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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A Multi-Fidelity Gaussian Process Regression Method for Probabilistic Wind Farm Power Curve Estimation

Accurate estimation of the power curve for wind turbines or wind farms is crucial to ensure their efficient operation and management. However, conventional methods for power curve estimation rely either on expensive and infrequent measurements or on low-quality numerical simulations. Moreover, the majority of previous studies on power curve estimation for wind turbines or wind farms focused on deterministic estimation, which provides a point estimate of the relationship between wind speed and power generation. Nevertheless, the deterministic approach fails to consider the inherent uncertainty associated with wind energy production resulting from varying turbine characteristics. This can lead to inaccurate power generation estimation and suboptimal decisions regarding energy management. In this paper, a kernel density estimation (KDE) based Multi-Fidelity Gaussian Process Regression (MFGPR) model is proposed to fuse theoretical power curve data and the ground true measurements to create a mapping of wind speed and wind power. By conducting a case study on an actual wind farm in China, the efficacy of the proposed MFGPR model was demonstrated in characterizing the variability of wind power. The probabilistic MFGPR model was also able to generate confidence intervals that encompassed the measured power, thereby improving the accuracy and confidence in wind power estimation or wind resource assessment. Overall, the proposed MFGPR model offers a reliable approach to integrate high-fidelity ground measurements and theoretical power curve data, resulting in precise wind resource assessment and power estimation.

Gaussian process regression↗
DOE OSTI2023

Modeling and control of nuclear–renewable integrated energy systems: Dynamic system model for green electricity and hydrogen production

The need for decarbonization and diversification of energy resources has led to the development of integrated energy systems (IESs), where multiple resources supply more than one energy sector. Here, one such IES with small modular nuclear reactors and renewables (wind and solar) as generating resources, catering to the demand of the electric grid while producing hydrogen for industries, is modeled in this paper. The physics-based component models are represented using the Modelica language and interconnected to form the IES. The control and coordination of the overall system are ensured by designing a suitable control architecture composed of individual subsystem-level controls and supervisory control. The dynamic performance and the load-following capability of the IES are evaluated, while satisfying the safe operational limits of the components. Different configurations and modes of IES operation are considered, where the adaptability of the control system in the presence of varying demands and renewable generations is validated. The simulation results indicate that hydrogen as a flexible load facilitates the supply of varying grid demand. Additionally, the renewables are also accommodated into the IES owing to the flexibility of the balance of plant associated with the nuclear reactors.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Control of core–shell nanoparticles properties through plasma synthesis: a computational study

The improved properties of core–shell nanoparticles (CSNPs) over homogeneous nanoparticles (NPs) have expanded and diversified the applications of these nanomaterials. However, controlling the properties of CSNPs can be a challenging task. Low temperature plasmas have proven to be an effective method of producing NPs with uniform size and morphology, and high yield. That said, NP transport and growth dynamics are sensitive to LTP properties. We report on a computational investigation of the evolution of Ge–Si CSNP properties as a function of operating conditions through the modeling of a flowing, two-zone inductively coupled plasma (ICP) reactor. Ar/GeH 4 and Ar/SiH 4 gas mixtures were supplied to separate plasma zones at a pressure of 1 Torr to promote growth of Ge cores and Si shells. The negatively charged CSNPs are trapped electrostatically in the vicinity of the antennas where the plasma is generated and where the majority of particle growth occurs. Particles that grow to a critical size are then de-trapped by fluid drag due to neutral gas flow. A two-dimensional hybrid plasma model coupled with a three-dimensional kinetic NP transport model were utilized to resolve plasma chemistry and NP growth processes that take place on distinct timescales. The trends in CSNP properties and trapping mechanisms associated with flow rate, applied ICP power and inlet precursor fraction are discussed. While the spatial distribution of plasma produced radical species can have significant impact on the NP growth process, the NP transport dynamics are what ultimately dictates the growth environment that is unique to each particle and so determines their final dimension and composition. The key to optimizing reactor conditions involves controlling the spatial density of growth species and plasma profile as a means to tailor particle trapping dynamics suitable to produce CSNPs for a specific application.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Economic Dispatch With Distributed Energy Resources: Co-Optimization of Transmission and Distribution Systems

