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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

FY2021 Progress Report on BISON Metallic Fuel Model Development and V&V Using EBR-II Legacy Data

In this report, the activities and achievements made by Argonne National Laboratory for the Nuclear Energy Advanced Modeling and Simulation (NEAMS) BISON code metallic fuel validation and verification project in FY2021 are summarized. The cladding degradation model based on the FCCI/CCCI wastage calculations has been developed and implemented into BISON. A comprehensive evaluation of the cladding degradation model was performed based on FIPD data of the IFR experiment X447. BISON objects were also developed to enable direct use of time-varying cladding outer surface temperature profile as temperature boundary conditions, which proved to provide more accurate temperature predictions for the metallic fuel pins irradiated in EBR-II. Additionally, a new BISON object was implemented to enable direct comparison between BISON predicted data and FIPD-based post-irradiation examination (PIE) results, which would significantly facilitate BISON metallic fuel verification and validation (V&V) activities. These new BISON-FIPD integration features were used in the establishment of a low-burnup fuel swelling evaluation framework as demonstration. The framework was successfully used to evaluate current fuel swelling models based on the IFR experiment X423.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Degradation and Modeling of Large-Format Commercial Lithium-Ion Cells as a Function of Chemistry, Design, and Aging Conditions

Demand for large-format (>10 Ah) lithium-ion batteries has increased substantially in recent years, due to the growth of both electric vehicle and stationary energy storage markets. The economics of these applications is sensitive to the lifetime of the batteries, and end-of-life can either be due to energy or power limitations. Despite this, there is little information from cell manufacturers on the sensitivity of cell degradation to environmental conditions or battery use. This work reports accelerated aging test data from four commercial large-format lithium-ion batteries from three manufacturers, with varying design (thickness, casings, ...), chemistry (lithium-iron-phosphate (LFP) or lithium-nickel-manganese-cobalt-oxide positive electrodes (NMC), with graphite (Gr) negative electrodes), and capacity (50 to 250 Amp hours). The tested LFP|Gr cell is found to be relatively insensitive to cycling conditions like temperature or voltage window, while NMC|Gr cells have varying sensitivity. Degradation trends are further investigated by training predictive models: simple polynomial trend lines, a semi-empirical reduced-order model, and an empirical reduced-order model identified using machine-learning based on symbolic regression. Calendar and cycle life are simulated over a variety of conditions to directly compare the various batteries. Cell size and thickness are found to substantially impact sensitivity to temperature during cycle aging, while electrode chemistry impacts depth-of-discharge sensitivity. Real-world battery lifetime is evaluated by simulating residential energy storage and commercial frequency containment reserve systems in several U.S. climate regions. Predicted lifetime across cell types varies from 7 years to 20+ years, though all cells are predicted to have at least 10 year life in certain conditions.

battery lifetime↗
DOE OSTI2021

Parametric analysis of anodic degradation mechanisms for fast charging lithium batteries with graphite anode

In this work, we report the impact of the temperature-driven synergistically-coupled anodic degradation mechanisms on the electrochemical performance of lithium batteries with graphite anode over multiple cycles. Temperature dependence of electrochemical reactions and damage mechanisms, such as solid electrolyte interface (SEI) growth, lithium plating/stripping, dead lithium storage/dissolution, and film cracking are incorporated into the degradation model. Results of a parametric analysis are presented, evaluating the effects of charging rates (1–6 C), operating temperatures (-15 – 45°C) and electrode design parameters, on the relative performance fade in the lithium-ion battery. Thermo-electrochemical process maps are developed to provide insights into the relationship between electrode performance and failure mechanisms. The simulation results predict a severe capacity loss due to lithium plating at low temperatures, which is further aggravated at high charging rates. A common strategy for mitigating lithium plating, through charging at high temperatures, also results in rapid capacity loss due to accelerated SEI formation. Simulation results are used to identify the combination of operating conditions and electrode design parameters that improve the electrochemical performance of the battery. These results demonstrate an opportunity to design safe and high-performance lithium-ion batteries, guided by anodic degradation models.

Battery design↗
DOE OSTI2021

Anisotropic thermal-conductivity degradation in the phase-field method accounting for crack directionality

Dynamic loads applied to metals may lead to brittle or ductile fracture depending on the imposed strain rates, material properties, and specimen geometry. With an increase in the applied velocity, brittle to ductile failure mode transition may also be observed. Furthermore, at high strain rates, ductile fracture may be preceded by shear bands, which are narrow bands of intense plastic deformation, typically accompanied by a significant rise in temperature. Reliable models are needed to predict the response of metals subject to dynamic loads. In particular, capturing the interplay between heat conduction and crack propagation using the phase-field fracture method is still an open research field. To accurately capture the heat transfer physics across crack surfaces, damage models degrading thermal-conductivity are necessary. While isotropic thermal-conductivity degradation models were proposed, they may lead to errors as they are indifferent to crack directionality. In our current work, a new anisotropic approach is proposed in which thermal-conductivity, which depends on the phase-field gradient, is degraded solely across the crack. Here, it is shown that this approach improves the near-field approximation of temperature and heat flux compared with isotropic degradation, when taking the discontinuous crack solutions as reference. Additionally, the anisotropic degradation approach is implemented in a unified dynamic fracture model and a couple of examples demonstrate the viability of this approach.

