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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

High-Mileage Courier Fleet Vehicle Laboratory Battery Pack Testing

For one of each of the AVTA's plug-in hybrid electric vehicles, battery electric vehicles, and some hybrid electric vehicles tested in high-mileage courier fleets, the high-voltage traction battery packs were removed from the vehicle and tested at the beginning and end of fleet testing. For some vehicles, batteries also were tested at periodic intervals during fleet testing. Standard reference performance tests were conducted to characterize battery degradation over time. This dataset contains results from two or more rounds of battery tests for 21 distinct year/make/model vehicles (see reference ["INL Advanced Vehicle Testing Activity: On-road Logger and Laboratory Battery Pack Testing Vehicle List"](https://avt.inl.gov/sites/default/files/pdf/reports/DatasetVehicleList.pdf) for full list of vehicles). Each round of battery testing included the "Static Capacity Test" and the "Hybrid Pulse Power Characterization (HPPC) Test", conducted according to test procedures published in the United States Advanced Battery Consortium ["Battery Test Manual For Power-Assist Hybrid Electric Vehicles"](https://www.uscar.org/commands/files_download.php?files_id=57), ["Battery Test Manual For Plug-In Hybrid Electric Vehicles"](https://www.uscar.org/commands/files_download.php?files_id=168), and ["Electric Vehicle Battery Test Procedures Manual"](https://www.uscar.org/commands/files_download.php?files_id=5) prior to the time of testing. These tests were performed by Intertek Testing Services, North America. This dataset is shared by API; a small sample of the vehicle battery and test data has been extracted and is also available for download.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
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 OSTI2025

Methods and recommendations for large-scale deflagration testing of battery energy storage system enclosures

UL Solutions produced this report as a summary of a test series executed at Sandia National Laboratories that they funded. This report was approved for release by UL Solutions in October 2025 and released in 2026 on their website at https://www.ul.com/insights/methods-and-recommendations-large-scale-deflagration-testing-battery-energy-storage-system.

Gaudet, Benjamin [UL Solutions] (ORCID:00090006419↗
DOE OSTI2025

High-Voltage, Intermediate-Temperature, Fe- and Al-Mixed Metal Halide Molten Salt for Molten Sodium Battery Energy Storage

An inorganic Fe and Al halide-based, low-temperature molten salt catholyte is described, which, when paired with a molten sodium anode, has high operating potentials rivaling those of Li ion batteries. The newly developed catholyte consists of metal halides FeCl 3 /FeCl 2 –AlCl 3 –NaCl and is intended to cycle between Fe 3+ /Fe 2+ redox couples in the molten salt. The multicomponent molten salt was initially evaluated for phase behavior and basic electrochemical behavior before full battery testing. The assembled battery, utilizing a 20:35:45 (FeCl 3 :AlCl 3 :NaCl) composition, with a 50.83 Ah/kg theoretical gravimetric capacity and a specific energy of 176.95 Wh/kg, was cycled at variable depths of discharge (DoD) and current densities to determine its cycling efficiencies and limitations. In conclusion, preliminary cycling tests showed two operational potential regimes, with higher potential, 3.91 V (vs Na/Na + ), at low DoD and lower potential, 3.39 V (vs Na/Na + ), at high DoD with excellent energy efficiencies and cycling behavior under both regimes.

Aluminum↗
DOE OSTI2025

Utilization of Carbon Supply Chain Wastes and Byproducts to Manufacture Graphite for Energy Storage Applications

This project explored how coal and waste coal can be transformed into high-value graphite used in batteries for electric vehicles and power grids. A manufacturing technique that converts coal into carbon foam and subsequently graphite was developed and refined to improve the thermal and mechanical properties of the resulting materials. Cost-effectiveness and environmental impacts were also evaluated. Coal-derived graphite, especially when processed at high temperatures, was shown to perform comparably to commercial graphite in battery tests. Advanced computer simulations were used to model battery behavior and predict long-term performance, demonstrating that coal-based graphite has the potential to support electric vehicle deployment while reducing reliance on imported materials. This work offers a promising pathway for cleaner energy storage solutions and creates economic opportunities for coal-reliant communities by generating new uses for mining byproducts.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Miniaturize the Redox Flow Battery for Accelerated Materials Discovery and Development

