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Results for “Solid-state Li-ion 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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Tuning collective anion motion enables superionic conductivity in solid-state halide electrolytes

Halides of the family Li 3 MX 6 (M = Y, In, Sc and so on, X = halogen) are emerging solid electrolyte materials for all-solid-state Li-ion batteries. They show greater chemical stability and wider electrochemical stability windows than existing sulfide solid electrolytes, but have lower room-temperature ionic conductivities. Here, in this work, we report the discovery that the superionic transition in Li 3 YCl 6 is triggered by the collective motion of anions, as evidenced by synchrotron X-ray and neutron scattering characterizations and ab initio molecular dynamics simulations. Based on this finding, we used a rational design strategy to lower the transition temperature and thus improve the room-temperature ionic conductivity of this family of compounds. We accordingly synthesized Li 3 YCl x Br 6-x and Li 3 GdCl 3 Br 3 and achieved very high room-temperature conductivities of 6.1 and 11 mS cm -1 for Li 3 YCl 4.5 Br 1.5 and Li 3 GdCl 3 Br 3 , respectively. These findings open new routes to the design of room-temperature superionic conductors for high-performance solid batteries.

25 ENERGY STORAGE↗
DOE OSTI2024

Unlocking Li superionic conductivity in face-centred cubic oxides via face-sharing configurations

Oxides with a face-centred cubic (fcc) anion sublattice are generally not considered as solid-state electrolytes as the structural framework is thought to be unfavourable for lithium (Li) superionic conduction. Here we demonstrate Li superionic conductivity in fcc-type oxides in which face-sharing Li configurations have been created through cation over-stoichiometry in rocksalt-type lattices via excess Li. We find that the face-sharing Li configurations create a novel spinel with unconventional stoichiometry and raise the energy of Li, thereby promoting fast Li-ion conduction. The over-stoichiometric Li–In–Sn–O compound exhibits a total Li superionic conductivity of 3.38 × 10 -4 S cm -1 at room temperature with a low migration barrier of 255 meV. Our work unlocks the potential of designing Li superionic conductors in a prototypical structural framework with vast chemical flexibility, providing fertile ground for discovering new solid-state electrolytes.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Visualizing diverse lithium growth and stripping behaviors in anode-free solid-state batteries with operando X-ray tomography

Anode-free solid-state batteries (SSBs), which eliminate the need for lithium metal use during cell assembly, have the potential to enable high energy densities and simplified manufacturing. However, the factors that control lithium growth/stripping at the anode current collector are not well understood. Here, we use operando X-ray microcomputed tomography to comprehensively image and quantify lithium deposition and stripping under various conditions in three different Li|Li 6 PS 5 Cl|current collector cells, revealing diverse behavior that depends on interface morphology, cell resistance, and solid-state electrolyte (SSE) microstructure. A cell with high resistance exhibits extensive lithium filament growth across the entire current collector interface, with filaments that grow around pre-existing pores in the SSE rather than lithium filling these pores. Lithium filament formation is partially reversible, with the cracks shrinking as lithium metal is stripped. Uniform lithium deposition is achievable at low current densities in low-resistance cells, whereas higher current densities in these cells cause an increase in interfacial roughness, which is correlated with subsequent filamentary growth at the edges of the cell. These results provide insight into filamentary vs. planar lithium growth and highlight that the evolution of lithium is sensitively dependent on SSE microstructure and electrochemical processes.

25 ENERGY STORAGE↗
DOE OSTI2025

Fast lithium ion diffusion in brownmillerite Li x Sr 2 Co 2 O 5

Transition metal oxides not only exhibits novel magnetic properties but also provides outstanding ionic transports. Ionic conductors have great potential for interesting tunable physical properties via ionic liquid gating and novel energy storage applications such as all-solid-state lithium batteries. In particular, low migration barriers and high hopping attempt frequency are the keys to achieve fast ion diffusion in solids. Taking advantage of the oxygen-vacancy channel in Li x Sr 2 Co 2 O 5 , we show that migration barriers of lithium ion are as small as 0.28–0.17 eV depending on the lithium concentration rates. Our first-principles calculation also investigated hopping attempt frequency and concluded the room temperature ionic diffusivity and ion conductivity are high as 10 −7 –10 −6 cm 2 s −1 and 10 −3 –10 −2 Scm −1 , respectively, which outperform most of perovskite-type, garnet-type, and sulfide Li-ion solid-state electrolytes. This work proves Li x Sr 2 Co 2 O 5 as a promising super-ionic conductor.