The increasing penetration of distributed energy resources (DERs) in the distribution networks has turned the conventionally passive load buses into active buses that can provide grid services for the transmission system. To take advantage of the DERs in the distribution networks, this letter formulates a transmission-and-distribution (T&D) systems co-optimization problem that achieves economic dispatch at the transmission level and optimal voltage regulation at the distribution level by leveraging large generators and DERs. A primal-dual gradient algorithm is proposed to solve this optimization problem jointly for T&D systems, and a distributed market-based equivalent of the gradient algorithm is used for practical implementation. Finally, the results are corroborated by numerical examples with the IEEE 39-Bus system connected with 7 different distribution networks.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Topology-Agnostic, Scalable, Self-Healing, and Cost-Aware Protection of Microgrids

Most microgrid protection schemes found in published literature suffer from a lack of generality in that they work well for the assumed topology, including type and placement of sources. Other generic protection schemes tend to be too complicated, too expensive, or both. To overcome these draw- backs, a topology-agnostic, scalable, and cost-aware protection based on fundamental principles that work in the presence of high penetration of inverter-based resources (IBRs) is developed and tested in this paper. Here, the protection system also implements stable automatic reconfiguration of the healthy sections of the system after clearance of fault, thus increasing resilience by self- healing. To achieve this ambitious goal, stable inverter models are developed that operate in unbalanced networks in grid-connected and islanded modes, even with 100% IBRs, share power without conflicting controls, and can ride through faults while limiting fault currents. The scheme is tested for primary and backup protection and reconfiguration on the IEEE 123-node feeder in grid-connected and islanded modes with 15 IBRs connected to the system.

42 ENGINEERING↗
DOE OSTI2026

Dynamic Transmission Line Switching Amid Wildfire-Prone Weather Under Decision-Dependent Uncertainty

During dry and windy seasons, environmental conditions significantly increase the risk of wildfires, exposing power grids to disruptions caused by transmission line failures. Wildfire propagation exacerbates grid vulnerability, potentially leading to prolonged power outages. To address this challenge, we propose a multistage optimization model that dynamically adjusts transmission grid topology in response to wildfire propagation, aiming to develop an optimal response policy. By accounting for decision-dependent uncertainty, where line survival probabilities depend on usage, we employ distributionally robust optimization to model uncertainty in line survival distributions. We adapt the stochastic nested decomposition algorithm and derive a deterministic upper bound for its finite convergence. To enhance computational efficiency, we exploit the Lagrangian dual problem structure for a faster generation of Lagrangian cuts. Using realistic data from the California transmission grid, we demonstrate the superior performance of dynamic response policies against two-stage alternatives through a comprehensive case study. In addition, after solving the multistage formulation, we construct easy-to-implement policies that significantly reduce computational burden while maintaining good performance in real-time deployment. History: Accepted by Russell Bent, Area Editor for Network Optimization: Algorithms and Applications. Funding: This work was supported by the U.S. Department of Energy, Office of Electricity [Grant DE-AC02-05CH11231]. The work of R. Jiang was supported in part by the U.S. National Science Foundation, Division of Electrical, Communications and Cyber Systems [Grant ECCS-1845980] and the U.S. Air Force Office of Scientific Research [Grant FA9550-23-1-0323]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2025.1210 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2025.1210 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Estrada-Garcia, Juan-Alberto↗
DOE OSTI2025

Queued Up: 2025 Edition – Characteristics of Power Plants Seeking Transmission Interconnection As of the End of 2024 [Slides]

