Solid-electrolyte fracture models driven by lithium metal plating require electrochemical mechanical couplings
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25 ENERGY STORAGE↗
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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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PemNet: A Transfer Learning-Based Modeling Approach of High-Temperature Polymer Electrolyte Membrane Electrochemical Systems
Widespread adoption of high-temperature electrochemical systems such as polymer electrolyte membrane fuel cells (HT-PEMFCs) requires models and computational tools for accurate optimization and guiding new materials for enhancing fuel cell performance and durability. Furthermore, while robust and better suited for extrapolation, knowledge-based modeling has limitations as it is time-consuming and requires information about the system that is not always available (e.g., material properties and interfacial behavior between different materials). Data-driven modeling, on the other hand, is easier to implement but often necessitates large datasets that could be difficult to obtain. In this contribution, knowledge-based modeling and data-driven modeling are combined by implementing a few-shot learning (FSL) approach. A knowledge-based model originally developed for a HT-PEMFCs was used to generate simulated data (887,735 points) and used to pretrain a neural network source model tuned via a genetic algorithm-based AutoML. Then, experimental datasets from HT-PEMFCs with different materials and operating conditions (~50 points each) were used to train six target models via FSL. Models for the unseen data reached high accuracies in all cases (rRMSE < 10%).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Extended Finite Element Based Approach in Additive Manufacturing Modeling for Optimizing Highly Complex Manifold in Protonic Ceramic Electrochemical Cells
The objective of this project is to develop a novel numerical approach based on the extended finite element method (XFEM) for modeling moving boundaries of material deposited in additive manufacturing (AM) process with improved accuracy and reduced computational cost. One of the major challenges in simulating AM processes is that the boundaries of the computational domain need to change as material is deposited. Previous approaches have achieved this by activating new finite elements, but this requires a high level of mesh refinement to capture small movement of the boundary. XFEM allows the solution boundary to move independently of the mesh, permitting smooth representation of the evolution of the boundary as material is deposited. As a result, this new method will significantly improve the simulation accuracy while reducing the computational cost compared with existing approaches. Furthermore, this project will bring significant improvements for the design and AM process optimization in an iterative fashion among numerical simuation, parameter optimization, and experimental validation. Specifically, this project will support the protonic ceramic electrochemical cells (PCEC) stack development at INL by providing insights into the density, residual stress, thermal and mechanical properties of the PCEC manifold and interconnect, which is critical in enhancing the system lifetime and reducing the overall cost. Upon the success of proposed development and validation of the proposed approach, simulation will be applied to determine an optimal set of AM process parameters for the PCEC manifold production. This project will consolidate Idaho National Laboratory (INL)’s simulation capabilities provided by the MOOSE framework and the Valhalla AM simulation application, with encouraging expansion to emerging PCEC applications and AM technologies at INL and beyond.
97 MATHEMATICS AND COMPUTING↗
Machine learning-based ethylene concentration estimation, real-time optimization and feedback control of an experimental electrochemical reactor
With the increase in electricity supply from clean energy sources, electrochemical reduction of carbon dioxide (CO 2 ) has received increasing attention as an alternative source of carbon-based fuels. As CO 2 reduction is becoming a stronger alternative for the clean production of chemicals, the need to model, optimize and control the electrochemical reduction of the CO 2 process becomes inevitable. However, on one hand, a first-principles model to represent the electrochemical CO 2 reduction has not been fully developed yet because of the complexity of its reaction mechanism, which makes it challenging to define a precise state-space model for the control system. On the other hand, the unavailability of efficient concentration measurement sensors continues to challenge our ability to develop feedback control systems. Gas chromatography (GC) is the most common equipment for monitoring the gas product composition, but it requires a period of time to analyze the sample, which means that GC can provide only delayed measurements during the operation. Moreover, the electrochemical CO 2 reduction process is catalyzed by a fast-deactivating copper catalyst and undergoes a selectivity shift from the product-of-interest at the later stages of experiments, which can pose a challenge for conventional control methods. To this end, machine learning (ML) techniques provide a potential approach to overcome those difficulties due to their demonstrated ability to capture the dynamic behavior of a chemical process from data. Motivated by the above considerations, we propose a machine learning-based modeling methodology that integrates support vector regression and first-principles modeling to capture the dynamic behavior of an experimental electrochemical reactor; this model, together with limited gas chromatography measurements, is employed to predict the evolution of gas-phase ethylene concentration. The model prediction is directly used in a proportional-integral (PI) controller that manipulates the applied potential to regulate the gas-phase ethylene concentration at energy-optimal set-point values computed by a real-time process optimizer (RTO). Specifically, the RTO calculates the operation set-point by solving an optimization problem to maximize the economic benefit of the reactor. Finally, suitable compensation methods are introduced to further account for the experimental uncertainties and handle catalyst deactivation. The proposed modeling, optimization, and control approaches are the first demonstration of active control for a CO 2 electrolyzer and contribute to the automation and scale-up efforts for electrified manufacturing of fuels and chemicals starting from CO 2 .
