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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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Presentation covers: (1) Safety Certification Process at NASA (2) Safety Testing for Lithium-ion Batteries (3) Limitations Observed with Li-ion Batteries in High Voltage and High Capacity Configurations.
This is an overview of EVALS (Enhanced VALidation of advanced battery Supply chains), funded by the U.S. Department of Energy Vehicle Technologies Office (VTO) and some initial data from a transformational laboratory directed research and development (LDRD) program project funded at NREL. EVALS aims to accelerate the process to bring domestic and allied primary sources of battery materials online, from production to deployment.
Forge Nano has recently developed an innovative strategy based on atomic layer deposition(ALD) of oxide coatings on battery separators and electrode materials. ORNL team worked with Forge Nano under this CRADA to construct larger format cells, validate battery and separator performance, and perform advanced materials characterization. It was observed that oxide coatings improve the battery cell performance in terms of both rate capability and long-term cyclic stability. ORNL team performed the research under this CRADA at DOE’s Battery Manufacturing Facility (BMF) at ORNL.
Forge Nano has recently developed an innovative strategy based on atomic layer deposition (ALD) of oxide coatings on battery separators and electrode materials. ORNL team worked with Forge Nano under this CRADA to construct larger format cells, validate battery and separator performance, and perform advanced materials characterization. It was observed that oxide coatings improve the battery cell performance in terms of both rate capability and long-term cyclic stability. ORNL team performed the research under this CRADA at DOE’s Battery Manufacturing Facility (BMF) at ORNL.
The major objective of this Cooperative Agreement was to develop a noise level specification for laboratory rats in the Centrifuge Facility Specimen Chambers (Space Station Biological Research Project), and to validate the specification for 3 noise octave bands: center frequencies 8 kHz, 16, kHz, and 32 kHz. This has been accomplished. Objective measures were used to verify that the chronic noise exposure was not harmful to the animals from physiological and behavioral perspectives. These measures were defined in the Stress Assessment Battery Validation for the Rat Acoustic Tolerance Test. In addition, the effects of the chronic noise exposure on rat hearing was assessed by the Brainstem Auditory Evoked Potential Method (BAER).
Lithium-ion battery (LiB) technology is playing a crucial role in transforming the predominantly fossil fuel-based transportation and stationary storage sectors to achieve a low-carbon economy. Rapid innovation in the LiB materials to electrode to cell design is happening to satisfy the performance, life, and safety metrics required by those myriads of applications. Lately, advanced analytics, such as machine-learning or artificial intelligence (ML/AI) techniques, are being used more frequently to aid in expedited LiB technology development, performance validation, and life prediction. The success of these techniques often relies on a large volume of well-defined and high-quality battery test data. On the other hand, most battery developers and research and development (R&D) communities are still following a classical approach to develop batteries, which is running calendar- and/or cycle-aging tests, performing reference performance tests (RPTs), and conducting post-mortem analyses periodically without paying attention to the wealth of data often not collected during the calendar or cycle life aging tests. This sparse data collection approach is time- and resource-intensive, requiring data capture and evaluation of months to years of RPT data to diagnose accurate battery state of performance, health, and safety. Even so, the underlying aging modes and mechanisms can be missed. If collected properly, battery test data during cycling or calendaring can be efficiently combined with ML/AI techniques to create powerful tools in the rapid diagnosis of battery state of performance, health, and safety along with insights into underlying aging modes and mechanisms. In this report, we discuss the importance of effective cycle-by-cycle (CBC) data collection with example case studies. Within a reasonable timeframe, RPT data are often inadequate in capturing many of the crucial battery aging dynamics, which often predominantly show up in CBC test data. Finally, we also show examples of ML/AI techniques that use CBC data in rapid diagnosis and projection of LiB state of health (SOH) to motivate the scientific community in collecting and using CBC data to facilitate expeditious technology development and validation.
EVALS is a consortium funded by the Vehicle Technologies Office at DOE involving Idaho National Lab, Argonne National Lab, and NREL. The goal of EVALS is to fully develop a suite of tools that support domestic electric vehicle manufacturing through evaluation of domestic primary resources and acceleration of their path to domestic material and battery production. This talk will focus on describing the EVALS project and discussing initial results regarding domestic LiFePO4 precursor sourcing and impacts on the domestic manufacturing supply chain.