Redox flow batteries are a promising technology for grid-scale energy storage. The aqueous organic redox flow battery is of particular interest for its potentially low material cost and sustainability. Developing novel organic active material for flow battery electrolytes typically entails molecular engineering toward desired properties, necessitating organic synthesis. In a research laboratory setting, the synthesis of specifically designed organic molecules featuring targeted functional groups is time and resources intensive. In the past, synthesizing materials required for battery testing has often required gram-scale production, presenting considerable constraints on the pace of novel organic material discovery. In this report, we introduce a miniaturized cell design that mandates only milligram-scale material synthesis while yielding testing outcomes equivalent or superior to those reported with other commercially available or homemade flow cells in the literature. The test results under various pH conditions validate the scale-down strategy to accelerate the flow battery material discovery and development using the newly designed mini cell. This approach offers researchers an efficient means to notably reduce the time and resources required to develop novel materials for flow batteries.

25 ENERGY STORAGE↗
DOE OSTI2024

Recycled graphite enabled superior performance for lithium ion batteries

Recycling graphite attracts growing attention since cumulative amount of spent Li-ion batteries and the shortage of graphite supply chain. Although various recycling methods have been reported, the recycled graphite cannot reach the strict commercial standards of purity, scalability, efficiency, and capacity, preventing it from battery manufacturing. Herein, the important roles of defects and functional groups on the graphite surface are deeply studied, and a closed-loop graphite recycling process with the surface recovery and modification for the graphite from the end-of-life batteries is demonstrated. The recovered graphite delivers a purity of over 99.9 % and an average initial coulombic efficiency of 91.5 %. Compared with commercial graphite in industrial standard battery testing parameters, full cells with recovered graphite possess enhanced rate reversibility, doubled cycle life, over 10 % higher capacity along with half anode material cost. In conclusion, these impressive results not only underscore the transformative potential of surface reconstruction and modification in graphite recycling, but also present economic feasibility and sustainable pathway for significantly improving battery performance and addressing global resource challenges via integration with the hydrometallurgical recycling process.

25 ENERGY STORAGE↗
DOE OSTI2025

Highly crystalline, low-ash, graphite from coal using an Fe 2 O 3 -based catalytic process with recovery and reuse of catalyst and process acid

This study presents a sustainable process for producing highly crystalline, low-ash graphite from sub-bituminous coal using an Fe 2 O 3 -based catalytic method. The process integrates coal mineral removal, catalyst regeneration, and reagent recycling into a closed-loop system. Acid-soluble Fe-residue and mineral impurities are eliminated from the solid graphite through HCl treatment, followed by hydrolytic distillation to regenerate Fe 2 O 3 and recover HCl for reuse. Coal-derived silica is removed with a KOH rinse, yielding low-ash graphite suitable for high-performance applications. The closed-loop catalytic graphitization, where the recovered Fe 2 O 3 and HCl are used in subsequent graphitization runs, produces graphite with a degree of graphitization exceeding 95%. The L a and L c crystallite sizes reach 65–78 nm and 44–48 nm, respectively, with BET surface areas of 4–10 m 2 /g and an ash content below 0.1 wt.%. Lithium-ion battery testing reveals that anodes fabricated with this graphite deliver an initial discharge capacity between 384.5 and 421.2 mAh/g, averaging 395.0 ± 19.1 mAh/g, along with initial coulombic efficiencies of 85.0–89.1%. After 100 discharge–charge cycles at 0.25C, reversible capacities remain between 358.8 and 369.7 mAh/g, while coulombic efficiency stays above 99.9%. The findings highlight that coal can serve as a viable precursor for high-quality graphite production under relatively mild conditions, avoiding the need for extreme temperatures or aggressive reagents such as hydrofluoric acid, commonly employed in conventional processes. This work demonstrates both technical feasibility and environmental benefits, emphasizing its potential to support large-scale, sustainable graphite production for applications such as lithium-ion batteries.