42 ENGINEERING↗
DOE OSTI2022

Characterization and Accelerated Life Testing of a New Solid Oxide Electrolysis Cell

The project aims to develop mechanistic degradation models that realistically predict long-term Solid Oxide Electrolysis Cell (SOEC) durability, using input data from accelerated electrochemical life testing combined with quantitative microstructural and microchemical evaluation. A promising SOEC type will be further developed. The understanding achieved by combining experimental results and theory will be used to guide improvements in long-term SOEC durability, and validate that this technology can reach DOE H 2 production cost and durability targets. Planned outcomes include: (1) new experimental accelerated testing data on SOEC degradation at the oxygen electrode, fuel electrode, and electrolyte; (2) predictive theory of degradation developed based on the accelerated test data; (3) new electrode materials and processing methods that minimize degradation rate; (4) determination of the impact of SOEC electrolyte thickness and electrode composition/microstructure on performance and degradation; (5) determination of the operating conditions where degradation is minimized; and (6) validation of degradation models for extrapolating accelerated test data to predict long-term durability.

08 HYDROGEN↗
DOE OSTI2022

Spectral Rear Irradiance Testing and Modeling for Degradation and Performance of Solar Fields: Preprint

This work investigates how the spectrum of irradiance incident on the rear of solar modules impacts their degradation, and in case of bifacial PV their performance. We calculate the spectral irradiance incident on the rear of modules through raytrace simulations and validate with measured field data collected from a 75kW single axis-tracked bifacial test site. An equation to calculate relative degradation is shown, showing that current acceleration factors for UV damage in chambers can be off by +3% for the rear surfaces. Finally, a method for improving non-spectral simulations is proposed.

bifacial performance↗
DOE OSTI2024

The "PVLib" of Degradation: PVDeg

The Photovoltaic (PV) industry constantly aims for lower costs through higher-efficiency cells, improved module designs, and improvements in durability. This leads to the use of new materials, designs, and manufacturing processes, and not always with a sufficient amount of durability testing. To help drive down costs there is a desire to create modules that will last for up to 50 years of service life. To accomplish this, every degradation mode and mechanism must be identified and either eliminated or otherwise mitigated. This involves the extrapolation of laboratory results to the field conditions. There is a need to organize the existing degradation data into an accessible format and to provide industry relevant tools for extrapolation from laboratory to field conditions. While the basic equations used to model degradation are sometimes very simple, the full analysis involves calculations are cumbersome but ubiquitous for many degradation processes. A simplified, modeling framework to accomplish these repetitive processes will facilitate the analysis to help researchers keep up with the rapid pace of technological changes. In this talk, we will describe our progress creating the open-source tool PVDeg. This tool can be used to search for and analyze degradation information and extrapolate PV module performance and durability to field exposure. PVDeg simplifies many of the common foundational computational operations for obtaining meteorological data and using it to generate a model of the PV deployment. This prediction tool repository also contains various degradation models as well as a library of material parameters suitable for estimating the durability assessment of materials and components. We use an integration pipeline approach that allows us to leverage weather data from the National Solar Radiation Database, and other weather sources, to perform geospatial degradation analysis in the US and worldwide. We hope to become a repository that can be used for weathering and degradation analysis for various applications beyond the PV industry. During the talk, we will provide the PVPMC attendees the opportunity to interact with the tool via a Google Collab tutorial they can run on their phones or laptops.

durability↗
DOE OSTI2022

Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Advanced battery modeling for interfacial phenomena and optimal charging

Lithium ion batteries are one of the most promising energy storage systems for portable devices, transportation, and renewable grids. To meet the increasing requirements of these applications, higher energy density and areal capacity, long cycle life, fast charging rate and enhanced safety for lithium ion battery (LIBs) are urgently needed. To solve these challenges, the relevant physics at different length scale need to be understood. However, experimental study is time consuming and limited in small scale’s study. Modeling techniques provide us powerful tools to get a deep understanding of the relevant physics and find optimal solutions. This work focuses on studying the mechanism in advanced battery engineering techniques and developing a new charging algorithm by model-based optimization. The research topics are divided into six topics and each topic is reported as a form of journal publication. Paper Ⅰ provides a new aspect of how ALD coating improves the lithium ion diffusion at electrode particles. Paper Ⅱ explains the mechanisms by which 3D electrodes enhance battery performance and reveals guidelines for optimized 3D electrode designs by a 3D electrochemical-mechanical battery model. Paper Ⅲ investigates the electrolyte concentration impact on SEI layer growth and Li plating, especially under high charge rates. Paper Ⅳ proposes an optimized charging protocol for fast charging for reducing the charging time with minimal degradation. Paper Ⅴ reports a comprehensive degradation model for degradation estimation and life predication of energy storage system (ESS). Paper Ⅵ is a study of temperature-dependent state of charge (SOC) estimation for battery pack.

