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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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465 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Electrification pathways for U.S. passenger vehicles

As electric vehicle (EV) adoption continues to accelerate, we explore the implications of different adoption trajectories that achieve a full transition to EVs by 2050 for U.S. light-duty passenger vehicles (LDVs). Using a vetted transportation system model, we find that achieving 100% EV sales by 2040 would decrease tailpipe greenhouse gases (GHGs) by 90% between 2022 and 2050, leaving about 45 million gasoline vehicles on the road. Achieving 100% sales by 2035, tailpipe emissions decrease 93%, with about 28 million gasoline vehicles on the road in 2050 (9% of stock). Slower EV adoption, reaching 100% sales by 2045, would result in 69 million gasoline vehicles on the road in 2050. Fully electrifying passenger vehicles by 2050 would require a full transition to EVs sales in the 2030s coupled with either changes to mobility, or an accelerated stock turnover in the 2040s with additional 19–30% annual LDV sales.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

The impacts of charge transfer, localization, and metallicity on hydrogen retention and transport capacity

Solid state hydrides such as early transition metal hydrides are of inestimable importance for the future of hydrogen energy and are actively being investigated for energy conversion and storage applications such as fuel cells, solid-state batteries and neutron moderators. The retention and transport behavior of hydrogen in these hydrides has a huge role on the extended performance of components. While early transition-metal-based compounds exhibit many peculiar properties due to their unique correlated electronic signatures arising from -orbital electrons, the fundamental chemistry and transport behavior of hydrogen in such hydrides is not well understood. In the present work, using density functional theory, a highly intricate bonding feature is revealed through the theoretical investigation of the electronic structure of early transition metal hydrides YH 2 and ZrH 2 . In particular, a pronounced charge transfer from the transition element to H, results in localized electron densities at deep energy levels. The interplay between intrinsic charge transfer, charge localization, and metallicity in YH 2 and ZrH 2 leads to strong chemical bonding between metal and hydrogen atoms and large energy barriers for the migration of hydrogen vacancies. Specifically, hydrogen vacancies are found to be stable in the neutral state due to electron screening effects, accompanied by substantially high migration barriers between 0.8–1.2 eV along different crystallographic directions. In contrast, recent literature shows the migration barrier for charged H vacancies in insulating s-block metal hydrides lie between 0.1–0.4 eV, which is suitable for fast conduction applications. This pivotal electron structure difference exploited between early transition metal hydrides and alkali/alkaline earth metal hydrides determines extended hydrogen retention in these early transition metal hydrides. Finally, this work explains fundamental differences between the electronic structure of s-block and d-block metal hydrides, and its impact on the mobility of hydrogen vacancies.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Direct Deoxygenation of Phenol over Fe-Based Bimetallic Surfaces Using On-the-Fly Surrogate Models

We present an accelerated nudged elastic band (NEB) study of phenol direct deoxygenation (DDO) on Fe-based bimetallic surfaces using a recently developed Gaussian process regression (GPR) calculator. Our test calculations demonstrate that the GPR calculator achieves up to 3 times speedup compared to conventional density functional theory calculations while maintaining high accuracy, with energy barrier errors below 0.015 eV. Using GPR-NEB, we systematically examine the DDO mechanism on pure Fe(110) and surfaces modified with Co and Ni in both top and subsurface layers. Our results show that subsurface Co and Ni substitutions preserve favorable thermodynamics and kinetics for both C–O bond cleavage and C–H bond formation, comparable to those on the pure Fe(110) surface. In contrast, top-layer substitutions generally increase the C–O bond cleavage barrier, render the step endothermic, and result in significantly higher reverse reaction rates, making DDO unfavorable on these surfaces. This work demonstrates the effectiveness of GRR-accelerated transition state searches for complex surface reactions and provides insights into rational design of bimetallic catalysts for selective deoxygenation.

