING FOR HEALTH MONITORING: SENSOR SET SELECTION
This paper addresses the problem of how to select a sensor set for equipment health monitoring that meets the needs of advanced O&M tasks that target cost reduction. They include maintenance optimization and asset management for the existing fleet and near-autonomous operation as currently envisioned for advanced reactors. The method uses physics-based automated reasoning to provide for a more “explainable” diagnosis. The algorithm is described along with its implementation on a computational cluster. Preliminary results for application to a use case in the current fleet are described.
diagnosis↗
Automatic Rural Road Centerline Detection and Extraction from Aerial Images for a Forest Fire Decision Support System
To effectively manage the terrestrial firefighting fleet in a forest fire scenario, namely, to optimize its displacement in the field, it is crucial to have a well-structured and accurate mapping of rural roads. The landscape’s complexity, mainly due to severe shadows cast by the wild vegetation and trees, makes it challenging to extract rural roads based on processing aerial or satellite images, leading to heterogeneous results. This article proposes a method to improve the automatic detection of rural roads and the extraction of their centerlines from aerial images. This method has two main stages: (i) the use of a deep learning model (DeepLabV3+) for predicting rural road segments; (ii) an optimization strategy to improve the connections between predicted rural road segments, followed by a morphological approach to extract the rural road centerlines using thinning algorithms, such as those proposed by Zhang–Suen and Guo–Hall. After completing these two stages, the proposed method automatically detected and extracted rural road centerlines from complex rural environments. This is useful for developing real-time mapping applications.
Lourenço, Miguel (ORCID:0000000157673394)↗
Federal Energy Management Program
The Federal Energy Management Program (FEMP) helps federal agencies manage their building and fleet energy use by providing training, tools, technical assistance, and funding.
DOE↗
EVs-at-RISC: A Secure and Resilient Interoperable SCM Control System Architecture for Electric Vehicle’s-at-Scale (Final Technical Report)
The EVs-at-RISC project was a five-year research, development, and demonstration initiative to create foundational tools for utility-scale fleet aggregation and Smart Charge Management (SCM) of Electric Vehicles (EV), Electric Vehicle Charging Infrastructure (EVCI), and related Distributed Energy Resources (DER). Rather than seeking to develop and demonstrate highly perfected SCM algorithms and control strategies, this project instead focused on creating foundational software solutions that enable unprecedented digital interoperability across the communications technologies and vendor platforms used to manage EV , EVCI, and DER, as well as existing energy management infrastructure operated by utilities, grid operators, and aggregators. This project then extends these novel interoperability capabilities to develop and deploy powerful middleware abstractions across grid edge networks and EVCI/DER fleet aggregations incorporating modern software tools and best practices, such as CI/CD, to bring the immense capabilities of infrastructure-as-code and policy-as-code to modern grid edge network environments. This addresses the foremost systemic issues preventing realization of any net operational benefits from scaled deployment of behind-the-meter EV, EVCI, and DER assets in electric power grids and markets today. The results of this approach and project unlock massive potential for new SCM capabilities to be easily prototyped, evaluated, and deployed at-scale within the existing grid edge network infrastructure and EVCI/DER technology ecosystem. The EVs-at-RISC project achieves this by extending Open Field Message Bus (OpenFMB), a conceptual model for digital interoperability and distributed intelligence in traditional front-of-meter utility SCADA networks, validating our hypothesis that OpenFMB could be similarly used to solve systemic digital interoperability issues in behind-the-meter environments and unlock real-world utility-scale SCM capabilities without requiring any new proprietary vendor solutions or significant infrastructure reconfiguration.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Depot Charging Schedule Optimization for Medium- and Heavy-Duty Battery-Electric Trucks
Charge management, which lowers charging costs for fleets and prevents straining the electrical grid, is critical to the successful deployment of medium- and heavy-duty battery-electric trucks (MHD BETs). This study introduces an energy demand and cost management framework that optimizes depot charging for MHD BETs by combining an energy consumption machine learning model and a linear program optimization model. The framework considers key factors impacting real-world MHD BET operations, including vehicle and charger configurations, duty cycles, use cases, geographic and climate conditions, operation schedules, and utilities’ time-of-use (TOU) rates and demand charges. The framework was applied to a hypothetical fleet of 100 MHD BETs in California under three different utilities for 365 days, with results compared to unmanaged charging. The optimized charging solution avoided more than 90% of on-peak charging, reduced fleet charging peak load by 64–75%, and lowered fleet energy variable costs by 54–64%. This study concluded that the proposed charge management framework significantly reduces energy costs and peak loads for MHD BET fleets while making recommendations for fleet electrification infrastructure planning and the design of utility TOU rates and demand charges.
Song, Shuhan↗