As renewable energy deployment grows, hybrid power plants (HPPs) combining photovoltaic (PV) and battery systems must evolve to offer both energy and grid stability services. These systems typically include a mix of grid-following (GFL) and grid-forming (GFM) inverters, presenting unique coordination and control challenges. This Department of Energy–funded project developed and validated a Unified Universal Control and Coordination (UUCC) framework for such PV + battery hybrid plants, enabling seamless and stable operation, including ultrafast black start, autonomous synchronization, and robust frequency and voltage regulation, under different grid conditions. The project significantly advanced the understanding of inverter-based resource (IBR) control by developing and validating three complementary system-level approaches for hybrid GFL/GFM operation: 1. A combined Virtual Resistance (VR)-based GFL and Virtual Oscillator Control (VOC)-based GFM method, where each inverter type is governed by a specialized control strategy. Together, these achieve stable, fast-response coordination, eliminating inrush current and enabling smooth black start and grid synchronization across a wide range of grid strengths. 2. A Deadbeat-based UUCC strategy, which uses discrete-time, switching-cycle-level control for both GFL and GFM inverters. This approach replaces traditional PI/PLL control with a control parameter-free, high-bandwidth framework that supports stable LVRT and instantaneous synchronization under all conditions. 3. A benchmark comparison with Siemens’ commercial GFM microgrid controller, which provided a fast baseline platform. The commercial approach decoupled v & f control was implemented on a commercial microgrid controller.The baseline commercial benchmark helped highlight superior transient response and black start performance offered by the deadbeat and VOC approaches. These technical contributions offer substantial improvements over conventional inverter control schemes, which often rely on slow phase-locked loop (PLL)-based synchronization, require careful control parameters tuning, and prone to unstable in weak grids with GFL inverters and in stiff grid with GFM inverters therefore challenging for hybrid GFL+GFM under all grid conditions. The deadbeat-based UUCC framework enables simpler, faster, and more robust operation of hybrid IBR systems using wide-bandgap (WBG) devices such as SiC power semiconductors. The rapid expansion of hybrid distributed energy resources (DERs), including residential and commercial PV-BESS installations such as Tesla Powerwall, PV with vehicle-to-grid (V2G) capability, and other integrated configurations, presents complex operational challenges for medium-voltage radial distribution feeders. These networks are subject to frequent disturbances such as faults, switching operations, rapid reclosing sequences, and feeder reconfigurations, all of which introduce dynamic stress on IBRs. In addition, planned feeder segmentation and deliberate islanding for resilience will require DERs that can autonomously perform blackstart, establish voltage and frequency references, and resynchronize with the main grid. The advanced deadbeat-based UUCC control and blackstart functionalities developed in this project directly address these requirements, enabling decentralized and autonomous operation of inverter-dominated DERs in distribution systems under a wide range of fault and reconfiguration scenarios. From a public benefit perspective, these innovations enable more reliable and cost-effective integration of renewable energy into distribution networks. The ability to autonomously black start and stabilize grids under varying grid conditions support accelerates recovery from outages and support decentralized resilient energy systems. By reducing system complexity and improving performance, this project lays critical groundwork for future inverter-dominated power grids that are clean, reliable, and accessible to all.
A heat pipe integrated thermal battery system has been constructed to investigate a high-temperature latent heat thermal energy storage technology that takes advantage of near isothermal operation of latent heat storage and heat pipes to potentially enable high-energy isothermal heat storage. A dynamic model constructed in Modelica has been validated, showing errors between 2.5 °C–39.7 °C across 10-h to 47-h simulations against experiment results, showing good prediction capability of experiment output, especially against phase change time. Model calibrations showing vessel heat-up capability of 3 kW and heat pipes combining to provide 600 W each during experiment operation validate experiment circumstances including reduced material loading and reduced power capability. The experiment configuration uses an Al-Mg-Zn eutectic metal as the storage material, heated via heat tape wrapped around the vessel and guide tubes to bring the system to operation range (>400 °C) and to simulate charging heat exchange, respectively, with heat rejection occurring through the surfaces of the material and facilitated via guide tubes with less insulation wrapping. The model is available in the open-source repository HYBRID on Github.
