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Results for “All-solid-state battery”

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
NASA NTRSDate not supplied

Improving Cell-Level Specific Energy for All-Solid-State Lithium Sulfur Batteries

All-solid-state lithium-sulfur (Li-S) batteries are considered as one of the top choices toward 500 Wh/kg of specific energy, a key metric for an energy storage system to enable large regional electric aircrafts. Many obstacles remain, such as S utilization in the cathode, cyclability regarding both cathode and anode as well as electrode-electrolyte interfaces, and effective means to increase the S content within the all-solid-state cell architecture. The latter is directly related to cell-level specific energy when considering the weights of all battery cell components. In this presentation, we discuss the efforts in both cathode optimization and cell-level improvement toward increasing the overall specific energy. Various strategies for improving S utilization and reducing the solid electrolyte layer thickness will be presented.

Solid state batteries↗
NASA NTRS2017

Sintered Cathodes for All-Solid-State Structural Lithium-Ion Batteries

All-solid-state structural lithium ion batteries serve as both structural load-bearing components and as electrical energy storage devices to achieve system level weight savings in aerospace and other transportation applications. This multifunctional design goal is critical for the realization of next generation hybrid or all-electric propulsion systems. Additionally, transitioning to solid state technology improves upon battery safety from previous volatile architectures. This research established baseline solid state processing conditions and performance benchmarks for intercalation-type layered oxide materials for multifunctional application. Under consideration were lithium cobalt oxide and lithium nickel manganese cobalt oxide. Pertinent characteristics such as electrical conductivity, strength, chemical stability, and microstructure were characterized for future application in all-solid-state structural battery cathodes. The study includes characterization by XRD, ICP, SEM, ring-on-ring mechanical testing, and electrical impedance spectroscopy to elucidate optimal processing parameters, material characteristics, and multifunctional performance benchmarks. These findings provide initial conditions for implementing existing cathode materials in load bearing applications.

Ceramics↗
NASA NTRSDate not supplied

A materials-informatics based study of solid electrolytes and protective coatings for Li batteries

All-solid-state batteries with Li metal anode can address the safety issues surrounding traditional Li-ion batteries as well as the demand for higher energy densities. However, the development of solid electrolytes and protective coatings simultaneously possessing high ionic conductivity and wide electrochemical stability has proven to be a challenge. Here, we present a data-driven approach to explore the Li compound space for promising solid electrolytes and coatings. This is accomplished through the generation of a large database of battery-related materials properties of Li compounds by computing Li+ migration barriers using bond-valence-based pair potentials, and stability windows using density functional theory energies. Using this database, we implement machine learning models that can accurately predict migration barriers and electrochemical stability windows for any new Li compound. Through feature engineering, we ensure that our models are both accurate and interpretable. We perform feature importance analysis on our models to highlight materials properties that can be tuned for future design of coatings/electrolytes. Our database and informatics approach provide a valuable tool for the rapid discovery of new solid-state battery chemistries.

Solid state batteries↗
NASA NTRSDate not supplied

Interpretable Tree-Based and Graph Neural Network Approaches for Novel Solid State Electrolyte Design

All-solid-state batteries with Li metal anode can address the safety issues surrounding traditional Li-ion batteries as well as the demand for higher energy densities. However, the development of solid electrolytes simultaneously possessing high ionic conductivity and good chemical and electrochemical stabilities has proven to be a challenge. I will present our informatics approach to explore the Li compound space for promising solid electrolytes using high-throughput multi-property screening and interpretable machine learning. This is accomplished through the generation of a large database of battery-related materials properties of Li compounds. We use tree-based ensemble learning methods and graph neural network approaches to accurately learn relationships between crystal structures and corresponding thermodynamic and kinetic properties, with interpretability being a major focus. Our models give us the ability to enable rapid discovery and design of novel solid-state battery chemistries.