Aromatic compounds↗
NASA NTRSDate not supplied

A Notional Artemis Lunar Surface Exploration Package (ArLSEP) based on the Gandalf Staff Platform

Introduction: The Artemis program is planning to deliver crew and cargo to the lunar surface, but there is no current package for supporting lunar in-struments and experiments similar to the Apollo Lunar Surface Exploration Package (ALSEP). This abstract provides a possible concept for such a package using the Gandalf Staff Platform as a common core. Gandalf Staff: The Gandalf Staff is an early prototype system developed over FY’21/FY’22 using NASA Science Technology Mission Directorate (STMD) Center Information Fund (CIF) grants to de-sign, build and test “proof-of-concept” components. These components include a 24v battery powered monopole that powers a suite of subsystems, including a Graphical User Interface (GUI) for crew, surface voice and data communications, Lunar Search and Rescue (LunaSAR) navigation and communications, LiDAR, field site external lighting, 360-degree camera, and a geothermal instrument for measuring sub-surface temperature gradient. The staff can be carried independently by an Extra-Vehicular Activity (EVA) astronaut, or can be mounted into a tripod for “hands free” support at a surface site being investigated. The staff can be attached to an external solar array and power storage system for long-duration operations. [1,2] ALSEP: An ASLEP flew on each mission Apollo 12 to Apollo 17. For Apollo 11, a simplified packaged called the Early Apollo Scientific Experiments Pack-age (EASEP) was flown. Each package included a “Central Station” that provided the power and communications connected to a variety of instruments and sensors. The power was provided by a Radioisotope Thermoelectric Generator (RTG) fueled by Plutoni-um-238 generating 70 watts of power (initially, decayed over time) [3]. The communications system provide for direct to Earth data transfer from the lunar surface. Each pack-age was stowed externally in the Lunar Module (LM) Scientific Equipment (SEQ) bay with a mass up to 163 kg (Apollo 17). The crew unloaded the ALSEP from the LM and deployed the instruments on the lunar surface. Although designed to operate for only 1 year, many sites operated for up to 8 years successfully [4]. The Active Seismic Experiment (ASE) included 3 geophones for detecting seismic waves created by mortars and thumpers deployed by the crew. Other active experiments measured the lunar atmosphere, the heat flow in the subsurface, the lunar gravity and potential gravity waves, the lunar magnetic field, the solar wind and plasma interactions in cislunar space. Passive experiments included collectors for dust and cosmic rays, and retroreflectors for precise measurements of distance using a laser from Earth. The ALSEP program continues to generate insights into lunar formation and evolution. ArLSEP Concepts: The lunar surface science package for the Artemis program will hopefully exceed the capability of the ALSEP. There are multiple issues for discussion leading to the design of a new ArLSEP, needing requirements definition from the science community, NASA mission architecture, and NASA budget planners. 1. Delivery Mechanism Two possible projects currently provide capability to deliver scientific cargo to the lunar surface: 1) the Commercial Lunar Payload Services (CLPS) [5] and the Human Landing System (HLS) [6, 7]. Each project is controlled by a different organization within NASA and budgeted with different criteria although both support lunar exploration. The HLS system delivers crew (and potentially cargo) to human landing sites. If an ArLSEP is “predeployed” to such a site, the design must include power (either from the vehicle or independently) to keep the electronics functioning until deployed by the crew. If an ArLSEP is delivered on a vehicle after the crew is present on the lunar surface, safety protocols require adequate distance from the humans for impact from descent propelled sur-face regolith ejecta. This distance can not exceed the capability of the crew to walk (if no rover) to the vehicle for ArLSEP deployment. 2. Overall Guidelines The general design of ArLSEP will likely follow the ALSEP with a common system for communications and power; however, significant architecture differences between Apollo and Artemis exist. Power: The RTG will not be available for early Artemis missions nor likely follow-on Lunar Exploration Transportation Services (LETS) missions [8]. Thus, ArLSEP power must be supplied by solar arrays with sufficient battery capability to “keep alive” necessary electronics during any lunar surface eclipse period. Communication: The Artemis program is developing a series of communications satellites for lunar orbit to provide surface transmission of data and voice to Earth. Called “LunaNET”, this network is component useful for ArLSEP since south polar locations may not always have direct “line-of-sight” to Earth [9]. 3. Concept of Operations (ConOps) The general ConOps for ArLSEP is to deliver the package to lunar surface before the crew arrives, and then have the crew deploy the package after some period of time. This requires coordinated design (for power systems) and launch window (for schedule) on both the cargo and crew missions. Once the ArLSEP is deployed, it will operate autonomously for a number of years. It should be designed to be EVA compatible for crew maintenance and upgrade. 4. Notional Design (for discussion purpose only) The landing site near the South Pole is expected to have no eclipse cycle exceeding 5 days, so the “keep alive” power is 144 hours (6 days to include margin). A 12v ArLSEP will use rechargeable LiFePO4 cells, which are common in the Electric Vehicle (EV) industry. With a current of 5 amps and a 125 watt system, the mass is about 90kg. The comm. system and structure adds another 10kg, thus the “Central Station” is approximately 100kg. The solar power is collected on four arrays (each 2m above the surface), and the entire ArLSEP is designed to stow in a 2m x 1m x 1m volume. The experiment and instrument design will vary for each installation and add mass to the total (although they are expected to fit within the 2m3 volume). Seismic wave generation will likely not be provided with mortars, thus an electric “thumper” will be required. Active instruments such as imaging systems and sensing instruments will benefit from the additional power and communication capability provided by ArLSEP. Passive systems such as retroreflectors, witness plates, and cosmic dust collectors can be added to either the landing vehicle and/or the ArLSEP. With repeated HLS missions to the same human site, the ArLSEP can be expanded and easily maintained for long duration science collection on the lunar surface.