Electric transmission system operators (ISOs, RTOs, or utilities) require proposed power plants seeking to connect to the transmission grid to undergo a series of impact studies before they can be built. This process establishes what new transmission equipment or upgrades may be needed before a project can connect to the system and assigns the costs of that equipment. The lists of projects in this process are known as “interconnection queues”. In collaboration with interconnection.fyi, Berkeley Lab compiled, aggregated, and cleaned interconnection queue data from >50 transmission grid operators (7 ISO/RTOs and 49 non-ISO balancing areas), which collectively represent ~97% of currently installed U.S. electric generating capacity. The dataset includes requests submitted to queues through the end of 2024, and only includes requests seeking to connect to the transmission grid (not distribution-connected or behind-the-meter projects). The files below include both a PDF report and an Excel data file. The PDF report analyzes interconnection data and metrics through the end of 2024. The Excel data file includes (a) the full project-level interconnection queue dataset through 2024, (b) a codebook (data dictionary) describing each data field, and (c) 35 additional tabs featuring tables summarizing a range of interconnection metrics. Key highlights from the Queued Up: 2025 Edition (featuring data through 2024) include: • As of the end of 2024, there were ~10,300 projects actively seeking grid interconnection in the U.S., representing 1,400 GW of generation and approximately 890 GW of storage. • Historic withdrawal rates alongside relatively fewer new requests resulted in a 12% decrease in total active queue volume compared to the prior year. • Active natural gas capacity (136 GW, +72% year-over-year) increased in 2024, while solar (956 GW, -12%), storage (890 GW, -13%), and wind (271 GW, -26%) capacity decreased. • 408 GW of capacity already has a draft or executed interconnection agreement (IA) but has not yet reached commercial operations. • The time projects spend in queues before reaching COD is increasing. For the regions with available data, the median duration from IR to COD has doubled from <2 years for projects built in 2000-2007 to over 4 years for those built in 2018-2024. • Ultimately, most of this proposed capacity will not be built. Only 13% of capacity that submitted interconnection requests from 2000-2019 had reached commercial operations by the end of 2024; 77% of that capacity had been withdrawn and 10% was still active. • FERC Order 2023 and various other reforms are being implemented. These are important measures to reduce interconnection bottlenecks and enhance grid system reliability, but it is too early to measure and assess their full impact. • New additions for the 2025 edition include: (a) additional detail on data processing and gaps; (b) updates on interconnection reforms; (c) new analysis on interconnection agreements, and more.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

PVInsight (Final Technical Report)

Data generated from real-world photovoltaic (PV) systems represent significant opportunities for the industry–from digital operations and maintenance to real-time planning and forecasting. However, these data also come with substantial, unique challenges. A particular challenge is the analysis of unlabeled PV performance data, which we define as time-series measurements of real power production (or sometimes current or voltage) that do not have corresponding meteorological measurements (irradiance, temperature, etc.) or system configuration information (sometimes called system metadata). These challenges are amplified in the distributed rooftop sector, in which data quality, completeness, and metadata can be very poor.

14 SOLAR ENERGY↗
DOE OSTI2024

Uncertainty Quantification and Sensitivity Analysis of Non-Nuclear Advanced Controls Testbed Reactor Mockup

The research presented in this report describes our progress in applying stochastic methods and uncertainty quantification, parametric study, and variance-based sensitivity analysis (also known as Sobol sensitivity analysis) to a full-core model of a nuclear thermal propulsion (NTP) system simulated with Griffin, with the goal of developing a reduced order (surrogate) model which can be rapidly sampled while perturbing multiple input parameters. In this NTP system, reactivity and power feedback affect the rotation of control drums, which are controlled by a hybrid proportional, integral and derivative (PID) controller, actuated by the power demand and reactivity feedback from the numerical model. This model uses reactor kinetic feedback (mean generation time and $\beta$ from a transient Griffin simulation executed with the improved quasi-static method to provide the kinetic parameters) as inputs to functions which control the CD rotation angle. Using a number of stochastic method approaches, we developed a dual purpose training-surrogate model of the NTP system using polynomial regression. The trained model can be rapidly sampled while simultaneously perturbing various input parameters of the model, such as coefficients on the PID control, or temperature (directly affect the neutron cross section). The surrogate model delivers accurate results orders-of-magnitude faster (minutes, not days) than the base model. Once the base model has been trained, distributions of the uncertain parameters can be changed at will to investigate the effects of perturbing multiple inputs and their effect on the output. For example, coefficients used in the PID control system may vary due to some physical interference, or there may be uncertainty in the temperature of the neutron cross sections in various regions of the reactor. A distribution can be placed on these parameters and operational boundaries can be determined. The goal of this work is to support development of an advanced control system to operate CDs in a functioning NTP system.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