Materials discovery↗
NASA NTRSDate not supplied

Interpretable ML Approaches for Novel Solid State Electrolyte Design

All-solid-state batteries with Li metal anode can address the safety issues surrounding traditional Li-ion batteries as well as the demand for higher energy densities. However, the development of solid electrolytes simultaneously possessing high ionic conductivity and good chemical and electrochemical stabilities has proven to be a challenge. I will present our informatics approach to explore the Li compound space for promising solid electrolytes using high-throughput multi-property screening and interpretable machine learning. This is accomplished through the generation of a large database of battery-related materials properties of Li compounds. We use tree-based ensemble learning methods and graph neural network approaches to accurately learn relationships between crystal structures and corresponding thermodynamic and kinetic properties, with interpretability being a major focus. Our models give us the ability to enable rapid discovery and design of novel solid-state battery chemistries.

Shreyas J Honrao↗
NASA NTRSDate not supplied

Hierarchical screening for Li-based solid electrolytes using fast, interpretable machine-learned potentials

Li-based solid-state electrolyte materials enable safer, all-solid-state batteries but the computational search for candidates with favorable stability and Li-ion conductivity is challenging due to the size of the search space and the cost of evaluating transport properties with ab initio methods. The prohibitive cost of high-throughput screening with DFT has lead to the development of surrogate models using geometric analysis, empirical potentials, and descriptors for ionic transport. Here, I will discuss a hierarchical screening approach for identifying promising materials using a combination of density functional theory, bond-valence methods, and machine learning potentials generated with the Ultra-Fast Force Fields (UF3) framework. We show how the inexpensive bond-valence method can be used to guide the generation of training samples for machine learning, in addition to filtering candidates. Finally, we apply the hierarchical workflow to screen for ionic conductivity across a database of Li-containing compounds.

Materials discovery↗
NASA NTRSDate not supplied

Hierarchical Screening for Li-Based Solid Electrolytes Using Fast, Interpretable Machine-Learned Potentials

Li-based solid-state electrolyte materials enable safer, all-solid-state batteries but the computational search for candidates with favorable stability and Li-ion conductivity is challenging due to the size of the search space and the cost of evaluating transport properties with ab initio methods. The prohibitive cost of high-throughput screening with DFT has lead to the development of surrogate models using geometric analysis, empirical potentials, and descriptors for ionic transport. Here, I will discuss a hierarchical screening approach for identifying promising materials using a combination of density functional theory, bond-valence methods, and machine learning potentials generated with the Ultra-Fast Force Fields (UF3) framework. We show how the inexpensive bond-valence method can be used to guide the generation of training samples for machine learning, in addition to filtering candidates.

Materials discovery↗
NASA NTRSDate not supplied

High-Throughput Screening of Li Solid-State Electrolytes With Bond Valence Methods and Graph Neural Networks

Li-based solid-state electrolyte (Li-SSE) materials enable safer, all-solid-state batteries but the computational search for candidates with favorable stability and Li-ion conductivity is challenging due to the size of the search space and the cost of evaluating transport properties with ab initio methods. We present a high-throughput screening approach for Li-SSE materials using a combination of bond-valence methods and graph neural networks. We demonstrate the screening approach with a dataset containing tens of thousands of Li-containing compounds. Furthermore, we combine the machine-learning screening procedure with an isovalent substitution scheme to generate and screen additional Li SSE candidates beyond existing databases. Finally, we discuss relative importances of geometric and bond-valence quantities in the training of graph neural networks, providing insight for future modeling of ionic conductivity in Li-SSE materials.

Materials discovery↗
NASA NTRSDate not supplied

Screening of Li-Based Solid Electrolytes Using Bond-Valence Methods and Graph Neural Networks

Li-based solid-state electrolyte (Li-SSE) materials enable safer, all-solid-state batteries but the computational search for candidates with favorable stability and Li-ion conductivity is challenging due to the size of the search space and the cost of evaluating transport properties with ab initio methods. We present a high-throughput screening approach for Li-SSE materials using a combination of bond-valence methods and graph neural networks. We demonstrate the screening approach with a dataset containing tens of thousands of Li-containing compounds. Furthermore, we combine the machine-learning screening procedure with an isovalent substitution scheme to generate and screen additional Li SSE candidates beyond existing databases. Finally, we discuss relative importances of geometric and bond-valence quantities in the training of graph neural networks, providing insight for future modeling of ionic conductivity in Li-SSE materials.