ALSEP↗
DOE OSTI2023

Floating zone crystal growth, structure, and properties of a cubic Li 5.5 La 3 Nb 1.5 Zr 0.5 O 12 garnet-type lithium-ion conductor

As a promising candidate for solid-state electrolytes in Li-ion batteries, the garnet-type Li-ion conductor series Li 5+x La 3 Nb 2-x Zr x O 12 (LLNZO) (0 ≤ x ≤ 2) exhibits high ionic conductivity at room temperature. However, no previous single-crystal growth or characterization has been reported for LLNZO compositions 0 ≤ x ≤ 1. To obtain a complete understanding of the trend in the structure–property relationship in this class of materials, we used the floating zone (FZ) method to grow a single crystal of Li 5.5 La 3 Nb 1.5 Zr 0.5 O 12 that was 4 mm in diameter and 10 mm in length. Using Laue neutron single-crystal diffraction, two distinct Li sites were observed: tetrahedral 24d and octahedral 96h sites. The maximum entropy method (MEM) based on neutron single-crystal diffraction data was used to map Li nuclear density and estimate that the bottleneck of Li transport exists between neighboring tetrahedral and octahedral sites, and that Li is delocalized between split octahedral sites. Room-temperature Li-ion conductivity in Li 5.5 La 3 Nb 1.5 Zr 0.5 O 12 measured with electrochemical impedance spectroscopy (EIS) was 1.37 × 10 -4 S cm -1 . The Li migration activation energy was estimated to be 0.50 eV from EIS and 0.47 eV from dielectric relaxation measurements. The Li-ion jump attempt rate was estimated to be 1.47 × 10 12 Hz while the time scale of successful migration is 10 -7 to 10 -6 s.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Design Considerations for Fast Charging Lithium Ion Cells for NMC/MCMB Electrode Pairs

Lithium ion cells that can be quickly charged are of critical importance for the continued and accelerated penetration of electric vehicles (EV) into the consumer market. Considering this, the U.S. Department of Energy (DOE) has set a cell recharge time goal of 10–15 min. The following study provides an investigation into the effect of cell design, specifically negative to positive matching ratio (1.2:1 vs. 1.7:1) on fast charging performance. By using specific charging procedures based on negative electrode performance, as opposed to the industrial standard constant current constant voltage procedures, we show that the cells with a higher N:P ratio can be charged to ~16% higher capacity in the ten-minute time frame. Cells with a higher N:P ratio also show similar cycle life performance to those with a conventional N:P ratio, despite the fact that these cells experience a much higher irreversible capacity loss, leading to a lower reversible specific capacity.

