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DOE OSTI2024

Delivering Clean Air in Denver: Propane Trucks and Infrastructure in Mail Delivery Application

In 2021, Drive Clean Colorado, a Denver-based Clean Cities coalition, initiated the deployment of five Class 6 medium-duty propane autogas delivery trucks into a United States Postal Service (USPS) mail delivery contractor fleet with the goal of providing a proof-of-concept demonstration for mail delivery fleets nationwide. Funded by the U.S. Department of Energy's Office of Energy Efficiency & Renewable Energy, the project, titled "Delivering Clean Air in Denver: Propane Trucks and Infrastructure in Mail Delivery Application," enabled the purchase and deployment of the propane-powered trucks, refueling infrastructure, and supporting analysis by the National Renewable Energy Laboratory (NREL). Key objectives of the analysis included calculating the propane vehicles' emissions reductions, analyzing the costs and operational performance of the propane fleet compared to conventional diesel-powered vehicles, and evaluating the viability of propane as a clean and cost-effective alternative to conventional diesel trucks (AFDC 2023b, 2023c). The primary goal of the project was to demonstrate alternative fuel vehicles in a mail transport application and evaluate the viability of propane as a clean and cost-effective alternative to conventional diesel trucks. This proof-of-concept demonstration is expected to lead to improved understanding of the performance attributes, costs, and operational issues to inform technology adoption decisions, helping to spur market transformation toward lower-emission truck fleets. By reducing the risk of first adoption, potential exists to transform the U.S. Postal Service (USPS) mail delivery system into a lower-carbon national fleet. This technical report details the vehicle duty cycle characterization and fleet cost and performance comparison performed by the National Renewable Energy Laboratory, in addition to estimated emissions reductions for delivery operations.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

Safe Operations at Roadway Junctions - Design Principles from Automated Guideway Transit

Herein this paper describes a system-level view of a fully automated transit system comprising a fleet of automated vehicles (AVs) in driverless operation, each with an SAE level 4 Automated Driving System, along with its related safety infrastructure and other system equipment. This AV system-level control is compared to the automatic train control system used in automated guideway transit technology, particularly that of communications-based train control (CBTC). Drawing from the safety principles, analysis methods, and risk assessments of CBTC systems, comparable functional subsystem definitions are proposed for AV fleets in driverless operation. With the prospect of multiple AV fleets operating within a single automated mobility district, the criticality of protecting roadway junctions requires an approach like that of automated fixed-guideway transit systems, in which a guideway switch zone "interlocking" at each junction location deconflicts railway traffic, affirming safe passage. The analogous AV protection safety subsystem is defined as fail-safe equipment that monitors roadway intersections and junctions, communicates traffic signal status, perceives and communicates alerts and signals to AV connected vehicles concerning potential unsafe conditions, and performs related primary safety functions. Conclusions are drawn that the AV protection roadway intersection functions must be performed by local roadside equipment dedicated to protecting each roadway intersection and junction. Further, it is concluded that the communications technology connecting the infrastructure with the vehicle to perform this vital, fail-safe protection should meet specific functional and performance criteria.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRSDate not supplied

Development of a Safety Hazards Risk Assessment Tool for Uncrewed Aircraft System Traffic Management during Preflight Planning