42 ENGINEERING↗
Advanced battery modeling for interfacial phenomena and optimal charging
Lithium ion batteries are one of the most promising energy storage systems for portable devices, transportation, and renewable grids. To meet the increasing requirements of these applications, higher energy density and areal capacity, long cycle life, fast charging rate and enhanced safety for lithium ion battery (LIBs) are urgently needed. To solve these challenges, the relevant physics at different length scale need to be understood. However, experimental study is time consuming and limited in small scale’s study. Modeling techniques provide us powerful tools to get a deep understanding of the relevant physics and find optimal solutions. This work focuses on studying the mechanism in advanced battery engineering techniques and developing a new charging algorithm by model-based optimization. The research topics are divided into six topics and each topic is reported as a form of journal publication. Paper Ⅰ provides a new aspect of how ALD coating improves the lithium ion diffusion at electrode particles. Paper Ⅱ explains the mechanisms by which 3D electrodes enhance battery performance and reveals guidelines for optimized 3D electrode designs by a 3D electrochemical-mechanical battery model. Paper Ⅲ investigates the electrolyte concentration impact on SEI layer growth and Li plating, especially under high charge rates. Paper Ⅳ proposes an optimized charging protocol for fast charging for reducing the charging time with minimal degradation. Paper Ⅴ reports a comprehensive degradation model for degradation estimation and life predication of energy storage system (ESS). Paper Ⅵ is a study of temperature-dependent state of charge (SOC) estimation for battery pack.
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Analysis of bipolar membranes for electrochemical CO 2 capture from air and oceanwater
Carbon dioxide (CO 2 ) must be removed from the atmosphere to mitigate the negative effects of climate change. However, the most scalable methods for removing CO 2 from the air require heat from fossil-fuel combustion to produce pure CO 2 and continuously regenerate the sorbent. Bipolar-membrane electrodialysis (BPM-ED) is a promising technology that uses renewable electricity to dissociate water into acid and base to regenerate bicarbonate-based CO 2 capture solutions, such as those used in chemical loops of direct-air-capture (DAC) processes, and in direct-ocean capture (DOC) to promote atmospheric CO 2 drawdown via decarbonization of the shallow ocean. In this study, we develop an experimentally validated 1D model for the electrochemical regeneration of CO 2 from bicarbonate-based carbon capture solutions and seawater using BPM-ED. For DAC, our experimental and computational results demonstrate that pH swings induced by BPM water dissociation drive the formation of CO 2 at the cation-exchange layer|catholyte interface with energy-intensities of less than 150 kJ mol -1 . However, high rates of bubble formation increase energy intensity at current densities >100 mA cm -2 . Correspondingly, accelerating water dissociation catalysis and enacting bubble removal could enable CO 2 recovery at energy intensities <100 kJ mol -1 and current densities >100 mA cm -2 . For DOC, mass transport limitations associated with low carbon concentrations in oceanwater suggest that DOC is best suited for clean production of acid and base usable in downstream processes. Further, these results provide design principles for industrial-scale CO 2 recovery using BPM-ED.
