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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A Segregated Approach for Modeling the Electrochemistry in the 3-D Microstructure of Li-Ion Batteries and Its Acceleration Using Block Preconditioners

Abstract Battery performance is strongly correlated with electrode microstructure. Electrode materials for lithium-ion batteries have complex microstructure geometries that require millions of degrees of freedom to solve the electrochemical system at the microstructure scale. A fast-iterative solver with an appropriate preconditioner is then required to simulate large representative volume in a reasonable time. In this work, a finite element electrochemical model is developed to resolve the concentration and potential within the electrode active materials and the electrolyte domains at the microstructure scale, with an emphasis on numerical stability and scaling performances. The block Gauss-Seidel (BGS) numerical method is implemented because the system of equations within the electrodes is coupled only through the nonlinear Butler–Volmer equation, which governs the electrochemical reaction at the interface between the domains. The best solution strategy found in this work consists of splitting the system into two blocks—one for the concentration and one for the potential field—and then performing block generalized minimal residual preconditioned with algebraic multigrid, using the FEniCS and the Portable, Extensible Toolkit for Scientific Computation libraries. Significant improvements in terms of time to solution (six times faster) and memory usage (halving) are achieved compared with the MUltifrontal Massively Parallel sparse direct Solver. Additionally, BGS experiences decent strong parallel scaling within the electrode domains. Last, the system of equations is modified to specifically address numerical instability induced by electrolyte depletion, which is particularly valuable for simulating fast-charge scenarios relevant for automotive application.

25 ENERGY STORAGE↗
DOE OSTI2020

Self-supported MoO 2 /MoS 2 nano-sheets embedded in a carbon cloth as a binder-free substrate for high-energy lithium–sulfur batteries

As one of the most prospective candidates for next-generation rechargeable batteries, lithium-sulfur (Li-S) batteries currently still encounter great challenges associated with the low conductivity, severe shuttle effects, and sluggish redox kinetics. Herein, a self-supported sulfur host is fabricated by an in-situ growth of MoO 2 /MoS 2 nano-sheets on a carbon cloth (CC). First, carbon cloth serves as a good template for the morphology-controlled synthesis of nanostructured materials, which not only alleviates the agglomeration of MoO2/MoS2, but also enhances the flexibility and mechanical strengthen of the hybrid architecture as a free-standing host. Second, the polysulfide-trapping ability can be greatly enhanced by both physical and chemical adsorption from the MoO 2 /MoS 2 -anchored carbon cloth. Moreover, the partially sulfurized MoO 2 /MoS 2 nano-sheets integrate the benefits of conductive MoO 2 and sulfiphilic MoS 2 , thus facilitating fast charge transfer and redox kinetics of polysulfide conversion. As a result, these attributes enable the host to hold a high sulfur loading (up to 7.6 mg cm -2 ), which exceeds most of the reported carbon cloth-related cathode work in the literature. Furthermore, the Li-S cells can achieve a high peak capacity of 1350 mA h g -1 , excellent rate capability (C/20 - 2C rate), impressive areal capacity (up to 6.3 mA h cm -2 ), and a high capacity retention of 85% after 100 cycles.

25 ENERGY STORAGE↗
DOE OSTI2021

Rapid failure mode classification and quantification in batteries: A deep learning modeling framework

Unique, rapid identification and quantification of the dominant aging modes in lithium-ion batteries (LiBs) with early and non-specialized test data is a significant scientific challenge. Leveraging synthetic-data, deep-learning (DL) techniques have great potential to enable fast and robust classification and quantification of battery aging modes that produce different patterns of cell aging. This study, for the first time, presents a synthetic–data-based DL modeling framework for rapid and automatic classification and quantification of battery-aging modes and resultant aging with experimental validation. Availing synthetic dQ.dV -1 curves for ~26000 initial conditions and aging modes, the framework classified the dominant aging modes, for cells undergoing fast charge, in fewer than 100 cycles. Upon classification, the framework quantified the evolution of the aging modes, which were often nonuniform with cycling, for 22 gr/NMC532 pouch cells tested up to 600 cycles at different charging rates (1C–9C).