25 ENERGY STORAGE↗
DOE OSTI2024

Theoretical Investigation of the Hydrogenation of Cyclohexene Catalyzed by Supported Single-Atom Sites on Redox Noninnocent LiMn 2 O 4 and Li 2 Mn 2 O 4 Surfaces

Here, the tuning of catalyst activity via stereoelectronic modulation of the active-site structure remains a grand challenge in heterogeneous catalysis. In homogeneous catalysis, the redox noninnocent ligands can be introduced to organometallic fragments to donate electrons to the metal center and fine-tune the catalytic activity. Analogously, lithium-ion battery materials, such as lithium manganese oxide (LMO), lithium titanium oxides (LTO), etc., can serve as redox noninnocent catalyst support to modulate the electronic structures of the active site via lithiation, hence tuning the catalytic activity of supported active sites. Experimentally, the Ni single-atom site was supported on LiMn 2 O 4 via oxidative grafting and exhibited no catalytic activity toward the hydrogenation of cyclohexene. After introducing additional Li into the catalyst support forming Li 2 Mn 2 O 4 , the Ni single-atom site becomes active, with the catalytic rates increasing as a function of the lithiation for Li/Mn ratios >0.9. In this paper, density functional theory (DFT) calculations are performed to study the Ni site structure via X-ray absorption near edge structure (XANES) simulations and investigate the electronic properties of Ni single-atom site before and after the addition of intercalated lithium in the LMO spinel structure. Furthermore, the study of the reaction mechanism is also carried out to understand the thermodynamically and kinetically favored pathways. XANES simulation suggests that the Ni single-atom site is likely to stay in the Li channel of the spinel support structure and form an octahedral structure. After Li intercalation, the Ni site becomes less positively charged, indicating the partial reduction of the Ni site. The simulated reaction energy profile over the LiMn 2 O 4 support exhibits high-energy barriers (1.07 eV) for the hydrogenation of cyclohexene; however, the Li 2 Mn 2 O 4 support is able to better stabilize low-coordinated ion sites and improve ion mobility, leading to lower overall energy barriers (0.64 eV). The reduced and low-coordinated ion site, thus, can better stabilize the reaction intermediates and promote hydrogenation reaction. Similarly, other transition metal ions (Fe, Co, and Cu) are also considered over the LiMn 2 O 4 and Li 2 Mn 2 O 4 catalyst supports for hydrogenation reaction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Cybersecurity for Grid Connected eXtreme Fast Charging (XFC) Station (CyberX) (Final Scientific/Technical Report)

This report summarizes the activities conducted under the DOE VTO funded project DE- EE0008451, where ABB Inc. (ABB), in collaboration with Idaho National Laboratory (INL), APS Global (APS), and XOS Trucks (XOS) pursued the development of a cyber-resilient extreme fast charging (XFC) management system. This project entitled Cybersecurity for Grid Connected eXtreme Fast Charging (XFC) Station (CyberX) focuses on a resilient architecture for smart charging EV Supply Equipment (EVSE) device control and Coordinated Anomaly Detection System (CADS) features that can be added at the charging site depot level to increase cybersecurity. The project was split into two budget periods focused first on developing the threat model and resilient control concepts and second on testing, improving, and validating those developed resilient control algorithms and features with a focus on key vulnerabilities identified during the threat assessment portion of the project. During the first budget period of the CyberX project, the ABB led team focused on activities to identify, model, and quantitatively prioritize high-impact attack scenarios with potential cyber-physical effects while also modeling and developing concepts for a resilient control system that could securely address integration of DERs and other resources with EV charging. Development of the security focused XFC management system (XMS) was accomplished first by offline simulation using a developed XFC station or depot with 480V input level and simulating measurement inputs to monitoring and control systems in concept development. A representative distribution grid model was developed supporting an EV charging site model with BESS and 6 general EV charging models. These EV charging models allowed multiple configurations of charging level, multiple connected protection and measurement devices, and simulation function to show general compromise of EV, BESS, and protection features based on parallel threat analysis. During the second budget period, the EV site and supporting systems model was developed in more detail and converted from offline model to real-time to real-time with EV charging hardware in the loop (HIL). The resilient control architecture developed as concept in the first part of the project was further tested and validated for integration of local energy resources and XFC charging station site equipment while maintaining cybersecure operating principles. The proposed resilient architecture for smart charging and cybersecurity features consists of two main concepts developed and tested within the project. The first concept is an XFC management system (XMS) consisting of a hardware gateway, software platform, and Supervisory Control and Data Acquisition (SCADA) or Distribution Management System integration components. The second concept is a Coordinated Anomaly Detection System (CADS) which forms a primarily software-related subsystem of the total CyberX solution focused on monitoring system measurements, estimation of measurement states, and predicting current at the utility point of interaction based on machine learning for anomaly detection.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