54 ENVIRONMENTAL SCIENCES↗
Technoeconomic Analysis of the Electrochemically Mediated Amine Regeneration CO 2 Capture Process
The electrochemically mediated amine regeneration (EMAR) process presents an alternative route to the conventional thermal amine regeneration for carbon capture from a flue gas source. In this study, we conducted an economic analysis on the EMAR system for postcombustion CO2 capture from a 550 MWe power plant capturing 3.1 MtCO2 annually and from a mini steel mill with annual capture close to 170 ktCO2. We followed the recommendation of the National Energy Technology Laboratory (NETL) 2010 report to estimate the cost of CO2 avoided ([$/tCO2]). Detailed cost modeling of the electrochemical separation stage was conducted. This is integrated with the entire process flowsheet (e.g., including absorber, compressor, pumps, and other auxiliary equipment). Here, we identified the membrane cost as the dominant capital cost for the electrochemical separation train. At a membrane unit price of less than 10/m2, the CO2 capture cost can be reduced to below 50/tCO2 with optimized process conditions (e.g., desorption pressure and utilization of waste heat). Improvements in process design, cell construction, and solvent formulation may lead to additional reductions in the CO2 capture cost.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Transport and mechanical behavior in PEO-LLZO composite electrolytes
Abstract Composite solid electrolytes (CEs), wherein ion-conducting polymer and ceramic/glass is mixed, are promising candidates for all-solid-state batteries due to their promise of acceptable ionic conductivity and mechanical properties compared to their individual constituents. While numerous studies have focused on improving the performance of CEs, it is still unclear what the material targets are that can result in improved macroscopic performance especially in light of the coupled needs for high transport and high mechanical strength in these materials. In this study, a two-dimensional (2D) mathematical model is developed to investigate electrochemical and mechanical characteristics of CEs. The model is compared to CEs consisting of poly-ethylene-oxide (PEO) polymer and lithium lanthanum zirconium oxide (LLZO) ceramic material with examination of the impact of varying LLZO volume fractions. The potential drop at the PEO-LLZO interface is evaluated using the junction potential theory. Using experimental data from the literature, the model estimates the ionic conductivity, effective transference number, and mechanical stiffness of the CEs. While the mechanical stiffness improves with increasing volume fraction of LLZO, the impact on conductivity and transference number depends on interfacial resistance at the interface. Finally, the study reports CE’s potential to enhance Li-ion transport and mechanical properties to inhibit lithium (Li) dendrite growth.
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Early calendar life and health prediction of silicon batteries via machine learning with uncertainty quantification
Lithium-ion batteries with silicon anodes promise high energy density but are limited by calendar lifetime. Reducing the long iteration time to obtain experimental results requires predicting calendar lifetime early in a cell's life. In this study, we demonstrate that lightweight machine learning models with feature engineering can provide calendar lifetime estimates from early electrochemical signals. After 1 month of electrochemical aging, the best models achieve 10% error in calendar-life prediction and can separate "bad" from "good" lifetime cells with a mean F1 score of 0.857. As battery systems exhibit inherent variability, four methods for uncertainty quantification are compared, and confidence intervals are demonstrated with an uncertainty of +-3.6 months in lifetime prediction. A feature importance analysis indicates that early patterns in voltage decay are the strongest indicators of calendar lifetime. Finally, this modeling approach has high error when generalizing to new electrode chemistries or testing conditions but with appropriately low confidence.
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Controlling Ion Uptake in Carboxylated Mixed Conductors
Organic mixed ionic‐electronic conductors (OMIECs) have garnered significant attention due to their capacity to transport both ions and electrons, making them ideal for applications in energy storage, neuromorphics, and bioelectronics. However, charge compensation mechanisms during the polymer redox process remain poorly understood, and are often oversimplified as single‐ion injection with little attention to counterion effects. To advance understanding and design strategies toward next‐generation OMIEC systems, a series of p‐channel carboxylated mixed conductors is investigated. Varying side‐chain functionality, distinctive swelling character is uncovered during electrochemical doping/dedoping with model chao‐/kosmotropic electrolytes. Carboxylic acid functionalized polymers demonstrate strong deswelling and mass reduction during doping, indicating cation expulsion, while ethoxycarbonyl counterparts exhibit prominent mass increase, pointing to an anion‐driven doping mechanism. By employing operando grazing incidence X‐ray fluorescence (GIXRF), it is revealed that the carboxyl functionalized polymer engages in robust cation interaction, whereas ester functionalization shifts the mechanism towards no cation involvement. It is demonstrated that cations are pivotal in mitigating swelling by counterbalancing anions, enabling efficient anion uptake without compromising performance. These findings underscore the transformative influence of functionality‐driven factors and side‐chain chemistry in governing ion dynamics and conduction, providing new frameworks for designing OMIECs with enhanced performance and reduced swelling.
