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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

BatteryPro: A Python Toolkit for Battery Data Analysis and Machine Learning Predictions

Analyzing battery test data for research & development can be time-consuming since battery tests often run on the order of months to years, generating large volumes of data. BatteryPro is a comprehensive Python package and software designed to facilitate advanced analysis and performance predictions for battery test data. Developed for battery researchers, it supports data types from widely used battery testing instruments, including MACCOR and Biologic cycling systems. The software provides a variety of tools for extracting and plotting key battery parameters such as time, voltage, capacity, current, and pressure. In addition to its extensive data analysis capabilities, BatteryPro features a dedicated machine learning module that employs a Bayesian Gaussian Mixture Model (GMM) to predict battery performance and degradation. Users can generate synthetic capacity fade data, calculate fade metrics, and leverage predictive models to forecast long-term battery behavior. The software's graphical user interface (GUI) enhances usability, allowing researchers to upload, merge, and analyze multiple data files with full customizability. The GUI also supports machine learning predictions, enabling users to fit models and make predictions based on selected data and parameters. BatteryPro is built using QtDesigner, scikit-learn, matplotlib, and pandas, ensuring a high level of customization, flexibility, and accuracy in battery data analysis. This tool aims to empower researchers with the ability to perform detailed battery analysis and make informed predictions, ultimately advancing the field of battery research.

25 - ENERGY STORAGE↗
DOE OSTI2017

Revealing Anisotropic Spinel Formation on Pristine Li- and Mn-Rich Layered Oxide Surface and Its Impact on Cathode Performance

Surface properties of cathode particles play important roles in the transport of ions and electrons and they may ultimately dominate cathode's performance and stability in lithium-ion batteries. Through the use of carefully prepared Li 1.2 Ni 0.13 Mn 0.54 Co 0.13 O 2 crystal samples with six distinct morphologies, surface transition-metal redox activities and crystal structural transformation are investigated as a function of surface area and surface crystalline orientation. Complementary depth-profiled core-level spectroscopy, namely, X-ray absorption spectroscopy, electron energy loss spectroscopy, and atomic-resolution scanning transmission electron microscopy, are applied in the study, presenting a fine example of combining advanced diagnostic techniques with a well-defined model system of battery materials. Here, we report the following findings: (1) a thin layer of defective spinel with reduced transition metals, similar to what is reported on cycled conventional secondary particles in the literature, is found on pristine oxide surface even before cycling, and (2) surface crystal structure and chemical composition of both pristine and cycled particles are facet dependent. Oxide structural and cycling stabilities improve with maximum expression of surface facets stable against transition-metal reduction. Finally, the intricate relationships among morphology, surface reactivity and structural transformation, electrochemical performance, and stability of the cathode materials are revealed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Differentiable modeling and optimization of non-aqueous Li-based battery electrolyte solutions using geometric deep learning

Electrolytes play a critical role in designing next-generation battery systems, by allowing efficient ion transfer, preventing charge transfer, and stabilizing electrode-electrolyte interfaces. In this work, we develop a differentiable geometric deep learning (GDL) model for chemical mixtures, DiffMix, which is applied in guiding robotic experimentation and optimization towards fast charging battery electrolytes. In particular, we extend mixture thermodynamic and transport laws by creating GDL-learnable physical coefficients. We evaluate our model with mixture thermodynamics and ion transport properties, where we show improved prediction accuracy and model robustness of Diff-Mix than its purely data-driven variants. Furthermore, with a robotic experimentation setup, Clio, we improve ionic conductivity of electrolytes by over 18.8% within 10 experimental steps, via differentiable optimization built on DiffMix gradients. By combining GDL, mixture physics laws, and robotic experimentation, DiffMix expands the predictive modeling methods for chemical mixtures and enables efficient optimization in large chemical spaces.