25 ENERGY STORAGE↗
DOE OSTI2021

Interfaces in all solid state Li-metal batteries: A review on instabilities, stabilization strategies, and scalability

With technological advancements in electrochemical energy storage systems increasing at a spectacular rate, batteries equipped with a lithium anode hold the key towards unlocking high energy densities. While lithium-ion batteries with layered anodes (e.g. graphite) and liquid organic electrolytes have been ubiquitous in portable electronics, electric vehicles, and grid applications, all solid-state batteries that use the combination of a lithium anode and a solid-state electrolyte (SSE) will further advance the present technology. The underlying challenge that limits the successful development of all solid-state batteries (ASSBs) is dictated largely by the highly reactive interfaces at the anode/SSE and the cathode/SSE interface. In this comprehensive review, we present an overview of the following: (i) characterization of the electrode/SSE interface via multimodal characterization, which include X-ray-, electron-, neutron-, optical-, and computation-based methods: (ii) parasitic reactions that occur from chemical, mechanical or electrochemical instabilities and interfacial engineering strategies for improving the stability of these interfaces classified by the class of inorganic SSEs (sulfide-, NASICON-, and garnet-type SSEs); (iii) laboratory-to-industry scale processing perspectives of SSEs; (iv) scalability and manufacturing aspects of current interfacial strategies; and (v) the prospects of all ASSBs within the context of extreme fast charging capability. Here, this review seeks to highlight key efforts in the field of ASSBs, by focusing particularly on stabilizing the electrode/SSE interfaces, which will help to bridge fundamental studies to technological relevance.

25 ENERGY STORAGE↗
DOE OSTI2021

Design and processing for high performance Li ion battery electrodes with double-layer structure

A two-layer LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811) cathode has been designed and fabricated containing a “power layer” and “energy layer”, with corresponding porosity and particle size prescribed to each layer to achieve best utilization of electrode material (maximum integrated depth of discharge across the electrode thickness) at high applied current. The cathode showed a 17% improvement in capacity when tested in symmetric cells. When applying the design to a full cell, where both positive and negative electrodes contain power and energy layers, a 74% increase in discharge capacity at 2C was achieved compared to the cell with conventional electrodes. Furthermore, this demonstrates an avenue to increase energy and power density of lithium–ion batteries and enable fast charging capability.

25 ENERGY STORAGE↗
DOE OSTI2026

Ionic Interdiffusion at Cathode|Solid-Electrolyte Interface: A Machine Learning–Assisted Multiscale Investigation and Mitigation Strategies

Future lithium batteries are expected to use solid electrolytes to achieve higher energy density and fast charge capabilities. However, most solid electrolytes are thermodynamically unstable against layered oxide cathodes. In this study, the stability of LiCoO2 (LCO) cathode with Li10GeP2S12 (LGPS) solid electrolyte is investigated using ab initio molecular dynamics (AIMD) and machine learning molecular dynamics (MLMD). The propensity of ionic interdiffusion, formation of a passivating interphase layer, and corresponding decay in cell performance is addressed using a continuum model. Large-scale MLMD simulations confirm that the LCO|LGPS interface permits interdiffusion of cobalt (Co) and other ionic species, leading to the formation and growth of a resistive interphase and to dramatic capacity fade even in the first cycle. We examine the literature evidence that incorporating a thin layer of LiNb0.5Ta0.5O3 (LNTO) between LCO and LGPS prevents the interdiffusion of ions. Atomistic simulations suggest that substituting lithium (Li) in LNTO with Co is thermodynamically unfavorable, thereby inhibiting ionic interdiffusion. The stable Nb5+/Ta5+ states form a rigid metal-oxide framework, which consequently also prevents the substitution of niobium (Nb) or tantalum (Ta). However, continuum-level analysis suggests that the higher mechanical stiffness of LNTO can lead to interfacial delamination between the LCO and LNTO. This phenomenon reduces the effectiveness of the protective layer. This paper, therefore, highlights the need to develop novel interlayers that balance low ionic interdiffusion with low mechanical stiffness.