Tremendous growth in the uncrewed and remotely piloted vehicle market is expected in low-altitude, uncontrolled airspace, resulting in potential decreases in safety without systems that support monitoring, assessing, and mitigating risk. At NASA, the System-Wide Safety (SWS) project has been developing a suite of data-driven tools to predict hazards so that the potential risks that these hazards pose can be mitigated. Services to predict various hazards have been developed, including battery capacity, proximity to static obstacles, population risks, global positioning system signal strength, radio frequency spectrum interference risk, and vertiport congestion. These services can monitor hazards along a flight path and if any risks posed by these hazards exceed a threshold, the uncrewed aircraft system (UAS) fleet manager can be alerted to mitigate the risk by modifying the flight path, changing the scheduled departure or arrival times, and/or diverting the vehicle to an alternate vertiport. These services were originally developed to monitor and assess risks during flight, but they have been adapted to assess hazard risks prior to departure so that a fleet manager can evaluate the potential risks for a fleet of UAS along their planned flight paths. These services have been integrated into a prototype tool called the Supplemental Data Service Provider-Consolidated Dashboard (SDSP-CD), developed at NASA Ames Research Center. The tool consists of a dashboard which provides a comprehensive overview for a number of risks and a map display that shows the details of the hazards along each flight’s path. Based on the findings from three previous studies, the SDSP-CD has been updated with new design elements and functions. In this paper, we describe lessons learned from the previous studies, changes made to the interface, and the feedback received during a follow-up usability study. Overall, participants reported that there is a substantial benefit of having a fleet manager use a consolidated dashboard to assess hazards for the vehicles in their fleet and to provide situational awareness to potential risks so that they can be mitigated prior to flight. Once the SDSP-CD matures, it will need to be integrated into flight and mission planning tools. Some initial thoughts on how this integration should be accomplished are shared in this paper. Finally, the functional differences between preflight vs. in-flight risk assessment and the differences in fleet manager vs. UAS pilot roles that may require different information and user interactions are discussed.

preflight↗

DOE OSTI2024

Electrification Analysis: All Aboard America!

This one-page highlight details the key takeaways from a project that utilized NREL's Fleet Research, Energy Data, and Insights (FleetREDI) data analysis pipeline, an electrification analysis for the Bustang motorcoach fleet operated by All Aboard America! Holdings Inc. (AAA). NREL installed logging devices and collected operational data on nine 40-foot Bustang motorcoaches operating on fixed routes from May 2022 through August 2022. The analysis determined that partial fleet electrification may be feasible with electrified motorcoach options currently on the market. While this fleet faces significant challenges to electrification given current market options due to demanding range requirements and relatively limited charging opportunities, vehicles operating on the shorter, lower-grade routes along the I-25 corridor show more immediately available electrification potential. Increases in available battery capacity and the availability of fast-charging locations along I-70 routes are likely critical for electrification of the full fleet.

AAA↗

DOE OSTI2024

The health, climate, and equity benefits of freight truck electrification in the United States

Abstract Long-haul freight shipment in the United States relies on diesel trucks and constitutes ∼3% of U.S. greenhouse gas emissions and a significant share of local air pollution. Here, we compare the climate and air pollution-related health damages from electric versus diesel long-haul truck fleets. We use truck commodity flows to estimate tailpipe emissions from diesel trucks and regional grid emissions intensities to estimate charging emissions from electric trucks under various grid scenarios. We use a reduced complexity air quality model combined with valuation of air pollution-related premature deaths (using two hazard ratios (HRs)) and quantify the distributional health impacts in different scenarios. We find that annual health and climate costs of the current diesel fleet are $195–$249/capita compared to $174–$205/capita for a new diesel fleet, and $156–$177/capita for an electric fleet, depending on the HR. We find that freight electrification could avoid $6.2–8.5 billion in health and climate damages annually when compared to a fleet of new diesel vehicles (with even higher benefits when compared to the current diesel fleet). However, the Midwest and parts of the Gulf Coast would experience an increase in health damages due to vehicles charging using electricity from coal power plants. If old coal power plants (operating in 1980 or earlier) are replaced with zero-emission generation, electrification of all U.S. freight would result in $32.3–39.2 billion in avoided damages annually and health benefits throughout the U.S. Electrifying transport of consumer manufacturing goods (including electronics, transport equipment, and precision instruments) and food, beverage, and tobacco products would provide the largest absolute health and climate benefits, whereas mixed freight and manufacturing goods would result in the largest benefits per tonne-km. We find small variations in health damages across race and income. These results will help policymakers prioritize electrification and charging investment strategies for the freight transportation sub-sector.