25 - ENERGY STORAGE↗
DOE OSTI2023

Multi-Stage Modeling With Recourse Decisions for Solving Stochastic Complementarity Problems With an Application in Energy

This paper presents a multi-stage model with recourse decisions for solving complementarity problems in a competitive electricity market under uncertainty, while also considering renewable energy technologies and battery storage utilization. The model is based on a Nash-Cournot formulation of imperfect competition among power producers. We analyze the value of variable renewable energy (VRE) and battery storage under different uncertainties, such as demand level and VRE availability. To illustrate the proposed model, we apply it to three- bus five-player model and analyze different cases varying costs, including a user-optimal perspective (with market power) and a system-optimal perspective (with central planning). We also consider the potential for congestion in the system by restricting the transmission capacity between a single interface that connects two buses. Our findings show that increasing the battery storage capacity results in a decrease in the need for perfect information about future uncertainties. Additionally, as the model allows for more uncertainty, it becomes more apparent that the stochastic mixed complementarity problem (MCP) has an advantage over a deterministic equivalent. We propose the use of the Value of the Stochastic Equilibrium Solution (VSES) as a quality metric to compare the stochastic MCP with its deterministic equivalent. Overall, expanding battery storage capacity can lower the maximum, mean, and variance values of delivered prices, but there are diminishing returns to this approach.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Accommodating High Transformation Strains in Battery Electrodes via the Formation of Nanoscale Intermediate Phases: Operando Investigation of Olivine NaFePO 4 [Accommodation of High Transformation Strain in Battery Electrodes via Formation of Nanoscale Intermediate Phases: Operando Structure Investigation of Olivine Sodium Iron Phosphate]

Virtually all intercalation compounds used as battery electrodes exhibit significant changes in unit cell volume during use. Na x FePO 4 (0 < x < 1, NFP) olivine, of interest as a cathode for sodium-ion batteries, is a model for topotactic, high strain systems as it exhibits one of the largest discontinuous volume changes (~17% by volume) during its first-order transition between two otherwise isostructural phases. Using synchrotron radiation powder X-ray diffraction (PXD) and pair distribution function (PDF) analysis, we discover a new strain-accommodation mechanism wherein a third, <10 nm scale nanocrystalline phase forms to buffer the large lattice mismatch between primary phases. The new phase has a and b lattice parameters matching one crystalline endmember phase and c lattice parameter matching the other, and is not detectable by powder diffraction alone. Finally, we suggest that this strain-accommodation mechanism may apply to systems with large transformation strains but in which true “amorphization” does not occur.

25 ENERGY STORAGE↗
DOE OSTI2018

Quantification and modeling of mechanical degradation in lithium-ion batteries based on nanoscale imaging

Capacity fade in lithium-ion battery electrodes can result from a degradation mechanism in which the carbon black-binder network detaches from the active material. Here we present two approaches to visualize and quantify this detachment and use the experimental results to develop and validate a model that considers how the active particle size, the viscoelastic parameters of the composite electrode, the adhesion between the active particle and the carbon black-binder domain, and the solid electrolyte interphase growth rate impact detachment and capacity fade. Using carbon-silicon composite electrodes as a model system, we demonstrate X-ray nano-tomography and backscatter scanning electron microscopy with sufficient resolution and contrast to segment the pore space, active particles, and carbon black-binder domain and quantify delamination as a function of cycle number. The validated model is further used to discuss how detachment and capacity fade in high-capacity materials can be minimized through materials engineering.