Ncube, Musawenkosi K.↗
DOE OSTI2026

Neural Posterior Estimation for Scalable and Accurate Inverse Parameter Inference in Li-Ion Batteries

Diagnosing the internal state of Li-ion batteries is critical for battery research, operation of real-world systems, and prognostic evaluation of remaining lifetime. By using physics-based models to perform probabilistic parameter estimation via Bayesian calibration, diagnostics can account for the uncertainty due to model fitness, data noise, and the observability of any given parameter. However, Bayesian calibration in Li-ion batteries using electrochemical data is computationally intensive even when using a fast surrogate in place of physics-based models, requiring many thousands of model evaluations. A fully amortized alternative is neural posterior estimation (NPE). NPE shifts the computational burden from the parameter estimation step to data generation and model training, reducing the parameter estimation time from minutes to milliseconds, enabling real-time applications. The present work shows that NPE can infer parameters equally or more accurately than Bayesian calibration, even if it leads to higher voltage reconstruction errors. We also demonstrate that the higher computational costs for data generation are tractable even in high-dimensional cases (ranging from 6 to 27 estimated parameters). The NPE method also offers several interpretability advantages over Bayesian calibration, such as local parameter sensitivity to specific regions of the voltage curve. The NPE method is demonstrated using an experimental fast charge dataset, with parameter estimates validated against measurements of loss of lithium inventory and loss of active material. The implementation is made available in a companion repository (https://github.com/NatLabRockies/BatFIT).

25 ENERGY STORAGE↗
DOE OSTI2020

Advanced diagnostics to evaluate heterogeneity in lithium-ion battery modules

Battery packs for electric and stationary applications experience varied operating conditions, including abuse—e.g., fast charging, overcharging, thermal, vibration, shock, etc.—throughout their lifetimes. Innovative diagnostic tools and algorithms that go beyond single cells and deal with modules and packs are essential for early detection of off-normal issues. High-resolution tools with known detection limits are key to developing appropriate mitigation strategies. With the advent of rapid impedance spectroscopy that can generate a broadband impedance spectrum in ~10 sec, the case for impedance-based diagnostics that can be readily aligned with other methods, such as incremental capacity or dQ.dV -1 , has become promising. This study used the aforementioned diagnostic methods to identify realistic in-vehicle battery abnormalities (e.g., localized self-discharge and non-uniform aging), in series (up to 10S) and parallel (4P) strings, using 16 Ah graphite/NMC cells. The impedance-based diagnostic is found to be sensitive to the string size and state. Depending on the type of abnormality, detection frequency varied. The dQ.dV -1 method showed the potential to detect long-term aging-related heterogeneity in modules. In general, both the impedance and dQ.dV -1 methods were able to detect series strings’ abnormalities, but struggled to find those issues within parallel modules.

25 ENERGY STORAGE↗
DOE OSTI2021

Bayesian learning for rapid prediction of lithium-ion battery-cycling protocols

Advancing lithium-ion battery technology requires the optimization of cycling protocols. A new data-driven methodology is demonstrated for rapid, accurate prediction of the cycle life obtained by new cycling protocols using a single test lasting only 3 cycles, enabling rapid exploration of cycling protocol design spaces with orders of magnitude reduction in testing time. We achieve this by combining lifetime early prediction with a hierarchical Bayesian model (HBM) to rapidly predict performance distributions without the need for extensive repetitive testing. The methodology is applied to a comprehensive dataset of lithium-iron-phosphate/graphite comprising 29 different fast-charging protocols. HBM alone provides high protocol-lifetime prediction performance, with 6.5% of overall test average percent error, after cycling only one battery to failure. Here, by combining HBM with a battery lifetime prediction model, we achieve a test error of 8.8% using a single 3-cycle test. In addition, the generalizability of the HBM approach is demonstrated for lithium-manganese-cobalt-oxide/graphite cells.