Hennessy, Eleanor M. (ORCID:0000000294715765)↗

DOE OSTI2019

Stationary Spatial Charging Demand Distribution for Commercial Electric Vehicles in Urban Area

The electric vehicle (EV) is an increasingly popu- lar mobility solution today. It is especially appealing for urban commercial usage including serving commuters and delivering goods. Commercial EVs, defined as electric taxi fleets in this paper, contribute a lot to the energy consumption and emission. One critical component to facilitate the adoption of EVs is to provide sufficient charging infrastructures for road users to alleviate the range anxiety. In this study, we develop an analytical model to profile the stationary spatial distribution of charging demand for any given fleet of commercial EVs, which serves as the necessary input for planning the location of charging infrastructures. Our model considers the movement of EVs as a random walk, with the underlying Markov process driven by the spatial demand that the commercial EV fleet needs to serve. We then define the electricity consumption and charging models and develop the probability distribution that an EV may need to recharge its battery at any given location in the city, based on the stationary distribution of the random walk process. We calibrate the performance parameters of EVs from the trip record and present numerical experiments based on New York City taxi trip data and compare the solution from simulation with our analytical solution. The result shows that analytical solution is quite close to the simulation result. This paper provides insights to the city agencies and private companies to make charging infrastructure location planning.

Qian, Xinwu↗

DOE OSTI2020

Foundations of an Electric Mobility Strategy for the City of Mexicali

The Foundations of an Electric Mobility Strategy for the city of Mexicali aligns with numerous energy, environmental, and transport plans and will help Mexicali meet multiple related goals. Mexicali’s energy mix, with 28% renewables, already enables plugin electric vehicles (PEVs) to reduce the mass of greenhouse gases (GHGs) per km driven 2/3 below that of their conventional counterparts. This GHG benefit will increase should Mexicali take steps to further increase their share of renewables in their electricity supply. Beyond increasing renewables, Mexicali could possibly deploy PEVs so that electric load is added in the right location (depending on further analysis of substations and feeders) and at the right time (between 21:00 and 11:00) in order to minimize grid upgrade costs. There are a handful of charge timing control mechanisms –at various stages of development– that Mexicali could implement. Transport electrification can facilitate mass transit by powering buses, trains, and small vehicles that get people from their homes or work to the transit stations and vice versa. Mexicali could utilize fleets as early PEV adopters in order to gain acceptance and add electric vehicle supply equipment (EVSE). Recommended prioritization of different types of fleets are suggested in this report: transit buses, school buses, airport ground support equipment (GSE), refuse trucks, taxis, shuttle buses, campus vehicles, delivery trucks, utility trucks, and finally semitrailers. There are a handful of policy options that Mexicali could use to incentivize fleets to purchase PEVs, including mandates, economic incentives, energy performance contracts, waivers to access restrictions, electricity discounts, and EVSE requirements in building codes. Mexicali’s taxi fleet was an early adopter of PEVs and had experienced some challenges—mostly related to the insufficient range of the taxis due to hot weather. In this report, we strategize ways to extend the range of the current electric taxis, including ways to make charging more convenient to the drivers, and more suggestions for appropriate vehicles to purchase in the future. This report also includes the groundwork of geotracking Mexicali’s taxi fleet so that more detailed recommendations can be made in the future. Once fleets have increased PEV acceptance and EVSE installations, the market will be ready to expand to private vehicle owners. In order to do this, more EVSE needs to be installed in the right locations. This mobility strategy lays out general local areas where EVSE could be well utilized, based on traffic patterns, land use, and demographic data. Mexicali could approach businesses within these areas that would likely make suitable hosts, based on how well they can profit from the additional business that EVSE would bring. Mexicali could then adopt a series of purchase incentives (including sales tax waivers or access to high-occupancy vehicles [HOV] lanes) that would encourage private vehicle owners to purchase PEVs. Purchase incentives run the risk of creating equity issues, which can be countered by promoting electrification in mass transit, creating more HOV lanes that have PEV exemptions, and installing EVSEs in underserved communities. Private PEV ownership will require a set of experts that Mexicali can help train, including PEV repair technicians, EVSE installation electricians, and first responders.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Spatially and Temporally Resolved Velocimetry for Hypersonic Flows