Science & Technology - Other Topics↗
DOE OSTI2021

Decomposing Loosely Coupled Mixed-Integer Programs for Optimal Microgrid Design

Microgrids are frequently employed in remote regions, in part because access to a larger electric grid is impossible, difficult, or compromises reliability and independence. Although small microgrids often employ spot generation, in which a diesel generator is attached directly to a load, microgrids that combine these individual loads and augment generators with photovoltaic cells and batteries as a distributed energy system are emerging as a safer, less costly alternative. In this work, we present a model that seeks the minimum-cost microgrid design and ideal dispatched power to support a small remote site for one year with hourly fidelity under a detailed battery model; this mixed-integer nonlinear program (MINLP) is intractable with commercial solvers but loosely coupled with respect to time. A mixed-integer linear program (MIP) approximates the model, and a partitioning scheme linearizes the bilinear terms. We introduce a novel policy for loosely coupled MIPs in which the system reverts to equivalent conditions at regular time intervals; this separates the problem into subproblems that we solve in parallel. We obtain solutions within 5% of optimality in at most six minutes across 14 MIP instances from the literature and solutions within 5% of optimality to the MINLP instances within 20 minutes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Behind-the-Meter Solar + Storage Modeling Tool Comparison

Several techno-economic models exist to evaluate solar PV plus battery storage opportunities for behind-the-meter, commercial application. This tool comparison evaluates the methodological differences between several solar plus storage modeling tools and employs a case study to compare their outputs. The tools selected for comparison include NREL's REopt Lite, NREL's System Advisor Model (SAM), Homer Energy's Homer Grid, Geli ESyst, EPRI StorageVET, and LBL's DER-CAM. The comparison focuses on each tool's simulation and optimization capabilities, PV and battery performance modeling, dispatch strategies, and economic model assumptions and calculations, with brief mention of additional capabilities outside the scope of behind-the-meter PV and battery storage. This report describes the case study's tool inputs and outputs and dives into the differences in assumptions, methodologies, and capabilities between tools.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Integration of renewable energy generation and storage systems for emissions reduction in an islanded campus microgrid

Microgrid connected building communities are projected to play an integral part in the clean energy transition. These types of systems, when integrated with distributed energy resources (DERs) such as combined heat and power (CHP), district heating and cooling, renewable generation, and energy storage, can provide clean, reliable power to critical facilities and vulnerable communities. The intermittent nature of renewable generation is a challenge when integrating renewables into any grid system, but particularly in islanded microgrids. The University of Texas at Austin (UT) operates an islanded microgrid powered by a CHP plant, while also utilizing district heating and cooling systems and thermal energy storage (TES). High fidelity operating data was used to develop a validated reduced order model of UT’s integrated campus energy systems to serve as a testbed for use in a case study. Hypothetical renewable energy installations on land owned by UT in west Texas were modeled and integrated into the validated campus models along with battery energy storage (BES). Simulations showed that a combination of renewable energy from wind, and optimally controlled 24-hour thermal and battery storage systems could reduce carbon dioxide emissions on campus by 45.4%. The additional retrofit of burner systems to utilize hydrogen natural gas blends resulted in an overall annual emissions reduction of 54.7%. Carbon capture and storage eliminated the majority of the remaining emissions with increased plant energy expenditure. The presented simulations display the practical limitations of a CHP system complemented by renewable generation and short-term storage in eliminating emissions. Furthermore, results highlight the need for further research and development in long duration storage technologies and hydrogen fueled turbines to increase penetration of renewable energy and reduce emissions.

CHP↗
DOE OSTI2019

A Real-Time Vehicle-Specific Eco-Routing Model for On-Board Navigation Applications Capturing Transient Vehicle Behavior