25 ENERGY STORAGE↗
DOE OSTI2020

Performance degradation due to anodic failure mechanisms in lithium-ion batteries

Here, we report a mechano-chemical model for anodic degradation during fast-charging of nickel-manganese-cobalt (NMC)/graphite (C) cell due to SEI growth, lithium plating/stripping, dead lithium storage, and film fracture of composite SEI and plated lithium film. Degradation of the battery is analyzed for a range of charging rates from 1 to 6 C-rates, and the influence of plating mechanisms – lithium plating and dead lithium deposition and recovery during stripping – on the film resistance of the anode are accounted for in the model. Dynamic evolution of the interfacial properties is modeled using rule-of-mixture approach. Model predictions of plating associated stress fields are used to compute critical energy release rate for film cracking. The results indicate an increased tendency of fracture for thinner SEI film with lithium plating at higher charging rates. The process of reforming the cracked film absorbs a significant portion of the electrode current thereby reducing the cell capacity and plating efficiency. The mechano-chemical model provides an extensive analytical framework for understanding the synergistic coupling of anodic degradation mechanisms, prognosticating conditions of SEI failure, and evaluating the capacity fade and efficiency of lithium-ion battery.

25 ENERGY STORAGE↗
DOE OSTI2021

Thermodynamics and kinetics of 2D g-GeC monolayer as an anode materials for Li/Na-ion batteries

Development of high capacity anode materials is one of the essential strategies for next-generation high-performance Li/Na-ion batteries. Rational design, using density functional theory, can expedite the discovery of these anode materials. Here, we propose a new anode material, germanium carbide, g-GeC, for Li/Na-ion batteries. Our results show that g-GeC possesses both benefits of the high stability of graphene and the strong interaction between Li/Na and germanene. The single-layer germanium carbide, g-GeC, can be lithiated/sodiated on both sides yielding Li 2 GeC and Na 2 GeC with a storage capacity as high as 633 mA h/g. Besides germagraphene’s 2D honeycomb structure, fast charge transfer, and high (Li/Na)-ion diffusion and negligible volume change further enhance the anode performance. These findings provide valuable insights into the electronic characteristics of newly predicted 2D g-GeC nanomaterial as a promising anode for (Li/Na)-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2020

Ultrahigh coulombic efficiency electrolyte enables Li||SPAN batteries with superior cycling performance

Raising the coulombic efficiency of lithium metal anode cycling is the deciding step in realizing long-life rechargeable lithium batteries. Here, we designed a highly concentrated salt/ether electrolyte diluted in a fluorinated ether: 1.8 M LiFSI in DEE/BTFE (diethyl ether/bis(2,2,2-trifluoroethyl)ether), which realized an average coulombic efficiency of 99.37% at 0.5 mA cm -2 and 1 mAh cm -2 for more than 900 cycles. This electrolyte also maintained a record coulombic efficiency of 98.7% at 10 mA cm -2 , indicative of its ability to provide fast-charging with high cathode loadings. Morphological studies reveal dense, dendrite free Li depositions after prolonged cycling, while surface analyses confirmed the formation of a robust LiF-rich SEI layer on the cycled Li surface. Moreover, we discovered that this ether-based electrolyte is highly compatible with the low-cost, high-capacity SPAN (Sulfurized polyacrylonitrile) cathode, where the constructed Li||SPAN cell exhibited reversible cathode capacity of 579 mAh g -1 and no capacity decay after 1200 cycles. A cell where a high areal loading SPAN electrode (>3.5 mAh cm -2 ) is paired with only onefold excess Li was constructed and cycled at 1.75 mA cm -2 , maintaining a coulombic efficiency of 99.30% for the lithium metal. Computational simulations revealed that at saturation, the Li-FSI complex forms contact ion pairs, with a first solvation shell comprising DEE molecules, and a second solvation shell with a mix of DEE/BTFE. This study provides a path to enable high energy density Li||SPAN batteries with stable cycling.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Catalytic materials for lithium-sulfur batteries: mechanisms, design strategies and future perspective