General objectives and plan directions are given for current program support in the following areas: (1) solar cells and arrays; (2) batteries and fuel cells; (3) thermoelectric, thermionic, and Brayton cycle conversion systems; (4) circuits and subsystems for the management and distribution of power; and (5) the interactions of the environment with the power system and the spacecraft. Particular emphasis is given to the electrochemical energy conversion storage portion of the program where efforts are directed to improving the energy density and life of nickel cadmium batteries, to validating flight-weight silver hydrogen cells, to promoting the safe use of lithium primary batteries, to completing the silver zinc batteries and the orbital transfer fuel cell technology, to increasing the capacity of space batteries, to and to evaluating new electrochemical concepts for very high energy density. The use of the fuel cell electrolyzer concept for energy storage in both the dedicated and the truly regenerative mode is also being investigated.
An attempt was made to operationally define and measure strategic behavior in a complex multiple task environment. The Multi-Attribute Task battery was developed to simulate various aspects of flight and consisted of an auditory communication task, monitoring tasks, a tracking tasks, a resource management task which allowed a wide range of responding patterns, and a scheduling window which allowed operators to predict changes in workload. This battery was validated for its sensitivity to strategic behavior, and baseline measures for each individual task were collected. Twenty-four undergraduate and graduate students then performed the battery for four 64 minute sessions which took place over a period of 2 days. Each subject performed the task battery under four levels of workload, which were presented for equal lengths of time during all four sessions. Results indicated that in general, performance improves as a function of experience with the battery, but that performance decreased as workload level increased. The data also showed that subjects developed strategies for responding to the resource management task which allowed them to manage the high workload levels more efficiently. This particular strategy developed over time but was also associated with errors of complacency. These results are presented along with implications for the aviation field and areas of future research.
Two low-cost redox couples with near neutral or alkaline pH - 7,8-dihydroxyphenazine-2-sulfonic acid (DHPS) and chrome chelated ethylenediaminetetraacetic acid (Cr-EDTA) with ammonia – are identified to generate hydrogen evolution spontaneously on Pt/C catalysts. Cr-EDTA redox couple has two times higher hydrogen evolution rate than that of DHPS, and the charged Cr-EDTA molecules are fully utilized to generate hydrogen gas, while ~ 50% of DHPS molecules are utilized to produce hydrogen spontaneously on Pt/C catalysts. The Cr-EDTA electrolyte enables cost reduction by allowing cheap raw material of Cr and corrosion resistant alloy instead of costly superalloy for catalytic reactor systems. Furthermore, the Cr-EDTA with ammonia as a negolyte for flow batteries was validated to suppress hydrogen evolution side reaction, reach 99% of coulombic efficiency in flow cell operation paired with Fe(CN)6 redox couple near neutral pH.
Lithium-ion batteries are used in a wide variety of applications. To meet the power and energy demands of these applications battery packs are composed of hundreds to thousands of cells. The electrical and thermal interactions between cells introduce additional complexity in the pack dynamics. To capture these effects, a battery pack model composed of 192 cells based on a first-generation (2012) Nissan Leaf battery pack is developed in MATLAB/Simulink/Simscape. Here, with this model, we simulate the electrical dynamics (using a first-order equivalent-circuit model), the thermal dynamics (using a first-order lumped-parameter thermal model), and the aging dynamics (using a semi-empirical severity factor-based model) of every cell in the pack and we also create a pack thermal model that explicitly captures the heat exchange between the modules, and the cells contained within, during operation. The models are calibrated and validated, both at the cell and pack level, with experimental data. Two different case studies of this pack model are investigated. In the first case study, an initial, normally-distributed, cell-to-cell capacity variation is introduced and its effect on the pack voltage and module temperatures is studied. In the second case study, we deliberately insert cells with lower than nominal capacity into the pack and we investigate how this type of initial cell-to-cell capacity variation affects the pack’s ability to deliver energy over time. Finally, we also study how parallel-connected cells can reduce the effects of cell-to-cell variations at the expense of increased aging of the pack overall.