The development of new hypersonic flight vehicles is limited by the physical understanding that may be obtained from ground test facilities. This has motivated the present development of a temporally and spatially resolved velocimetry measurement for Sandia National Laboratories (SNL) Hypersonic Wind Tunnel (HWT) using Femtosecond Laser Electronic Excitation Tagging (FLEET). First, a multi-line FLEET technique has been created for the first time and tested in a supersonic jet, allowing simultaneous measurements of velocities along multiple profiles in a flow. Secondly, two different approaches have been demonstrated for generating dotted FLEET lines. One employs a slit mask pattern focused into points to yield a dotted line, allowing for two- or three-component velocity measurements free of contamination between components. The other dotted-line approach is based upon an optical wedge array and yields a grid of points rather than a dotted line. Two successful FLEET measurement campaigns have been conducted in SNL’s HWT. The first effort established optimal diagnostic configurations in the hypersonic environment based on earlier benchtop reproductions, including validation of the use of a 267 nm beam to boost the measurement signal-to-noise ratio (SNR) with minimal risk of perturbing the flow and greater simplicity than a comparable resonant technique at 202 nm. The same FLEET system subsequently was reconstituted to demonstrate the ability to make velocimetry measurements of hypersonic turbulence in a realistic flow field. Mean velocity profiles and turbulence intensity profiles of the shear layer in the wake of a hypersonic cone model were measured at several different downstream stations, proving the viability of FLEET as a hypersonic diagnostic.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2022

Reducing Uncertainty of Fielded Photovoltaic Performance (Final Technical Report)

Improved analysis and reporting of photovoltaic (PV) field performance increases the certainty of owners and financiers that systems will perform as expected. Advanced module technologies (e.g., PERC, HJT, and bifacial) introduce new degradation mechanisms and performance characteristics. The FY19-21 Reducing Uncertainty project leveraged data from the ever-increasing PV fleet to develop models and understanding of the field performance of existing and new technologies. Specifically, we accomplished: report on field performance and degradation rates for high-efficiency silicon (HJT, PERC, IBC) and more conventional technologies; developed automated analysis techniques to quantify system performance (performance ratio, energy yield) and production shortfalls (soiling, degradation, availability); refined the RdTools software toolkit to bring standard, validated analysis techniques to bear on third-party data; analyzed and reported on large datasets including Treasury data and Lawrence Berkeley National Laboratory's Utility-Scale dataset to expand the high-quality degradation-rate histogram published previously; worked with industry partners and the DuraMAT data hub to enable private parties to share and aggregate PV production data anonymously, leveraging cloud-based data analysis infrastructure and publishing on US fleet-scale performance comprising over 7GW of operating systems. (https://www.nrel.gov/pv/fleet-performance-data-initiative.html). Through our industry collaborations we have engaged in NDA-covered data transfer with twelve PV fleet owners as of January 2022, with more agreements in negotiation. Our scalable cloud-based time series database contains over 30 billion rows (20TB) of PV time series data, representing over 1700 commercial and utility-scale systems, and over 7.2 GW of DC capacity (Fig 1). Initial field performance results have been distributed in several public reports. Because our fleet composition and data quality methods are continually improving, annual updates to these results are published to our PV Fleet webpage [ https://www.nrel.gov/pv/fleet-performance-data-initiative.html ] and DuraMAT data hub [DOI: 10.21948/1842958]. Another existing dissemination channel used for observed soiling losses is a map we maintain for soiling losses. Additional products developed include a report detailing fleet-wide performance index, availability, startup loss and snow loss factors, a detailed report on the 1603 grant dataset comprising over 100,000 PV systems with failure and performance details and a utility-scale report coauthored with LBNL on 31 GW of system performance.