Lithium-sulfur batteries (LSBs) are attractive candidates for post-lithium-ion battery technologies because of their ultrahigh theoretical energy density and low cost of active cathode materials. However, the commercialization of LSBs remains extremely challenging primarily due to poor cycling performance and safety concerns, which are inherently caused by low conductivity of S 8 and Li 2 S, severe polysulfide shuttling, and high polarization by solid Li 2 S 2 /Li 2 S deposition. Catalytic materials could facilitate the large-scale practical application of LSBs by overcoming all these challenges. In this review, we investigate the sulfur species evolution in LSBs and explore the roles of catalytic materials in charge/discharge processes, highlighting the catalysis of solid S 8 to liquid polysulfides and solid Li 2 S 2 to Li 2 S. Furthermore, we offer systematic strategies from atomic to macro levels, including defect engineering, morphology engineering and catalyst compositing, to enhance catalysis efficiency in terms of sulfur supercooling, fast charge transfer, thiosulfate generation, disulfide bond cleavage, tuneable Li 2 S growth and Li 2 S decomposition enhancement. Finally, the design and availability of the proposed catalytic materials will further advance LSB technology from coin cells and pouch cells to the subsequent commercialization scale.

25 ENERGY STORAGE↗
DOE OSTI2020

A practical phosphorus-based anode material for high-energy lithium-ion batteries

State-of-the-art lithium-ion batteries cannot satisfy the increasing energy demand worldwide because of the low specific capacity of the graphite anode. Silicon and phosphorus both show much higher specific capacity; however, their practical use is significantly hindered by their large volume changes during charge/discharge. Although significant efforts have been made to improve their cycle life, the initial coulombic efficiencies of the reported Si-based and P-based anodes are still unsatisfactory (<90%). In this study, by using a scalable high-energy ball milling approach, we report a practical hierarchical micro/nanostructured P-based anode material for high-energy lithium-ion batteries, which possesses a high initial coulombic efficiency of 91% and high specific capacity of ~2500 mAh g –1 together with long cycle life and fast charging capability. In situ high-energy X-ray diffraction and in situ single-particle charging/discharging were used to understand its superior lithium storage performance. Moreover, proof-of-concept full-cell lithium-ion batteries using such an anode and a LiNi 0.6 Co 0.2 Mn 0.2 O 2 cathode were assembled to show their practical use. The findings presented here can serve as a good guideline for the future design of high-performance anode materials for lithium-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2022

Synergy of carbonyl and azo chemistries for wide-temperature-range rechargeable aluminum organic batteries

Rechargeable aluminum organic batteries (RAOBs) are promising for developing cost-effective and sustainable energy storage devices due to the low cost, abundance, and high sustainability of aluminum and organic resources. In this study, we designed and synthesized a redox-active polymer bearing carbonyl and azo groups as a cathode material for RAOBs. The polymeric cathode exhibits a high reversible specific capacity, superior cyclic stability, fast charging capability, and a wide operation temperature range (₋40°C to 100°C). X-ray photoelectron spectroscopy (XPS), pair distribution function (PDF) analysis, and soft X-ray absorption near edge structure (XANES) were employed to gain fundamental insight into the carbonyl and azo chemistries in RAOBs, as well as the cathode electrolyte interphase (CEI) structure. We demonstrated a step-by-step alumination/de-alumination reaction for carbonyl and azo groups in the polymer cathode and unraveled a Al 2 O 3⁻ and AlN-rich CEI, which is critical for the impressive performance of RAOBs.