Crystallographic defects↗
DOE OSTI2022

All Solid-State Li/LLZO/LCO Battery Enabled by Alumina Interfacial Coating

Li 7 La 3 Zr 2 O 12 (LLZO) garnet-type lithium-ion conductors are being investigated as a promising solid electrolyte for solid-state lithium batteries. To enable a functional all-solid-state configuration intensive investigations are needed to reduce the cathode/electrolyte interfacial resistance which contributes the most to cell performance loss. Among the commercial cathode materials investigated so far, LiCoO 2 (LCO) is one of the most stable with garnet electrolytes as only a superficial reaction has been detected between the two materials. However, even this minor reaction would block the Li-ion transport through the interface, resulting in deteriorated cell performance. In this work, we demonstrate that a thin aluminum oxide layer (5 nm) can be an effective interlayer to impede the formation of a harmful interphase and enable facile Li-ion transfer between LCO and the LLZO garnet. Room-temperature-sputtered LCO thin films were employed to form an interface with the garnet electrolyte and annealed at 800 °C to reveal the effect of the interfacial reaction on the Li-ion transfer across the interface. An aluminum oxide layer was then introduced between LCO and the garnet electrolyte by sputtering a metallic aluminum layer which is then annealed together with the upper LCO layer in oxygen, or by direct atomic layer deposition of the oxide. Compared to the LCO/LLZO/Li cells without an aluminum oxide interlayer, those with the interlayer exhibited improved performance, i.e., a stable discharge capacity of up to 90 mAh/(g LCO) at a C/10 rate, a rate capability up to 1.68C and a stable galvanostatic cycling at 0.1C for over 100 cycles with a discharge capacity fade rate of 0.15% per cycle. It was determined that aluminum diffused into the LCO layer after preventing the initial detrimental reaction between LCO and the LLZO garnet from happening during high temperature annealing, suggesting that the coating does not have to remain a physically blocking layer during cycling to function.

Electrochemistry↗
DOE OSTI2023

Transition from Vogel-Fulcher-Tammann to Arrhenius Ion-Conducting Behavior in Poly(Ethyl Acrylate)-Based Solid Polymer Electrolytes via Succinonitrile Plasticizer Addition

Solid polymer electrolytes (SPEs) offer potential advantages over liquid electrolytes, including flexibility, safety, and processability. However, they suffer from low room-temperature ionic conductivity. Recently, it has been reported a poly(ethyl acrylate) based (polyEA) SPE, by incorporating 50 wt% of succinonitrile (SN) solid plasticizer, 30 wt% of lithium salt and 5 wt% of fluoroethylene carbonate additive, which achieves a high room-temperature ionic conductivity of 1.01 × 10 −3 S cm −1 (Nat. Nanotechnol, 2022, 17, 768-776). This novel SPE exhibits stability against Li 0 and anodic stability up to 4.9 V vs Li + /Li. However, the specific mechanism responsible for its high ionic conductivity remains elusive. In this work, by adjusting the weight ratio of SN in the SPE, a transition from Vogel-Fulcher-Tammann to Arrhenius ion-conducting behavior is observed. It is demonstrated that the addition of SN leads to the gradual decoupling of Li-ion from the polymer backbone and its coordination with SN, as revealed by 6 Li solid-state nuclear magnetic resonance spectroscopy. As a result, Li-ion migration primarily occurs through SN rather than the segmental motion of the polymer backbone. Performances of the SPE in Cu||Li, Li||Li and a LiFePO 4 ||Li pouch cells are shown to demonstrate the commercial viability of this SPE in Li 0 -anode solid-state batteries.

Electrochemistry↗
DOE OSTI2025

Improved interfacial li-ion transport in composite polymer electrolytes via surface modification of LLZO

Composite polymer electrolytes that incorporate ceramic fillers in a polymer matrix offer mechanical strength and flexibility as solid electrolytes for lithium metal batteries. However, fast Li + transport between polymer and Li + -conductive filler phases is not a simple achievement due to high barriers for Li + exchange across the interphase. This study demonstrates how modification of Li 7 La 3 Zr 2 O 12 (LLZO) nanofiller surfaces with silane chemistries influences Li + transport at local and global electrolyte scales. Anhydrous reactions covalently link amine-functionalized silanes [(3-aminopropyl)triethoxysilane (APTES)] to LLZO nanoparticles, which protects LLZO in air. APTES functionalization lowers the poly (ethylene oxide) (PEO)-LLZO interphase resistance to half that of unmodified LLZO and increases effective Li + transference number, while insulating Al 2 O 3 completely blocks ion exchange and lowers transference number and conductivity in PEO-lithium bis(trifluoromethanesulfonyl)imide (LiTFSI)-LLZO composites. Modeling an inner resistive interphase between LLZO and PEO surrounded by an outer conductive interphase explains non-linear conductivity trends. Solid-state 7 Li & 6 Li nuclear magnetic resonance shows Li + only exchanges between PEO-LiTFSI and some LLZO interphase, with no appreciable Li + transport through bulk LLZO. Surface functionalization is a promising path toward lowering the polymer-ceramic interphase resistance. This work demonstrates that local changes in Li + transport affect macroscopic performance, highlighting the intricate relationships between all interfaces in inherently heterogeneous composite polymer electrolytes.