The paper develops a real-time vehicle-specific eco-routing model for use in on-board navigation systems for both conventional fuel- and battery-powered electric vehicles. The model uses a novel link cost function that retains all microscopic transient behavior along a link by capturing within-link speed, acceleration, and road grade variations. Specifically, eight vehicle-agnostic link-specific variables are transmitted to the cloud and fused with information received from other connected vehicles. The fused data are then sent back to the connected vehicles and used to compute vehicle-specific eco-routes (edge computing). The solution produces an energy-efficient dynamic user-equilibrium traffic assignment that is solved incrementally. A numerical experiment is first designed to test the model, demonstrating the benefits associated with vehicle-specific eco-routes compared to those produced by traditional models. The model is then implemented in a micro-simulation framework to evaluate the network-wide impacts of the proposed energy-efficient user-optimum incremental traffic assignment for different traffic demand levels. Results demonstrate that the proposed model produces lower network-wide energy consumption levels compared to traditional energy-efficient and travel time user-optimum traffic assignment methods for both conventional fuel and electric vehicles. Conventional fuel savings relative to travel time routing decrease with increasing traffic demand levels, while electric power savings do not vary monotonically with congestion levels. Energy savings relative to traditional eco-routing are also demonstrated to not vary monotonically with demand levels. Results of the study also demonstrate that the transportation network layout significantly affects the eco-routing system performance. Finally, the feasibility of the proposed model in real-world applications is discussed and presented.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Two-dimensional lithium diffusion behavior and probable hybrid phase transformation kinetics in olivine lithium iron phosphate

Olivine lithium iron phosphate is a technologically important electrode material for lithium-ion batteries and a model system for studying electrochemically driven phase transformations. Despite extensive studies, many aspects of the phase transformation and lithium transport in this material are still not well understood. Here we combine operando hard X-ray spectroscopic imaging and phase-field modeling to elucidate the delithiation dynamics of single-crystal lithium iron phosphate microrods with long-axis along the [010] direction. Lithium diffusivity is found to be two-dimensional in microsized particles containing ~3%lithium-iron anti-site defects. Our study provides direct evidence for the previously predicted surface reaction-limited phase-boundary migration mechanism and the potential operation of a hybrid mode of phase growth, in which phase-boundary movement is controlled by surface reaction or lithium diffusion in different crystallographic directions. These findings uncover the rich phase-transformation behaviors in lithium iron phosphate and intercalation com-pounds in general and can help guide the design of better electrodes.

25 ENERGY STORAGE↗
DOE OSTI2021

Development of a CNS-permeable reactivator for nerve agent exposure: an iterative, multi-disciplinary approach

Nerve agents have experienced a resurgence in recent times with their use against civilian targets during the attacks in Syria (2012), the poisoning of Sergei and Yulia Skripal in the United Kingdom (2018) and Alexei Navalny in Russia (2020), strongly renewing the importance of antidote development against these lethal substances. The current standard treatment against their effects relies on the use of small molecule-based oximes that can efficiently restore acetylcholinesterase (AChE) activity. Despite their efficacy in reactivating AChE, the action of drugs like 2-pralidoxime (2-PAM) is primarily limited to the peripheral nervous system (PNS) and, thus, provides no significant protection to the central nervous system (CNS). This lack of action in the CNS stems from their ionic nature that, on one end makes them very powerful reactivators and on the other renders them ineffective at crossing the Blood Brain Barrier (BBB) to reach the CNS. In this report, we describe the use of an iterative approach composed of parallel chemical and in silico syntheses, computational modeling, and a battery of detailed in vitro and in vivo assays that resulted in the identification of a promising, novel CNS-permeable oxime reactivator. Additional experiments to determine acute and chronic toxicity are ongoing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Rectification of nanopores in aprotic solvents – transport properties of nanopores with surface dipoles

Nanopores have become a model system to understand transport properties at the nanoscale. In this paper, we report experiments and modeling of ionic current in aprotic solvents with different dipole moments through conically shaped nanopores in a polycarbonate film and through glass nanopipettes. We focus on solutions of the salt LiClO4, which is of great importance in modeling lithium based batteries. Results presented suggest ion current rectification observed results from two effects: (i) adsorption of Li+ ions to the pore walls, and (ii) a finite dipole moment rendered by adsorbed solvent molecules. Properties of surfaces in various solvents were probed by means of scanning ion conductance microscopy, which confirmed existence of an effectively positive surface potential in aprotic solvents with high dipole moments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

HOMP (Hybrid Operation and Maintenance Platform) [SWR-22-81]