14 SOLAR ENERGY↗

DOE OSTI2014

Development of a Short-Duration Drive Cycle to Represent Long-Term Measured Drive Cycle Data: Evaluation of Truck Efficiency Technologies in Class 8 Tractor Trailers

Quantifying the fuel savings and emissions reductions that can be achieved from truck fuel efficiency technologies for a fleet's specific usage allows the fleet to select a combination of technologies that will yield the greatest operational efficiency and profitability. An accurate characterization of usage for the fleet is critical for such an evaluation; however, short-term measured drive cycle data do not generally reflect overall usage very effectively. This study presents a detailed analysis of vehicle usage in a commercial vehicle fleet and demonstrates the development of a short-duration synthetic drive cycle with measured drive cycle data collected over an extended period of time. The approach matched statistical measures of the vehicle speed with acceleration history and integrated measured grade data to develop a compressed drive cycle that accurately represents total usage. Drive cycle measurements obtained during a full year from six tractor trailers in normal operations in a less-than-truckload carrier were analyzed to develop a synthetic drive cycle. The vehicle mass was also estimated to account for the variation of loads that the fleet experienced. These drive cycle and mass data were analyzed with a tractive energy analysis to quantify the benefits in terms of fuel efficiency and reduced carbon dioxide emissions that can be achieved on Class 8 tractor trailers by using advanced efficiency technologies, either individually or in combination. Although differences exist between Class 8 tractor trailer fleets, this study provides valuable insight into the energy and emissions reduction potential that various technologies can bring in this important trucking application. Finally, the methodology employed for generating the synthetic drive cycle serves as a rigorous approach to develop an accurate usage characterization that can be used to effectively compress large quantities of drive cycle data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

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↗

NASA NTRS1971

Integrated operations/payloads/fleet analysis. Volume 1: Summary, study overview

The preparation of payload input data for analysis, and the capture and cost analyses are described for the current fleet, a low cost expendable fleet, and the space shuttle/space tug fleet. The data preparation consisted of formatting, extending, and correcting the input data which were broken down in terms of satellite gross weight. The results of the analyses indicate a significant reduction in the required total number of new payload units for the space shuttle/space tug fleet. Another result is that 26% of the payloads for the new low cost expendable fleet and 26% for the space shuttle/space tug fleet are of low cost payload designs.

Source record↗

NASA NTRS2005

Tributary Analysis Monitoring System

A monitoring system for a fleet of vehicles includes at least one data acquisition and analysis module (DAAM) mounted on each vehicle in the fleet, a control module on each vehicle in communication with each DAAM, and terminal module located remotely with respect to the vehicles in the fleet. Each DAAM collects/analyzes sensor data to generate analysis results that identify the state of a plurality of systems of the vehicle. Each vehicle's control module collects/analyzes the analysis results from each onboard DAAM to generate vehicle status results that identify potential sources of vehicle anomalies. The terminal module collects/analyzes the analysis results and vehicle status results transmitted from each control module from the fleet of vehicles to identify multiple occurrences of vehicle anomalies and multiple occurrences of those vehicle systems operating at a performance level that is unacceptable. Results of the terminal module's analysis are provided to organizations responsible for the operation, maintenance and manufacturing of the vehicles in the fleet as well as the plurality of systems used in the fleet.