A Hydrogen Load Modeling Method for Integrated Hydrogen Energy System Planning

The integrated hydrogen energy system incorporates hydrogen energy into the power grid, which has been recognized as a promising option for reaching a 100% renewable electricity supply. It can make a profit because the hydrogen produced can be sold as fuel or used to generate electricity for grid services. In this paper, we develop a planning model for the integrated hydrogen energy system that considers the uncertainty of the load demand, the renewable energy generation, and the market prices. To calculate the hydrogen load, we simulate the refueling operations at a hydrogen fueling station over the course of one day and generate representative load profiles with K-means clustering. Moreover, the long-term profitability of the integrated system under both current and future conditions is validated in 10-year planning results.

grid service↗
DOE OSTI2021

High Thermoelectric Performance through Crystal Symmetry Enhancement in Triply Doped Diamondoid Compound Cu 2 SnSe 3

The presence of high crystallographic symmetry and nanoscale defects are favorable for thermoelectrics. With proper electronic structures, a highly symmetric crystal tends to possess multiple carrier channels and promote electrical conductivity without sacrificing Seebeck coefficient. In addition, nanoscale defects can effectively scatter acoustic phonons to suppress thermal conductivity. Here, we report that the triple doping of Cu 2 SnSe 3 leads to a high ZT value of 1.6 at 823 K for Cu 1.85 Ag 0.15 (Sn 0.88 Ga 0.1 Na 0.02 )Se 3 , and a decent average ZT (ZT ave ) value of 0.7 is also achieved for Cu 1.85 Ag 0.15 (Sn 0.93 Mg 0.06 Na 0.01 )Se 3 from 475 to 823 K. Our study reveals: (1) Ag doping on Cu sites generates numerous point defects and greatly decreases lattice thermal conductivity. (2) Doping Mg or Ga converts the monoclinic Cu 2 SnSe 3 into a cubic structure. This symmetry enhancing leads to increase in the effective mass from 0.8 m e to 2.6 m e (m e , free electron mass) and the power factor from 4.3 μW/cm –1 K –2 for Cu 2 SnSe 3 to 11.6 μW/cm –1 K –2 . (3) Na doping creates dense dislocation arrays and nanoprecipitates, which strengthens the phonon scattering. (4) Pair distribution function analysis shows localized symmetry breakdwon in the cubic Cu 1.85 Ag 0.15 (Sn 0.88 Ga 0.1 Na 0.02 )Se 3 . Furthermore, the present work provides a standpoint to design promising thermoelectric materials by synergistically manipulating crystal symmetry and nanoscale defects.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Theoretical investigation of decoherence channels in athermal phonon sensors

The creation and evolution of nonequilibrium phonons is central in applications ranging from cosmological particle searches to decoherence processes in qubits. However, the fundamental understanding of decoherence pathways for athermal phonon distributions in solid-state systems remains an open question. Using first-principles calculations, we investigate the primary decay channels of athermal phonons in two technologically relevant semiconductors—Si and GaAs. We quantify the contributions of anharmonic, isotopic, and interfacial scattering in these materials. From this, we construct a model to estimate the thermal power in a readout scheme as a function of time. We discuss the implication of our results on noise limitations in current phonon sensor designs and strategies for improving coherence in next-generation phonon sensors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Machine-Learning Enabled Evaluation of Probability of Piping Degradation In Secondary Systems of Nuclear Power Plants

The transition to condition-based, risk-informed automated maintenance will contribute to a significant reduction of operations and maintenance costs that account for the majority of nuclear power generation costs. Furthermore, of the operations and maintenance costs in U.S. plants, approximately 80% are labor costs. To address the issue of rising operating costs and economic viability, technologies used to perform online monitoring of piping and other secondary system structural components in commercial nuclear power plants (NPPs) are under evaluation. These online monitoring systems have the potential to identify when a more detailed inspection is needed using real time measurements, rather than at a pre-determined inspection interval thus reducing the maintenance cost. This paper describes distributed high-temperature stable fiber sensors fabricated in optical fibers through a roll-to-roll laser direct writing process using femtosecond lasers. Using phase-sensitive optical time domain reflectometry, distributed acoustic and vibration sensors can be developed and deployed to critical components and systems in NPPs to perform active measurements with spatial resolution down to 0.5-meter throughout the piping systems. Complex acoustic and vibration signatures harnessed by distributed sensors are registered and analyzed by artificial intelligence algorithms for degradation detection and flaw identification. Piping elbows with machined-in flaws were instrumented with fiber sensors. High-spatial-resolution data were used to develop and validate machine learning algorithms, including both linear and nonlinear regression, and classification. Additionally, classification and sensor analysis were also performed for data analysis. The paper concludes with recommendations and future work on applications of machine learning enabled high-resolution fiber sensors for piping degradation monitoring in current or future NPPs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Common Grid Services: Terms and Definitions Report