A Data Processing Pipeline To Extract A Knowledge Graph From Heterogeneous Data For Socio-technical Analysis Of Critical Infrastructure Influence

The code is written in Python and consists of the following pipeline that is implemented in Apache Airflow. This pipeline intends to understand the companies that are directly or indirectly involved with a type of critical infrastructure system at some point in that system's lifecycle. The pipeline takes a configuration file that specifies a list of initial companies to consider, a geographic region of interest, and a set of SEC form types as well as other data sources (e.g. CrunchBase) from which to extract entities and relations. There are four main components to this pipeline as currently implemented: Entity Extraction, Network Construction, Analysis, and Visualization. First, Entity Extraction, is implemented as the `topear-extract_organizations` Apache Airflow workflow. Given an initial query that specifies a geographic region of interest and a time interval, the software will extract CI facilities of interest and organizations that have a direct influence relationship to those facilities (e.g. ownership). During the course of the LDRD, we focused on Electric Vehicle charging stations and this information is available via the Department of Energy (DOE) database on fueling stations maintained by NREL. Within the context of the DOE CESER project, we have focused on Battery Energy Storage Systems (BESS). Second, the Network Extraction component will iteratively construct a social network graph given the set of organizations and people extracted in the previous step. Organizations (and eventually People if desired) are then fed as a query to the `topgear-construct_social_network` Apache Airflow workflow which given a set of initial companies and data sets (e.g. SEC EDGAR form types, OpenCorporates, Crunchbase). This Airflow workflow will iteratively query such data sources to discover relationships with new organizations and people. For example, this module can iteratively query SEC EDGAR for metadata that documents the number of each type of form for the given set of companies and their location. This forms metadata represents a catalog of data sources from SEC EDGAR for the extracted social network knowledge graph. The pipeline then downloads these forms from the website and saves them in a build directory for further processing. These documents are then parsed for entities and relations. Again, we note that in additional to SEC data sources, this step can also pull in information on organizations via API services such as CrunchBase and OpenCorporates or bulk data sources. At the end of this step, the resultant social network, the Critical Infrastructure network, and the edges that encode relationships between organizations and CI facilities, form the Adversarial Socio-Technical Network (ASTN) that informs the analysis. Third, the Analysis component processes these generated ASTN. Previously, that has included the ability to compare prevalence of different vendors for a given infrastructure component type across different regions as well as identify common public and private investors across those vendors. This was demonstrated for EV Charging Stations across several different metropolitan areas within an IEEE PES GridEdge publication. More recently, we have looked at ways to identify infrastructure owners and operators of BESS with the most nameplate capacity across different states as well as other indictors of risk resulting from changes in ownership over time. Finally, the Visualization component consists of an HTML/CSS/JS framework by which users can interact geospatial, operational, and organizational relationships across a given portfolio of Critical Infrastructure facilities. The objective is to provide a library of UI/UX modules that can be repurposed for stakeholder-specific dashboards. All of the modules are related via a common event model that enables UI actions in one view to percolate across the other views.