25 ENERGY STORAGE↗
DOE OSTI2022

Composite Solid Ion Conductor with Engineered Lithium Interface

Successful widespread commercialization of electric vehicles is contingent upon development of safe high energy density batteries capable of long cycle life. Lithium metal affords the highest theoretical capacity (3,860 mAh/g) and lowest electrochemical potential (-3.04V vs SHE), which offers the highest specific energy density of anode materials today. While Li-ion batteries are capable of delivering energy densities of 400-600 Wh/kg, the development of lithium metal batteries such as Li-S and Li-air may boost this number up to 650 and 950 Wh/kg, respectively. However, significant progress towards the passivation of lithium metal must occur before the energy density benefit can be realized. Issues with lithium dendrite formation, anode volume expansion, and continuous solid electrolyte interphase (SEI) build-up often result in significant safety concerns, high cell resistance, and poor cycle life. The intrinsic high reactivity between lithium metal with conventional lithium ion electrolytes (organic carbonate-based solvents) makes it extremely difficult to overcome these problems. In this project, Wildcat performed focused, fundamental research and development on composite polymer/ceramic electrolytes and protected lithium metal anodes to develop an all solid state lithium metal battery targeting the DOE requirements to enable commercialization. Wildcat successfully leveraged its high throughput battery platform and explored a broad composite electrolyte compositional space. Additionally, Wildcat screened large numbers of inorganic and organic coatings for lithium metal protection using in situ liquid methods and translated the best results to all solid cells. The proposed composite polymer/ceramic electrolyte and a protected lithium metal anode enabled a solid-state lithium metal battery. The ultimate targets from this effort will deliver a safe all solid-state lithium metal pouch cell with over 350 Wh/kg and over 1,000 cycles (C/3) with the cost estimate below $100/kWh. During the course of the project, we demonstrated stable cycling of 10’s of cycles with the composite SSE and our protected lithium. The work has continued after the conclusion of the DOE funding, demonstrating further improvements in catholyte composition and thinner composite solid electrolytes.

25 ENERGY STORAGE↗
DOE OSTI2023

Multiscale simulations of novel lithium electrolytes for improved processability and performance of solid-state batteries

In FY23, we performed atomic-scale simulations and developed analysis tools to understand the dynamic behaviors of Li ions and cation/anion components and predict mechanical properties in a new class of Li-ion solid electrolytes (SEs) developed by TRINA. Specifically, we revealed the transport characteristics of Li ions in anion sublattice, rotational dynamics of anion and cation components, and the effect of cation and anion on Li transport as well as interactions between cation and anions. The results imply that the transport characteristics in these soft materials may be affected by processing conditions significantly, which will offer the necessary insights to optimize these future SEs and accelerate the deployment of practical and easily processable Solid-state batteries (SSBs).

25 ENERGY STORAGE↗
DOE OSTI2026

Energy Storage Technologies and U.S. Department of War Requirements

This report offers an overarching primer the energy storage market and assessment of each technology’s suitability for U.S. Department of War applications. We find that battery energy storage is the most promising technology for energy storage applications in terms of energy density and cost. While commercial and advanced Li-ion can satisfy certain electric mobility (e.g., cars, midsized vehicles) and electric flight application (e.g. drones) needs of DoW, next generation technologies like Li metal solid-state batteries, and conversion chemistries (Li-air, Li-CFx) are needed for heavy duty applications like armored vehicles, tanks, airplanes, jets. Batteries should also be supplemented with technologies such as supercapacitors and next-generation flywheels to provide short bursts of high energy for high power needs. Thermal energy storage solutions (sensible and latent heat) and fuel cells (PEMFC and SOFC) are good alternatives for supporting energy storage applications in terms of energy density and cost. Gravity-based energy storage promises the least energy density with high cost and should only be used for niche DoW applications. Of the different gravity energy storage technologies reviewed, pumped-hydro, flywheel, and compressed air energy storage are promising, while solid gravity energy storage appears the least attractive. Military facilities requiring 14 days of energy storage can benefit from flow batteries, especially commercial VFRBs and next generation iron-air systems. While VFRBs are expensive, iron-air batteries have the coupled benefits of low cost and good energy density promising 100+ hours of storage.

25 ENERGY STORAGE↗
NASA NTRSDate not supplied

Molten Salt Electrolytes: Computational Analysis of Transport, Electrochemical Properties and Designing New Mixtures

Electric aircraft propulsion has gained a traction over the last decade due to possible high-energy density electrochemistries with reasonable cycle life and identification of non-flammable chemistries that can guarantee much better safety. Commercial Li-ion batteries cannot reach such high capacities because of weight limitations that arise from the widely used intercalation electrodes. Also, use of organic electrolytes make them highly susceptible to fire on exposure to air and humidity. Currently, use of Lithium metal anode along with conversion electrochemistries such as Li-O2 and Li-S are being pursued to achieve such high energy densities. Safer inorganic solid-state electrolytes, recently discovered Water-in-Salt electrolytes, molten salt electrolytes are some of the alternatives being pursued for a safer/non-flammable battery. Of these safer alternatives, molten-salt electrolytes offer some attractive properties: liquid at operating temperatures resulting in better electrode-wetting, stable interface with Li-metal anodes and good ionic conductivity; their only drawback being high operating temperatures. In this talk, we will discuss some of the computational studies, driven via atomistic simulations, of the properties of molten-salt electrolytes including transport mechanism and interface stability. We will also discuss predicting electrochemical properties of these high-temperature electrolytes. Further, we will discuss a thermodynamic approach to predicting new molten-salt eutectics with lower melting points.

Molten Salt Electrolytes↗
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↗