Catalytic graphitization↗
DOE OSTI2025

Conformational Control as a Design Strategy to Tune the Redox Behavior of Benzotriazole Negolytes for Nonaqueous Flow Batteries

Here, we present a molecular engineering strategy to tune the reduction potentials of benzotriazole derivatives as high-energy-density negolytes in nonaqueous redox flow batteries. Within nonaqueous electrolytes, these derivatives, notably 2-(o-tolyl)-2H-benzo[d][1,2,3]triazole (1), demonstrate a theoretical capacity of up to 93.8 Ah L⁻¹ and a reduction potential of –2.35 V vs ferrocene/ferrocenium (Fc/Fc⁺). Introducing dimethyl substitution (i.e., 2-(2,6-dimethylphenyl)-2H-benzo[d][1,2,3]triazole (4)) shifts the reduction potential even more negatively to –2.55 V vs Fc/Fc⁺. We ascribe the nonlinear effect of dimethyl substitution on reduction potential to ground-state conformational effects. Flow battery tests with negolyte 1 and ferrocene posolyte demonstrated >90% Coulombic efficiency at 6.7 mA cm⁻² with improved cyclability in the presence of lithium bis(trifluoromethylsuylfonyl)imide supporting salt.

25 ENERGY STORAGE↗
DOE OSTI2025

A General Strategy for Bandgap Engineering Via Anion‐Lattice Doping in High‐Entropy Oxides

Bandgap engineering is a critical tool for tailoring the electronic properties of functional materials, traditionally achieved by modifying the cation sublattice. Here, a generalizable strategy is introduced that leverages facile anion-lattice doping in high entropy materials to modulate the bandgap in high-entropy metal oxides (HEMOs). By incorporating nitrogen into a single-phase high-entropy metal oxide/nitride (HEMO:HEMN) solid solution, a substantial bandgap reduction is achieved from 3.55 eV (HEMO) to ≈2.46 eV (HEMO:HEMN), significantly enhancing electronic conductivity. Unlike conventional bandgap tuning approaches that rely on cation substitution or heterojunction formation, this method exploits anion-mediated entropy stabilization, enabling uniform bandgap narrowing across the entire solid solution. This anion-lattice engineering strategy is broadly applicable to high-entropy systems, providing a new pathway for designing energy materials with tailored electronic properties. The resulting HEMO:HEMN solid solution exhibits a tenfold increase in capacitance and capacity compared to HEMO in supercapacitor and lithium-ion battery tests, demonstrating the transformative potential of anion-driven bandgap modulation for next-generation energy storage and conversion technologies.

energy storage↗
DOE OSTI2026

Structure–Property Relationships of Recycled Lithium-Ion Battery Cathodes: Microstructure Optimization Using Virtual Materials Testing

The increasing demand for sustainable battery technologies requires effective recycling strategies for end-of-life lithium-ion battery cathodes. In this study, virtual materials testing, a well-established framework for modeling conventionally manufactured NMC-based cathodes, is applied to partially recycled cathodes. To this end, virtual cathodes consisting of mixtures of pristine and recycled NMC particles are utilized to systematically analyze structure–property relationships depending on mixing ratios and different spatial arrangement strategies. For this purpose, a stochastic 3D model is developed that is capable of generating virtual cathodes with arbitrary volume fractions of active materials and mixing ratios of pristine and recycled NMC particles. Particularly, the stochastic 3D model can mimic the different size distributions of pristine and recycled particles that are observed in image data. Additionally, the model allows the structuring of pristine and recycled NMC either uniformly mixed or layer-wise arranged, mimicking single- and dual-layer cathodes. Subsequently, a systematic computational analysis is conducted to assess the influence of increasing active material ratios of recycled particles, ranging from 0 % to 100 %, while maintaining a constant overall active material volume fraction. The impact of particle mixing on cathode performance is evaluated by examining transport-relevant geometrical descriptors and effective properties, such as geodesic tortuosity, specific surface area, and tortuosity factor.