Weaver, Gabriel [Idaho National Laboratory (INL), ↗
DOE OSTI2024

Fast ionic conduction achieved through the design and synthesis of ceramic heterointerfaces

Lithium (Li) chloride and iron oxychloride (FeOCl), typically nonconductive, were combined to form a [Li 1+δ Cl] δ+ /[FeOCl] δ- heterointerface composite material (LFH), achieving ionic conductivities of >1 mS cm -1 . Analysis techniques (scanning transmission electron microscopy [STEM] and electron energy-loss spectroscopy [EELS]) indicated that the microstructure of LFH consisted of an amorphous LiCl-based shell surrounding a crystalline FeOCl-based core. Electrochemical measurements alongside solid-state 6,7 Li nuclear magnetic resonance (NMR) and molecular dynamic simulations revealed Li+ as the sole conductive species, with a diffusion barrier of ~0.25 eV. X-ray photoelectron spectroscopy (XPS) and X-ray absorption fine structure (XAFS) results further supported interstitial Li + diffusion at the heterointerface and within the LiCl phase, made possible by the heterointerface. Despite susceptibility to electronic conductivity, iron’s defects and multivalency (Fe³⁺, Fe²⁺) enable the Fe–O–Cl framework to accept Cl - , facilitating Li⁺ ionic conduction. Finally, a prototype solid-state cell (showing 97% Coulombic efficiency) demonstrated the viability of this heterointerface design for applications in energy storage.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Constructing stable interface layer for boosting high-voltage cycling performance of single-crystal Ni-rich cathodes

Increasing demand for electric vehicles (EVs) worldwide and the requirements for environmental protection have greatly promoted the development of low-Co, Ni-rich layered cathodes due to their high energy density, reasonable cost, and less environmental pollution. Nevertheless, serious performance degradation and safety concerns resulting from structural/interfacial instability under high operating voltages (≥4.3 V) have greatly hindered its commercialization. Herein, we propose a feasible surface modification strategy by introducing a multifunctional Li 2 SiO 3 (LSO) coating layer onto the surface of Co less single-crystalline LiNi 0.63 Co 0.07 Mn 0.3 O 2 (NCM) to solve the above challenges. Additionally, the uniform Li 2 SiO 3 coating layer has a unique three-dimensional (3D) ion diffusion channel, which greatly promotes the transmission of Li + and alleviates interfacial stress accumulation. Equally important, the electrochemically inert Li 2 SiO 3 coating layer has a stable structure framework with strong Si-O bonds, which inhibit anisotropic particle volume expansion and the occurrence of side reactions during long-term cycling. As a consequence, LSO modified NCM exhibits greatly improved electrochemical properties even at harsh testing conditions and achieves capacity retention of 79.1% after 200 cycles at 25°C within 2.95-4.5 V in half cells. Furthermore, it provides an outstanding capacity retention of 94.7% after 300 cycles at 25°C within 2.95-4.4 V when tested in pouch full cells.

25 ENERGY STORAGE↗
DOE OSTI2020

Charge Management for an Inductively Charged On-Demand Battery-Electric Shuttle Service with High Penetration of Renewable Energy

This paper presents a charge management control strategy for an on-demand battery-electric shuttle van operating at the National Renewable Energy Laboratory (NREL) campus and supported by day-time inductive charging at the vehicle's waiting spot. A new control algorithm has been proposed for reducing the demand charge costs incurred from wireless charging of the on-demand shuttle. A custom controller has been developed to monitor the shuttle, wireless charger, renewable energy generation, and various loads at NREL's campus, and regulate charging behavior for demand response. The intermittent renewable generation and sporadic operation of the on-demand shuttle service contribute to a high level of uncertainty in expected campus load profile, which must be carefully managed. The control algorithm predicts energy profile to estimate the mobility needs of the vehicle and maintain uninterrupted service during operation while still minimizing peak demand. The proposed controller has been designed and optimized using a Simulink model for the entire system. Next, it has been implemented and tested in real-time on the NREL campus. Two primary vehicle-use cases, charge sustaining and charge depletion operation, are tested under different load profiles and drive cycles to assess the controller's effectiveness at reducing peak demand and therefore demand charges. The proposed controller showed robust performance under different driving scenarios with high correlation between simulated and experimental data. The results showed that proper demand response can be achieved with an average of 94% reduction of charging loads during peak demand events.