Stanley E Woodard↗

NASA NTRS2016

Femtosecond Laser Tagging Characterization of a Sweeping Jet Actuator Operating in the Compressible Regime

A sweeping jet (SWJ) actuator operating over a range of nozzle pressure ratios (NPRs) was characterized with femtosecond laser electronic excitation tagging (FLEET), single hot-wire anemometry (HWA) and high-speed/phase-averaged schlieren. FLEET velocimetry was successfully demonstrated in a highly unsteady, oscillatory flow containing subsonic through supersonic velocities. Qualitative comparisons between FLEET and HWA (which measured mass flux since the flow was compressible) showed relatively good agreement in the external flow profiles. The spreading rate was found to vary from 0.5 to 1.2 depending on the pressure ratio. The precision of FLEET velocity measurements in the external flow field was poorer (is approximately equal to 25 m/s) than reported in a previous study due to the use of relatively low laser fluences, impacting the velocity fluctuation measurements. FLEET enabled velocity measurements inside the device and showed that choking likely occurred for NPR ≥ 2.0, and no internal shockwaves were present. Qualitative oxygen concentration measurements using FLEET were explored in an effort to gauge the jet's mixing with the ambient. The jet was shown to mix well within roughly four throat diameters and mix fully within roughly eight throat diameters. Schlieren provided visualization of the internal and external flow fields and showed that the qualitative structure of the internal flow does not vary with pressure ratio and the sweeping mechanism observed for incompressible NPRs also probably holds for compressible NPRs.

Peters, Christopher J.↗

DOE OSTI2021

High-dimensional Data-driven Energy optimization for Multi-Modal Transit Agencies (HD-EMMA) (Final Technical Report)

Public bus transit services in the U.S. are responsible for at least 19.7 million metric tons of CO 2 emission annually. Electric vehicles (EVs) can have a much lower environmental impact than comparable internal combustion engine vehicles (ICEVs), especially in urban areas. Unfortunately, EVs are also much more expensive than ICEVs. As a result, many public transit agencies can afford only mixed fleets of transit vehicles, consisting of EVs, hybrids (HEVs), and ICEVs. Transit agencies that operate such mixed fleets of vehicles face a challenging optimization problem: these agencies need to decide which vehicles are assigned to serving which transit trips. Since the advantage of EVs over ICEVs varies depending on the route and time of day (e.g., the benefit of EVs is higher in slower traffic with frequent stops and lower on highways), the assignment can have a significant effect on energy use and, hence, environmental impact. Through this project, we have developed reference data about energy collections and constructed a set of machine learning models that can accurately predict the energy consumption for the whole fleet at the level of each trip. We have used these models to develop a scheduling and assignment strategy that can rotate the different vehicle types across the transit agencies’ routes. The optimization algorithm ensures that the vehicles are matched to trips considering weather patterns, expected congestion, and road gradients to minimize the overall energy usage. We list the key observations from our project for other practitioners below. Details are available in the report, and the list of source code and our publications are included in the appendix. 1. We have demonstrated the feasibility of collecting, merging and analyzing large volumes of high-resolution real-world telemetry data from a mixed vehicle fleet. To mitigate the inherent noise of the recorded GPS points, the team developed an algorithm that filters data and maps the points onto a street. The algorithm considers previous and subsequent location measurements and different characteristics of nearby streets to determine how likely the vehicle travels on them. Then, the team segmented the time series into disjoint contiguous samples based on adjacent road segments and repeated the outlier detection and removal. For each data point, the team added features corresponding to elevation changes within the samples, weather features, such as temperature, and traffic data, such as speed ratio between actual speed and free-flow speed. 2. We have developed two forms of machine learning models that be used to understand and analyze the energy operations of a mixed vehicle transit fleet. The micro prediction model provides estimates of instantaneous energy prediction for all types of buses (diesel, hybrid, and electric). Such a model is important in evaluating the energy impacts of real-time bus operation strategies, but it is challenging due to diversified driving cycles of transit buses. The model can help the drivers understand the impact of their driving behaviors and short-term congestions. The macro prediction models estimate average energy consumption across the whole trip considering the features: distance traveled, various road-type features, elevation change, day of the week, time of day, various weather features (temperature, humidity, etc.), and traffic features (speed ratio and jam factor). 3. We have demonstrated that it is possible to transfer the machine learning models we have developed in this project to other teams and cities by using inductive transfer learning. We also showed that the performance of the macro energy prediction models can be improved using a multi-task learning approach where the learning parameters are shared between the models being developed for different vehicle types. The advantage of this approach is improved learning performance as the models can exploit common spatio-temporal and environmental characteristics. 4. Finally, we have developed trip and vehicle assignment and scheduling algorithms that use the energy prediction models and develop a trip to vehicle type (diesel, electric, hybrid) assignment for the whole operation to reduce overall emissions and cost. We have shown through simulations that the proposed algorithms can save $\$$ 48,910 in energy costs and 175 metric tons of CO 2 emission annually for CARTA.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Federal Home-to-Work Electric Vehicle Program Guide