25 ENERGY STORAGE↗
DOE OSTI2023

Initial Fracture Propagation Modeling of Graphite Components with Grizzly

Graphite has historically been extensively used in power reactor cores and will be used in multiple types of advanced reactors currently under development. These graphite structural components can experience significant stresses due to nonuniform volumetric strains induced by irradiation and thermal expansion, which can lead to fracture. Robust tools for predicting fracture initiation and propagation in graphite structural components in nuclear reactors are important for evaluating component integrity, developing design standards, and interpreting experimental results to characterize graphite performance. The U.S. Department of Energy’s Nuclear Energy Advanced Modeling and Simulation program has been developing degradation models for other structural components in nuclear reactors within the Grizzly and BlackBear codes. This report documents an effort to develop initial capabilities for modeling graphite fracture within these codes, building on prior efforts to model fracture in other materials. Major elements of this effort include developing a new system for modeling fracture nucleation and growth in two dimensions using the extended finite element method and incorporating a damage and plasticity model. These capabilities are applied here to model a representative graphite component and a splitting disc experiment used to obtain tensile strength.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Physics-informed Data-driven Degradation and Prognostic For Hydropower System

The hydropower system is a cyber-physical system, and the industry is experiencing high operation and maintenance (O&M) costs along with increasing unplanned outages, primarily due to aging equipment and new asset loading patterns. Current (O&M) practices in asset management are predominantly reactive rather than predictive and systematic. To address these challenges, a proposed physics-informed data-driven degradation model aims to simulate the health of components in various environments. Its purpose is to accurately characterize wear and fatigue in the pilot components of hydropower systems. Additionally, the model provides informed confidence in prognostic predictions concerning asset degradation and remaining life.

13 HYDRO ENERGY↗
DOE OSTI2021

Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning

Various modeling techniques are used to predict the capacity fade of Li-ion batteries. Algebraic reduced-order models, which are inherently interpretable and computationally fast, are ideal for use in battery controllers, technoeconomic models, and multi-objective optimizations. For Li-ion batteries with graphite anodes, solid-electrolyte-interphase (SEI) growth on the graphite surface dominates fade. This fade is often modeled using physically informed equations, such as square-root of time for predicting solvent-diffusion limited SEI growth, and Arrhenius and Tafel-like equations predicting the temperature and state-of-charge rate dependencies. In some cases, completely empirical relationships are proposed. However, statistical validation is rarely conducted to evaluate model optimality, and only a handful of possible models are usually investigated. This article demonstrates a novel procedure for automatically identifying reduced-order degradation models from millions of algorithmically generated equations via bi-level optimization and symbolic regression. Identified models are statistically validated using cross-validation, sensitivity analysis, and uncertainty quantification via bootstrapping. On a LiFePO 4 /Graphite cell calendar aging data set, automatically identified models utilizing square-root, power law, stretched exponential, and sigmoidal functions result in greater accuracy and lower uncertainty than models identified by human experts, and demonstrate that previously known physical relationships can be empirically "rediscovered" using machine learning.

25 ENERGY STORAGE↗
DOE OSTI2026

Electric-vehicle battery second-life and recycling pathways: How economics depend on chemistry, processing, and application

We assess the economics of repurposing and recycling electric vehicle (EV) batteries by estimating the maximum acquisition price repurposers and recyclers could pay for used EV packs across cathode chemistries, first-life conditions, second-life applications, and recycling processes. We develop a novel open-source process-based cost model of a UL-1974-certified repurposing facility and leverage battery degradation models to estimate the maximum acquisition price repurposers could pay for used EV batteries while producing second-life battery energy storage systems with life-adjusted costs equivalent to new systems. We compare these maximum price estimates to maximum prices for recyclers based on cost and revenue estimates from the EverBatt model. We find that repurposing is more economical than recycling for lithium iron phosphate (LFP) batteries, due to their relatively long life and low value materials; recycling is generally more economical than repurposing for lithium nickel cobalt aluminum oxide (NCA) batteries, due to their shorter life and higher value materials; and the economics for lithium nickel manganese cobalt oxide (NMC) batteries depend more heavily on first life retirement conditions and second life application intensity. These results suggest an overall strategy: reuse LFP, recycle NCA, and sort NMC into recycling or repurposing pathways based on state of health and second-life application.

25 ENERGY STORAGE↗