25 ENERGY STORAGE↗
DOE OSTI2026

High-Mileage Courier Fleet Vehicle On-Road Logger Data

This dataset describes the performance and fuel efficiency of AVTA test vehicles operating in commercial courier fleets the Phoenix, AZ metro area between 2010 and 2016. Aftermarket data loggers were installed in two to four vehicles of each of 30+ distinct year/make/models (see reference ["INL Advanced Vehicle Testing Activity: On-road Logger and Laboratory Battery Pack Testing Vehicle List"](https://avt.inl.gov/sites/default/files/pdf/reports/DatasetVehicleList.pdf) for full list of vehicles). Loggers recorded vehicle operation as they were driven up to 160,000 miles in up to three years of fleet testing. Parameters were logged at 1-second intervals, including - vehicle speed, - engine and/or electric motor speed, - fuel and/or electricity consumption, and - ambient temperature. This dataset includes both raw second-by-second data and trip-level metrics. This dataset is shared by API; a small sample of the vehicle and logger data has been extracted and is also available for download.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Revealing the Roles of CuF 2 /NiF 2 Incorporation in the Electrochemical Performance of FeF 3 Cathodes in Solid‐State Batteries

Mixed metal fluorides have been considered as a promising candidate to lower the voltage hysteresis of conversion-type iron fluoride cathodes, but their cycling stability is limited due to transition metal dissolution and interphase growth in liquid electrolyte batteries. Here, we study the role of incorporating CuF 2 and NiF 2 in the electrochemical performance of FeF 3 cathode in halide-based solid-state batteries to test whether we can transfer the kinetic benefit of low voltage hysteresis to solid-state batteries while using solid electrolyte to eliminate transition metal dissolution and stabilize the interphase. Synchrotron X-ray absorption spectroscopy results indicated the redox reactions are attributed to Cu 0 /Cu + and Fe 0 /Fe 2+ in 25CuF 2 -75FeF 3 and Ni 0 /Ni 2+ and Fe 0 /Fe 3+ in 10NiF 2 -90FeF 3 . While no apparent improvement in electrode kinetics can be observed, the incorporation of CuF 2 and NiF 2 can largely improve the cycling stability of FeF 3 cathodes. In conclusion, the results demonstrate the advantages of using solid-state concept to improve the cycling stability of conversion-type cathodes.

25 ENERGY STORAGE↗
DOE OSTI2026

An Accelerated Testing and Analysis Framework for Qualification of Battery Materials

The continuously growing demand for batteries used within automotive, aviation, and grid applications has exacerbated the need to supplement critical battery material feedstocks, such as those for anode and cathode active materials. New or supplementary material sources, however, universally comprise unique material properties that can significantly affect the lifetime and performance of resultant batteries. As such, the influence of composition, microstructure, and morphology on electrochemical performance should be characterized quickly and accurately to accelerate commercialization of new material sources. This work introduces a tiered framework to quickly assess new material viability and understand the influence of physicochemical properties on battery performance. The Tier 1 testing described here is rapid and lower-effort to quickly recognize materials with fundamental flaws and potentially disqualify them. Later testing would require more time and effort but provide higher fidelity information with a goal of validating materials for specific applications. A case study examining various commercial sources of LiFePO4 (LFP) is presented, using Tier 1 of the protocol to identify rapid electrochemical and physicochemical signals that correlate with performance and to provide early go/no-go decisions on LFP materials without the need for long-term cycling data.

25 - ENERGY STORAGE↗
DOE OSTI2026

An Accelerated Testing and Analysis Framework for Qualification of Battery Materials Part I: A Case Study with LFP Cathodes

The growing demand for batteries used within automotive, aviation, and grid applications has exacerbated the need to supplement critical battery material feedstocks, such as those for anode and cathode active materials. New or supplementary material sources, however, universally comprise unique properties that can affect the lifetime and performance of resultant batteries. Even minor differences between new sources and established supplies can delay qualification, making it difficult for new suppliers to commercialize and resulting in a less resilient supply chain. Accordingly, the influence of composition, microstructure, and morphology on electrochemical performance should be characterized quickly and accurately to accelerate commercialization of new sources. This work introduces a tiered framework to assess new material viability and understand the influence of physicochemical properties on battery performance. The Tier 1 testing described here is rapid and low-effort to recognize materials with fundamental flaws and potentially disqualify them. Later testing would require more effort but provide higher-fidelity information with a goal of application-based validation. A case study examining commercial sources of LiFePO4 (LFP) is presented, using Tier 1 of the protocol to identify rapid electrochemical and physicochemical signals that correlate with performance and provide early go/no-go decisions for LFP materials without requiring long-term cycling.