This document serves as a comprehensive resource for Federal agencies in developing their own program resources that promote the efficient and effective use of electric vehicles (EVs) for home-to-work travel while ensuring compliance with Federal regulations and sustainability objectives. One mission of the U.S. Department of Energy's Federal Energy Management Program (FEMP) Fleet program is to help federal fleet managers meet or exceed statutory requirements related to energy and environmental performance while improving overall fleet efficiency, reducing costs, and meeting mission requirements. To further this mission, FEMP provides resources to support Federal agencies with increasing alternative fuel vehicle (AFV) acquisitions and reducing petroleum use. EVs are AFVs and help agencies meet federal fleet requirements. Federal fleets include government-owned EVs used for home-to-work travel. The purpose of this document is to serve as a guide for Federal agencies in developing their own internal program documents to manage government-owned EVs used for home-to-work travel. Federal agencies should consult their counsel and consider their own policies and authorities in the implementation of any policies or best practices regarding government-owned EVs used for home-to-work travel. The guide provides key considerations for agencies, including launching a pilot program to fine-tune best practices, conducting a cost-benefit analysis to compare home versus public charging, and exploring cost-effective solutions, such as installing standard outlets instead of dedicated charging stations. The guide underscores the importance of legal and financial considerations, such as verifying agency authority to install home charging infrastructure at an employee's home, ensuring the availability and appropriateness of using agency funds for home charging infrastructure, and understanding the tax implications of reimbursements.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2017

The CPAT 2.0.2 Domain Model - How CPAT 2.0.2 "Thinks" From an Analyst Perspective.

To help effectively plan the management and modernization of their large and diverse fleets of vehicles, the Program Executive Office Ground Combat Systems (PEO GCS) and the Program Executive Office Combat Support and Combat Service Support (PEO CS &CSS) commissioned the development of a large - scale portfolio planning optimization tool. This software, the Capability Portfolio Analysis Tool (CPAT), creates a detailed schedule that optimally prioritizes the modernization or replacement of vehicles within the fleet - respecting numerous business rules associated with fleet structure, budgets, industrial base, research and testing, etc., while maximizing overall fleet performance through time. This report contains a description of the organizational fleet structure and a thorough explanation of the business rules that the CPAT formulation follows involving performance, scheduling, production, and budgets. This report, which is an update to the original CPAT domain model published in 2015 (SAND2015 - 4009), covers important new CPAT features. This page intentionally left blank

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Transportation Electrification Beyond Light Duty: Technology and Market Assessment

Commercial fleets form the backbone of the nation’s economy, getting people and the things they need to the places they need to go and performing services necessary to keep public and private physical infrastructure in working order. Commercial fleets include a wide range of vehicle and equipment types, typical uses, and sizes, and involve millions of on-road and offroad vehicles. This diversity means there is no single solution to the challenges these vehicles pose for reducing petroleum dependence, impact on air quality, and emission of greenhouse gases. This document focuses on electrification of government, commercial, and industrial fleets. These fleets have been divided into three market segments based on equipment use: service fleets, goods movement, and people movement. In particular, it addresses highway vehicles not used for personal transport; non-highway modes, including air, rail, and water; and non-road equipment used directly or in support of these uses.

33 ADVANCED PROPULSION SYSTEMS↗