This document is prepared as part of the Department of Energy’s Grid Modernization Laboratory Consortium (GMLC) 2.5.2 project, whose goal is to develop and socialize a common set of grid service definitions relevant to grid-related interactions with distributed energy resources (DER: responsive generation, storage, and loads) and to advance the concept and requirements of the Energy Services Interface (ESI) to the point of launching related interface standards and guides that can be implemented in communication protocols and business process definitions. The notion of grid services is associated with the definition of an ESI because a key principle of the ESI is that it permits coordination between grid operators and DER facilities in a way that is service-oriented with an understanding of performance expectations. The project investigated the current state of North American grid service definitions from various market operators and utilities, as well as the Federal Energy Regulatory Commission and the North American Electric Reliability Corporation, actively used today (Liu et al. 2022). This document builds on that work to propose terms and definitions for a set of grid service types that address operational objectives commonly found in power system operations. These grid service types derive from existing grid services used in bulk electric market operations, recognizing that each operational authority uses somewhat different names with variations on service performance parameters. Finding commonality at the wholesale or bulk system operation level will hopefully engender progress in seeking agreement on grid services for DER engagement at the distribution level in the emerging retail marketplace.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Leveraging Existing Assets for Long Duration Energy Storage

Increased renewables penetration to electrical grid is necessary to reduce overall emissions from the electrical power generation sector. Nonetheless, its integration creates challenges to grid operators who must match the power being generated by intermittent renewables and other traditional energy sources with the demand from consumers, while ensuring the reliability and power quality for the entire system. Energy storage has been proposed as an alternative to natural gas peaking plants and a form to deliver excess renewable energy generation at times of peak demand. For energy storage to provide benefits to end customers (energy consumers), it must be reliable, efficient, and cost effective. The Illinois Sustainable Technology Center (ISTC), one of the surveys that integrate the Prairie Research Institute (PRI), aims to develop a Center for Energy Storage at Existing Assets (CESEA) at UIUC with the participation of Waste Pressure Corp and Ecotek Engineering USA LLC. CESEA will focus on LDES systems that can integrate to existing infrastructure in a manner that reduces the initial capital expenditure and demonstrates the ability to repurpose fossil assets that would otherwise become stranded, to serve the energy transition. CESEA aims to leverage UIUC’s unique facilities to validate LDES systems performance at a relevant operating environment. UIUC’s facilities include a 85-MW combined heat and power (CHP) power plant, two (2) solar PV plants totaling over 18 MWdc of installed capacity, an electrical grid along with a substation at transmission and distribution voltages, a 22-mile gas pipeline network operating at two pressure levels, along with steam and chilled water distribution networks. The new LDES systems will connect to the existing UIUC grid through a new test electrical station, which will have the capacity to accommodate additional connections to test new devices and technologies as part of future CESEA R&D activities. The test electrical station will contain meters, instrumentation, and controls to accurately capture data and allow optimization of control algorithms. CESEA will initially focus on technologies that: i) utilize existing equipment or facilities to perform at least one of the process steps in LDES (charging, storage, or discharging), ii) leverage mature or commercially available components or controls, iii) show potential for cost-leadership in 10+ hour storage at a commercial scale. Initial technologies that were identified to meet these criteria include Compressed Gas Energy Storage (CGES), and TES. CGES stores electricity by raising the pressure of a compressible gas inside a control volume and converting the stored energy to electricity via expansion-generation. CGES is a generalization of CAES that covers any working gas (not just air). A successful CGES demo will help to circumvent many challenges faced by CAES (long development times due to site prospecting, high cost of compression and storage, heat recovery management, etc.) by: 1) utilizing existing infrastructure (compressors, pipelines, underground storage or pressure vessels) used in the transportation and storage of industrial gases for LDES charging and storage; 2) deploying over sites already-developed for industrial applications with minor additional work; 3) leveraging the price structure of commercial industrial gas to cover the costs of electricity used during charging. A previous DOE-sponsored conceptual study (DE-FE-0032018) estimated the levelized cost of energy of a 1.1 MW / 17 MWh CGES system at $0.08/kWh, with a commercial 10x scale system cost estimated at <$0.04/kWh (Giardinella, 2022). The pilot-sized system was estimated to avoid up to 2693 tons of CO2/year.