25 ENERGY STORAGE↗
DOE OSTI2025

CalCharge CRADA000008852 Master Agreement Amendment 1, Battery Consortium – Proprietary Activities

Lawrence Berkeley National Laboratory (LBNL) is partnering with the California Clean Energy Fund to launch CalCharge, an energy storage innovation accelerator, comprised of emerging and established California companies and related organizations developing battery technologies for the electric/hybrid vehicle transportation, the electric grid and consumer electronics markets. The vision of CalCharge is to accelerate the pace of technology innovation, business growth, and cluster development. Calcharge programs will deliver technology acceleration and technical expertise to the energy storage industry, as well as policy and market development support to strengthen the regional economy. LBNL shall collaborate with CalCharge members on the analysis and testing of battery and energy storage technologies. LBNL’s work will include analysis and testing of external design, examination of materials and components either separately or as a whole, and providing data and observations resulting from each collaboration. LBNL shall maintain and provide access to LBNL specialized facilities for research activities performed by or for CalCharge members. In addition, LBNL will provide expertise for short-term consultation, interpretation of testing data, or to clarify technical obstacles if requested by a Member. Over this time frame, Calcharge partnered with several start-ups to provide analytical resources. Those companies include Halotechnics, ZAF Energy Systems, Volkswagen Group of America, Toyota Motor Corporation, and Ensor Inc.

25 ENERGY STORAGE↗
DOE OSTI2025

Data-Driven State of Health Estimation for Second-Life Batteries Using Interpolated Synthetic Data and Feature Selection

Accurate estimation of the State of Health (SOH) for second-life batteries (SLBs) is crucial given their increasing use in energy storage applications. Precise SOH prediction is essential for safe operation and robust battery management systems. A major challenge is the limited availability of datasets for building reliable degradation models. To address this, synthetic data generation through linear interpolation is performed to extend the available data, making it more representative of real-world battery operating conditions. By analyzing feature correlation with SOH, the most relevant features are selected for the model. The proposed approach employs a convolutional neural network (CNN) model trained on this interpolated, feature-selected dataset, using time series data of voltage, temperature, and current over a cycle. By focusing on highly correlated features, the model achieves over 95% accuracy, with mean absolute error and root mean squared error up to 2.27% and 2.64%, respectively, in SOH estimation for two battery datasets tested. These results highlight the potential of combining synthetic data generation and feature selection to enhance SOH predictions, showcasing the superior performance of the proposed CNN model for both new batteries and SLBs.

feature selection↗
DOE OSTI2026

Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling as Catalysts for Next-Generation Breakthroughs

The Presidential Symposium (PRES) at the 2025 Fall Meeting, hosted by the President’s Office and Energy and Fuels Division, American Chemical Society (ACS) in Washington, DC, brought together a diverse group of chemists, engineers, and materials scientists working in battery materials & systems, automation and artificial intelligence from academia, industry, and national laboratories. The accelerating demand for high-performance, scalable, and sustainable energy storage has catalyzed a paradigm shift in how materials are dis-covered, devices are engineered, and systems are optimized. This Presidential Symposium, entitled “Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling Driving Next-Gen Breakthroughs”, brings together global leaders to unveil transformative strategies anchored in the AAA framework: Artificial Intelligence, Automation, and Advanced Modeling. Artificial Intelligence is redefining the frontiers of energy storage by enabling predictive design, real-time optimization, and intelligent control across diverse chemistries and architectures. Automation is streamlining the synthesis, characterization, and testing of battery materials, dramatically accelerating innovation cycles and unlocking scalable solutions for grid and mobility applications. Advanced Modeling, spanning atomic to system-level scales, provides unprecedented insight into electrochemical dynamics, degradation pathways, and thermal behavior, particularly when coupled with physics-informed machine learning and digital twin technologies. Digital twins, in turn, leverage the AAA framework by integrating real-time data, physics-based models, and AI predictions into dynamic virtual replicas, enabling proactive diagnostics, optimization, and system resilience. Together, these synergistic pillars are not only re-shaping the scientific landscape but also forging a new era of reproducible, data-driven, and resilient energy storage innovation. In conclusion, this symposium marks a pivotal moment in the convergence of computational intelligence and experimental rigor, charting the course for next-generation breakthroughs in lithium-ion, solid-state, and flow battery technologies.

Artificial Intelligence (AI)↗