25 ENERGY STORAGE↗
DOE OSTI2023

Developing a model for the impact of non-conformal lithium contact on electro-chemo-mechanics and dendrite growth

Lithium dendrite growth hinders the use of lithium metal anodes in commercial batteries. We present a 3D model to study the mechanical and electrochemical mechanisms that drive microscale plating. With this model, we investigate electrochemical response across a lithium protrusion characteristic of rough anode surfaces, representing the separator as a porous polymer in non-conformal contact with a lithium anode. The impact of pressure on separator morphology and electrochemical response is of particular interest, as external pressure can improve cell performance. We explore the relationships between plating propensity, stack pressure, and material properties. External pressure suppresses lithium plating due to interfacial stress and separator pore closure, leading to inhomogeneous plating rates. For moderate pressures, dendrite growth is completely suppressed, as plating will occur in the electrolyte-filled gaps between anode and separator. In fast-charging conditions and systems with low electrolyte diffusivities, the benefits of pressure are overridden by ion transport limitations.

25 ENERGY STORAGE↗
DOE OSTI2021

Bipolar Membranes for Ion Management in (Photo)Electrochemical Energy Conversion

Conspectus (Photo)electrochemical energy conversion is important in the development of a carbon-neutral energy economy because it can provide a pathway for mitigating the intermittency of renewable energy sources such as wind and solar. In order to operate efficiently, these technologies, which include photoelectrochemical cells, water and CO 2 electrolyzers, fuel cells, and redox flow batteries, require fast charge transfer kinetics at the electrode/electrolyte interface as well as robust ion management in the electrolyte. In conventional electrolyzers and fuel cells, the electrolyte is strongly acidic or basic and ionic current is carried by H + or OH – ions. In contrast, photoelectrodes and electrocatalysts for water splitting are often studied in buffered solutions. The question of ion balance in these systems led us to analyze the polarization losses due to ion concentration gradients in cells that employed various buffer–membrane combinations. Continuously driving the buffer ions across an ionomer membrane not only lowers the buffer capacity of an aqueous electrolyte but also introduces pH gradients that result in significant energy losses. To address the problem, we and other groups have studied the use of reverse-biased bipolar membranes (BPMs) in (photo)electrolytic cells. BPMs consist of an anion exchange layer (AEL) laminated with a cation exchange layer (CEL) and are usually equipped with a catalytic layer in between to accelerate the water dissociation reaction. At the AEL/CEL interface, water dissociates into protons and hydroxide ions, which replenish those consumed at the cathode and anode. Compared to conventional water electrolyzers with proton/anion exchange membranes (PEM/AEM), BPM electrolyzers provide the unique advantage of continuously operating the cathode and anode under different pH conditions, which is desirable when the two electrode reactions have different pH requirements. BPMs also enable the use of buffered electrolytes at pH values that are optimized for electrode stability and product selectivity in applications such as CO 2 electrolysis. Product crossover losses and CO 2 pumping can be dramatically reduced in BPM-based CO 2 electrolyzers, relative to conventional alkaline membranes, by electrostatic repulsion (of anionic products) and electroosmotic drag (for neutral products). BPM-based gas fed CO 2 electrolyzers can achieve high current density, but they suffer from low Faradaic efficiency (FE) due to the acidic local environment of the CEL. This problem can be mitigated by adding an aqueous buffering layer or by creating a weak acid cation exchange film on the CEL face of the membrane. The use of BPMs in fuel cells and redox flow batteries offers some interesting advantages. Configurations with both reverse and forward bias have been studied, but forward bias has been favored due to material compatibility, reaction kinetics, and thermodynamic considerations. The net reaction at the AEL/CEL interface is the acid–base neutralization reaction, which has a high inherent reaction rate constant, but in the BPM is limited to a nanometric space-charge layer and requires efficient catalysis to achieve high current density. Understanding the mechanism of the acid–base neutralization and the opposite process, the water dissociation reaction, will be essential for improving the performance of forward-biased BPMs. In conclusion, this Account reviews our current understanding of the working mechanisms of BPMs and discusses how we can use them to effectively manage ions for various (photo)electrochemical applications.

Yan, Zhifei↗