Materials discovery↗
NASA NTRS1994

Photocured Gelled Electrolytes For Secondary Li Cells

Class of photocured polymers exhibiting lithium-ion conductivities greater than those of well-studied polymers based on polyethylene oxide (PEO) show promise as polymeric electrolytes in rechargeable lithium cells. Increase in conductivity occasioned by use of electrolytes, coupled with amenability of electrolytes to formation into uniform thin (less than 25 micrometers thick), wide films, expected to result in cells with power densities greater than 100 W h/kg and charge/discharge rates exceeding currents equal, in amperes, to ampere-hour ratings. All-solid-state lithium batteries containing these electrolytes used as high-power, high-rate rechargeable power sources in commercial and aerospace applications.

Nagasubramanian, Ganesan↗
NASA NTRS2018

Freeze Casting of LAGP for 3D Textured Solid-State Structural Electrolytes

In this study, all-solid-state structural lithium-ion batteries, a type of load bearing electrochemical energy storage that provides systems-level weight savings is being pursued for the realization of inherently safe next generation hybrid-electric and all-electric green aerospace propulsion systems. Currently investigated all-solid-state batteries do not meet the requirements for specific power and mechanical stability. To address these issues, freeze casting of lithium aluminum germanium phosphate (LAGP) electrolyte material has been explored for the creation of a textured 3D electrolyte scaffold with large interfacial surface area for high power discharge and hierarchical porosity for accommodation of active material volume changes during electrochemical cycling. We report the effects of freeze casting processing parameters on the microstructural development and mechanical performance of the scaffolds, characterized through scanning electron microscopy and ring-on-ring mechanical testing. Slurry composition and casting parameters such as solids loading, casting speed, tape angle, and temperature gradients have been modified to determine the impact on density, lamellar morphology, and final load-bearing performance.

ceramics↗
NASA NTRS2009

NiF2/NaF:CaF2/Ca Solid-State High-Temperature Battery Cells

Experiments and theoretical study have demonstrated the promise of all-solid-state, high-temperature electrochemical battery cells based on NiF2 as the active cathode material, CaF2 doped with NaF as the electrolyte material, and Ca as the active anode material. These and other all-solid-state cells have been investigated in a continuing effort to develop batteries for instruments that must operate in environments much hotter than can be withstood by ordinary commercially available batteries. Batteries of this type are needed for exploration of Venus (where the mean surface temperature is about 450 C), and could be used on Earth for such applications as measuring physical and chemical conditions in geothermal wells and oil wells. All-solid-state high-temperature power cells are sought as alternatives to other high-temperature power cells based, variously, on molten anodes and cathodes or molten eutectic salt electrolytes. Among the all-solid-state predecessors of the present NiF2/NaF:CaF2/Ca cells are those described in "Solid-State High-Temperature Power Cells" (NPO-44396), NASA Tech Briefs, Vol. 32, No. 5 (May 2008), page 40. In those cells, the active cathode material is FeS2, the electrolyte material is a crystalline solid solution of equimolar amounts of Li3PO4 and LiSiO4, and the active anode material is Li contained within an alloy that remains solid in the intended high operational temperature range.

West, William↗
NASA NTRSDate not supplied

Microstructure Quantification and Random Forest Regression Models for Li4Ti5O12–Ni Property Prediction

All-solid-state structural lithium-ion batteries are sought to enable all-electric propulsion in next generation aerospace concepts through improved safety and systems level weight savings. In this work, the influence of processing conditions on microstructural evolution was evaluated for anode composites of strain-free Li4Ti5O12 and metallic nickel current collector. Beyond size distributions, this study explored methods of quantifying microstructural features that describe changes in the spatial distribution and coalescence of nickel particles as a function of sample composition and sintering conditions. Processing-microstructure-property relationships were described by microstructure quantifiers including nickel particle count per area, nearest neighbor distance distribution, and edge-to-edge distance distribution. Machine learning methods were applied to compare the relative influence of processing conditions and microstructural features on electrical conductivity and mechanical strength to optimize for simultaneous energy storage and load bearing performance. Insights gained from this work inform future evaluation of alternative energy storage materials and microstructures for multifunctional performance, and generation of microstructural descriptors strengthens modeling across length scales.

anode↗