Strong binders can be counterproductive for silicon anodes. Here, we show that stresses from cycling Si-based electrodes can cause permanent stretching and wrinkling of the current collector. Furthermore, this deformation damages the electrode coating and accelerates cell aging due to the inactivation of Si domains and facilitation of Li plating. Interestingly, we demonstrate that the formation of wrinkles is size-dependent, being present in pouch cells but absent from coin cells. This size-dependent performance decay indicates that, in extreme cases, testing outcomes are highly dependent on scale and that the validation of battery materials may require testing at larger cell formats.
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This invention develops a mathematical model to describe battery behavior during individual discharge cycles as well as over its cycle life. The basis for the form of the model has been linked to the internal processes of the battery and validated using experimental data. Effects of temperature and load current have also been incorporated into the model. Subsequently, the model has been used in a Particle Filtering framework to make predictions of remaining useful life for individual discharge cycles as well as for cycle life. The prediction performance was found to be satisfactory as measured by performance metrics customized for prognostics for a sample case. The work presented here provides initial steps towards a comprehensive health management solution for energy storage devices.
The NASA Aerospace Flight Battery Systems Program is an agency-wide effort aimed at ensuring the quality, safety, reliability and performance of flight battery systems for NASA applications. The program provides for the validation of primary and secondary cell and battery level technology advances to ensure their availability and readiness for use in NASA missions. It serves to bridge the gap between the development of technology advances and the realization and incorporation of these advances into mission applications. The program is led by the Glenn Research Center and involves funded task activities at each of the NASA mission centers and JPL. The overall products are safe, reliable, high quality batteries for mission applications. The products are defined along three product lines: 1. Battery Systems Technology - Elements of this task area cover the systems aspects of battery operation and generally apply across chemistries. This includes the development of guidelines documents, the establishment and maintenance of a central battery database that serves a central repository for battery characterization and verification test data from tests performed under the support of this program, the NASA Battery Workshop, and general test facility support. 2. Secondary Battery Technology - l h s task area focuses on the validation of battery technology for nickel-cadmium, nickel-hydrogen, nickel-metal-hydride and lithium-ion secondary battery systems. Standardized test regimes are used to validate the quality of a cell lot or cell design for flight applications. In this area, efforts are now concentrated on the validation and verification of lithium-ion battery technology for aerospace applications. 3. Primary Battery Technology - The safety and reliability aspects for primary lithium battery systems that are used in manned operations on the Shuttle and International Space Station are addressed in the primary battery technology task area. An overview of the task areas supported under this program will be presented.
Interfaces play a pivotal role in dictating the performance and reliability of all-solid-state batteries (ASSBs), where complex electro-chemo-mechanical phenomena at grain boundaries (GBs) and interfaces can lead to degradation and failure. Traditional atomistic simulation methods, such as first-principles calculations and classical molecular dynamics, face limitations in modeling these interfaces due to either high computational cost or insufficient transferability to the diverse atomic environments evolving at interfaces. Machine-learning interatomic potentials (MLIPs) have emerged as a transformative approach, enabling large-scale, high-accuracy simulations of disordered and chemically complex systems by leveraging the predictability of machine learning models trained on first-principles data. Recent applications of MLIPs have demonstrated their ability to capture intricate behaviors at ASSB interfaces, including ion transport, interfacial evolution, and degradation mechanisms, with accuracy and efficiency unattainable by conventional methods. This prospective paper presents comprehensive analysis and practical guidance for MLIP development for GBs and interfaces in ASSBs, with a focus on three key pillars: data generation, model selection, and validation. Here, we review the current state of MLIP applications for GBs and interfaces in both general and ASSB-specific materials, highlighting best practices and challenges in constructing diverse and representative datasets, choosing appropriate machine learning architectures, and rigorously validating model performance. We also discuss emerging strategies and opportunities for improved reliability and efficiency of MLIPs to simulate realistic interfaces in ASSBs.