25 ENERGY STORAGE↗
DOE OSTI2022

Design Guidelines for Deployable Wind Turbines for Defense and Disaster Response Missions

Access to on-site electrical energy is critical to ensuring a successful military or humanitarian response to conflicts and disasters. These missions typically rely on access to liquid fuel that could be vulnerable to disruption or attack during transport. Generating power on location with wind technology—whether at a contingency base or disaster response coordination point—can reduce this risk and enhance mission reach by diversifying energy sources. Common characteristics of these missions are short planning and execution time horizons and a global scope of potential locations. Compared to conventional wind turbine applications, defense and disaster response applications place a premium on rapid shipping and installation, short-duration operation (days to months), and quick teardown upon mission completion. These design drivers depart from features found in conventional distributed wind turbines, thus necessitating unique design guidance. The supporting information for this guidance comes from available relevant references, technical analyses, and input from industry and military stakeholders. This paper serves as a summary of the full report, Design Guidelines for Deployable Wind Turbines for Military Operational Energy Applications (Sandia report SAND2021-14581 R [1]), which presents the best currently available design guidance for deployable wind turbines to facilitate the effective development and acquisition of technology solutions to support mission success.

17 WIND ENERGY↗
DOE OSTI2025

Regional Inertia Estimation Using Actual Event Measurements: Florida Case

As inverter-based resources’ integration in power grids increases, their uneven distribution across the electrical network leads to the formation of local regions that are weakly coupled to the larger interconnection. This signifies the necessity of regional frequency dynamics investigation and analyzing various inertia metrics. This paper presents a practical methodology for estimating regional rate-of-change of frequency (RoCoF) using actual event recordings, which is then used to evaluate regional inertia. Florida (FL) is selected as the region of interest due to its distinct regional frequency dynamics and the rising levels of solar generation. We identify and analyze confirmed events from 2017 to 2024 that occurred in FL. The results indicate that FL contributes about 14% to the total inertia of the US Eastern Interconnection, which approximately matches its share of generation capacity. Results also highlight seasonal fluctuations in energy generation, which play a significant role in influencing inertia and thus the RoCoF levels. This emphasizes the importance of estimating regional inertia to enhance grid operations for a future that focuses on distributed generation.

Dulal, Saurav [University of Tennessee, Knoxville ↗
DOE OSTI2024

Auto Recycler Report: Results from a Questionnaire about Processing Electric Vehicles and Handling High-Voltage Batteries

With the transition to clean energy the demand for electric vehicles in the United States is rapidly growing. As demand continues to rise in the next couple of decades, sourcing materials (e.g., lithium, nickel, cobalt, graphite, etc.) to put into new batteries for electric vehicles will be critical. The U.S. does not have significant reserves of most of these materials and relies heavily on foreign countries. The lithium-ion batteries in electric vehicles are large and contain substantial amounts of critical materials, therefore, they are a promising source of materials for new batteries. Recycling electric vehicle batteries when they reach their end-of-life will enable the United States to generate a domestic supply of materials needed for future batteries. [1-3] The auto recycling industry is the largest collective owner of end-of-life vehicles in the U.S. and will be the primary handlers of battery-powered vehicles (i.e., hybrid or electric) when they reach the end of their useful life. In an effort to better understand how the auto recycling industry currently handles battery-powered vehicles and their high-voltage batteries, DOE’s ReCell Center for Advanced Battery Recycling (ReCell) developed a questionnaire and distributed it to various trade organizations in the automotive recycling industry.

25 ENERGY STORAGE↗