ADVANCED PROPULSION SYSTEMS,ENERGY STORAGE↗
DOE OSTI2026

DOE EV Data Collection - Vehicle Data

Vehicle data consist of electric vehicle performance data collected directly from the vehicle during standard operations. Data were collected using onboard data loggers that were either installed by the project team or preinstalled by the original equipment manufacturer. Data recorded by the data loggers were made accessible via an online web portal or an application programming interface. Different data loggers were used (HEM, ViriCiti, and Geotab), and the method for each vehicle is defined in the vehicle attributes file. Some systems collected data on a “trip-level” basis, in which each row of a table represents a single trip (the period between a key-on and key-off event), whereas other data were collected on a per-day basis, in which each row represents a single day of operation. Data were collected over a range of data collection periods, depending on the project. Data have been anonymized by removing information or decreasing information resolution as necessary so that fleets are not identifiable. Due to the wide range of vehicle types represented and variation in data collection, data parameters and frequencies differ between vehicles and fleets The **Performance Data Daily/Trip Data Dictionaries** contain definitions for each available parameter associated with a vehicle’s operations, aggregated at either a daily or trip level. The parameters available will vary from vehicle to vehicle, but every possible parameter will be defined. The **Vehicle Attributes Data Dictionary** contains definitions for each available parameter associated with a vehicle’s physical and functional attributes and fleet context. The **Vehicle Attributes** table contains specific vehicle characteristics, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. The **Vehicle Data** tables contain the data from each vehicle’s operations, aggregated at either a daily or trip level, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables. Data is being uploaded quarterly through 2023 and subject to change until the conclusion of the project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Port of New York and New Jersey Drayage Electrification Analysis

The National Renewable Energy Laboratory (NREL) evaluated the potential for drayage electrification in the Port of New York and New Jersey (PoNYNJ), with a focus on operators: Harbor Freight Transport (HF), Safeway Trucking (SWT), and International Motor Freight Inc (IMF). This report summarizes the data collection and electrification evaluation of all three drayage operators, includes detailed operational data, and identifies the performance requirements for battery electric tractors (BETs) and corresponding infrastructure operated within the context of PoNYNJ drayage operation. This report also details a methodology to evaluate opportunities, strategies, and challenges associated with future expansions of BETs in meeting PANYNJ emissions goals. The Port Authority has established a goal of achieving Net Zero carbon emissions by 2050 across all facilities, including from tenant and stakeholder sources such as drayage trucks. NREL used real-world performance data collected on the three PoNYNJ drayage operations, along with modeling and analysis tools to compare BET to diesel trucks. From March to July 2021, NREL collected 1Hz vehicle and engine data from 46 drayage trucks at the three operators totaling nearly 121,000 miles of operation, providing enough information to assess vehicle operations for electrification potential. A Future Automotive Systems Technology Simulator (FASTSim) electric truck powertrain model was validated using PoNYNJ data and scenarios were run to evaluate drayage truck electrification requirements over the real-world cycles. The first scenario examined BET viability with minimal changes to existing operations. This assumes the trucks charge when stopped for two hours or longer, have a functional battery size of 375 kWh, and can charge at 270 kilowatts (kW) average which are the specification of the commercially available Freightliner eCascadia. The second scenario looked at what operational, charging infrastructure, and BET technology changes would be needed to fully electrify. Finally, detailed analysis was run on charging rate structure to understand operational costs to the fleets. The studied drayage trucks averaged 5.1 MPG, spent roughly 9% of their energy at idle, and drove an average of 140 miles per day with a maximum daily distance of 573 miles. The FASTSim model results indicate a comparable BET would use 417 kWh of energy per day on average accounting for cargo weight, which is close to the full usable capacity of the eCascadia currently available on the market. Based on the daily average operating data, partial fleet electrification is possible with current technology. However, some specific days of operation would require over 1,600 kWh of energy due to longer distances traveled by the trucks and more intense operation. Trucks used for long distance and intense operation cannot be readily electrified with current technology without operational changes. Full adoption of BETs could reduce CO 2 emissions from these fleets by roughly 75% today, eliminating 76 metric tons of CO 2 (MTCO 2 ) per vehicle each year, which equates to 24,100 MTCO 2 per year for all three operators. Commercially available direct current fast chargers (DCFC) have charge rates up to 350 kW. Based on the average daily modeled energy use for each operator, current industrial rate structures, and the assumption of 350 kW peak charging, full drayage electrification would increase electricity consumption. In addition, peak demand usage would increase with unmanaged charging along with cost of electricity having a direct impact on cost per mile for electric vehicles. The resulting cost per mile for BETs along with comparable cost per mile for conventional diesel trucks are also examined at $\$$4.00 per gallon of diesel. It will be important for PANYNJ and the drayage operators within the PoNYNJ to consider these load impacts to their existing electrical infrastructure and devise operational strategies that avoid coincident charging of vehicles to mitigate demand charges. Despite these electricity cost increases, savings from reductions in diesel consumption will help offset the costs of this increased electricity consumption. However, prices of both electricity and diesel are subject to change based on various factors meaning the realized savings will vary over time. This shows BETs could be cost-competitive on an energy cost per mile basis for all scenarios while diesel is above $\$$3.00/gal. Further, if diesel prices dropped to the 15-year low of $2.33/gal, it would still be cost competitive to operate the EVs with electricity costs of 16.3 ¢/kWh or less.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