The HOMP software module is contained within NREL's Hybrid Optimization and Performance Platform (HOPP) at this URL: https://github.com/NREL/HOPP/tree/feature/HOMP. HOMP is a python-based software module which can be used concurrently with the Hybrid Optimization and Performance Platform (HOPP) to model, simulate, and optimize degradation, reliability, and operations and maintenance of hybrid power plant components. The program currently models lithium ion battery, PEM electrolyzer, wind turbine, and solar PV array components, which enables users to understand the design and operation tradeoffs of hybrid power plants. For instance, HOMP enables users to answer questions like: how large does a battery bank need to be to achieve optimal charge/discharge rates for performance and reliability? How does plant design change for different objectives (e.g., profit versus resilience)? Under what conditions is it better to produce electricity versus hydrogen? How do we optimally control hybrid power plants to reduce downtime and increase performance?

Clark, Caitlyn↗
DOE OSTI2025

CEC Quest: Long Duration Energy Storage Impact Analysis Tool

SAND2025-14389O CEC Quest is a Python tool with a user interface designed to analyze the greenhouse gas impacts of long-duration energy storage projects in California. The tool automates data collection from public sources and uses an Application Programming Interface (API) to enable users to download photovoltaic resource availability, marginal operating emissions rate, and utility rate data. It guides users in inputting parameters for a battery energy storage model and uploading site electrical load data, while also prompting for relevant analysis parameters like timestep and grid limits. CEC Quest performs monthly optimization of one year of data to assess impacts on the site’s electrical bill and the grid’s greenhouse gas emissions. Finally, it conducts a lifecycle analysis to evaluate changes over a defined quantification period, with results aggregated through automated report generation. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Rosewater, David [Sandia National Lab. (SNL-CA), L↗
DOE OSTI2024

Hydrogen Storage for Load-Following and Clean Power: Duct-firing of Hydrogen to Improve the Capacity Factor of NGCC Plants (Final Report, Phase II Pre-Front End Engineering Design Study)

GTI Energy (GTI) and team members Southern Company Services (SCS), Pacific Gas & Electric (PG&E) and the Electric Power Research Institute (EPRI) performed a Phase II Pre-Feed Study under contract DE-FE0032008 for Hydrogen Storage for Load-Following and Clean Power. The configuration of the proposed system consists of subsystems for on-site H 2 production, on-site H 2 storage (up to 54 MWth in commercial vessels) and H 2 combustion in a duct burner in a Heat Recovery Steam Generator (HRSG) integrated with an existing fossil asset. Here, the firing rate of the duct burner is varied to let the plant respond to fluctuations of electrical load and H 2 production is relatively constant by storing H 2 . The proposed system is an improvement over alternate low carbon dispatchable power options. Technoeconomic analyses show H 2 produced with GTI’s patented Compact Hydrogen Generator (CHG) with inherent carbon capture will be lower cost (Levelized Cost of Hydrogen, LCOH) relative to hydrogen produced with a Steam Methane Reformer (SMR) with an amine system for carbon capture. This lower cost hydrogen enables our integrated system to deliver electricity (Levelized Cost of Electricity, LCOE) at 17.4% lower cost relative to a SMR sourced H 2 -fired HRSG (with an amine system for carbon capture) – Steam Turbine Generator (STG). Our analysis using EPRI’s US REGEN macroeconomic model shows our system will have significant demand in the power market and therefore require significant capacity expansion (CHG plants built to deliver hydrogen) to deliver low cost, low carbon power. In Phase II, our Team has completed a detailed system definition including the development of process models, definition of battery limits, Process Flow Diagrams (PFDs), Piping and Instrumentation Drawings (P&IDs), and a plant layout. A preliminary design for the key components of the CHG was developed including component lists & specifications. An evaluation of environmental and permitting considerations was completed and included the development of an Environmental Information Volume (EIV). The duct burners, which are flexible and can burn hydrogen and/or natural gas, were defined and initial CFD analyses were completed.

03 NATURAL GAS↗