DOE EV Data Collection - Charging Data

Charging data are collected from one of three sources, each with varying levels of additional information. These sources, in approximate order from most to least additional information, are: • The electric vehicle supply equipment (charger) • Onboard the vehicle itself • From a utility submeter. Many chargers provide software that allows for the collection and reporting of charging session data. If unavailable, data may be recorded by the charging vehicle’s onboard systems. If neither of these options is available, data can be acquired from utility submeters that simply track the energy flowing to one or more chargers. Data collected directly from the electric vehicle supply equipment (EVSE) are typically the most accurate and highest frequency. However, it is not always possible to discern which exact vehicle is being charged during any one session. EVSE-side data can be identified where a single charger ID but a range of vehicle IDs are present (e.g., CH001, EV001-EV005). Data collected from the vehicle’s onboard systems usually does not provide information on which exact charger is being used. Vehicle-side data can be identified where a single Vehicle ID but a range of Charger IDs are present (e.g., EV001, CH001-CH005). Data collected from utility submeters provide no information on which specific vehicle is charging or which specific charger is in use. Submeter data can be identified where multiple Vehicle IDs and multiple Charger IDs are present, but only a single Fleet ID is present (e.g., EV001-EV005, CH001-CH005, Fleet01). The **Charge Data Daily/Session Dictionaries** contains definitions for each available parameter collected as part of an individual charging session, aggregated at either a daily or session level. The parameters available will vary between vehicles and chargers. The **Charger Attributes** table contains specific charger characteristics, coded to at least one anonymous Charger ID and linked to either a single or a range of Vehicle IDs. Vehicle ID can be used as a key between charging data and vehicle attribute tables. The **Charger Attributes Data Dictionary** contains definitions for each available parameter collected on the physical and operational characteristics of the charging hardware itself. The **Vehicle Attributes Data Dictionary** contains definitions for each available parameter associated with a vehicle’s physical and functional attributes and fleet context. The **Vehicle Attributes** table contains specific vehicle characteristics, coded to an anonymous Vehicle ID. This Vehicle ID can be used as a key between vehicle data and vehicle attribute tables, and in cases where charging data are supplied, links a vehicle with the charger(s) that supplied it power. The **Charging Data** tables contain the data from each charger’s operations, coded to at least one anonymous Charger ID and linked to either a single or a range of Vehicle IDs. Vehicle ID can be used as a key between charging data and vehicle attribute tables. Data is being uploaded quarterly through 2023 and subject to change until the conclusion of the project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