carboxylated polythiophenes↗
Enabling Extreme Fast-Charging: Challenges at the Cathode and Mitigation Strategies
We report charging lithium-ion batteries (LiBs) in 10 to 15 min via extreme fast-charging (XFC) is important for the widespread adoption of electric vehicles (EVs). Lately, the battery research community has focused on identifying XFC bottlenecks and determining novel design solutions. Like other LiB components, cathodes can present XFC bottlenecks, especially when considering long-term battery life. Therefore, it is necessary to develop a comprehensive understanding of how XFC conditions degrade LiB cathodes. The present article reviews relevant cathode-focused studies and summarizes the current understanding regarding cathode performance and aging issues under XFC conditions. Dominant aging modes and mechanisms are identified at different length-scales with electrochemical correlations for LiNi x Mn y Co z O 2 (NMC)-based cathodes. A range of electrochemical techniques and models provide key insights into cathode performance and life issues. A suite of multimodal and multiscale microscopy and X-ray techniques is surveyed to quantify chemical, structural, and crystallographic NMC-cathode degradation. Cathode cycle-life is scaled to equivalent EV miles to illustrate how cathode degradation translates to real-world scenarios and quantifies cathode-related bottlenecks that hinder XFC adoption. Finally, the article discusses several cathode cycle-life aging mitigation strategies with example case studies and identifies remaining challenges.
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Extended cycle life implications of fast charging for lithium-ion battery cathode
Enabling extreme fast charging (XFC, =10–15 min charging) requires a comprehensive understanding of its implications. While lithium plating is a key bottleneck for the anode, the full extent of limitations for the cathode are not well-understood, particularly in extended-cycle settings with well-defined battery designs and conditions. This article presents cycle-life implications of XFC on cathodes at multiple length scales, combining electrochemical analyses, degradation modeling, and post-test characterizations. The comprehensive test matrix includes 41 well-defined gr/NMC pouch cells under varied fast-charge rates (1–9C) and state-of-charges cycled up to 1000 times. Cathode issues remain minimal in early cycling, but begin to accelerate in later life, when distinct cracking is found and identified as a fatigue mechanism. The bulk structure of cathodes remains intact, but distinct particle surface reconstruction is observed; however, this shows less pronounced effect on cathode aging than does cracking.
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Ultrafast charging of energy-dense lithium-ion batteries for urban air mobility
Urban air mobility (UAM) demands batteries with high energy density, long cycle life, and fast rechargeability. In this paper, we demonstrate an energy-dense lithium-ion battery (LiB) with ultralong cycle life under ultrafast charging. By using the asymmetric temperature modulation (ATM) method, i.e., charging at an elevated temperature and discharging around the ambient temperature, it is experimentally shown that the 209 Wh/kg LiB is charged to 88% state of charge (SOC) in ~5 min under UAM cycling while retaining 97.7% capacity after 1,000 cycles. Moreover, an experimentally validated electrochemical-thermal (ECT) model is developed to elucidate the fast charging process and the degradation mode of UAM batteries, quantitatively capturing lithium plating during fast charging. We find that the LiBs for UAM applications are most prone to lithium plating due to their higher initial SOC required as the reserve for safety; nevertheless, the ATM method is effective in minimizing or preventing lithium plating in the high SOC range of 30-90%. In addition to slowing down capacity fade, the ATM method also raises the usable capacity by 10%, which boosts the battery energy density and ensures the battery to perform full UAM cycles even at the end of life.
25 ENERGY STORAGE↗
Fabrication of high-performance gas-diffusion-electrode based membrane-electrode assemblies
This paper demonstrates the fabrication and processing steps required to produce high performance fuel cell membrane electrode assemblies (MEAs) based on spray-coated gas-diffusion electrodes (GDEs). It is demonstrated that coating the catalyst layer with a thin layer of ionomer and then hot pressing the GDEs to the membrane is required to achieve comparable catalyst activity and air performance to catalyst-coated-membrane MEAs. We show that there is a critical amount of ionomer required to achieve maximized performance. Using electron microscopy, we show that the combination of the ionomer overlayer and hot-pressing bonds the catalyst layer to the membrane, increasing the interfacial contact area and quality of this interface. We also find that the ionomer overlayer smooths the surface of the GDE and provides increased contact area between the GDE and the membrane. Additionally, we demonstrate that much less ionomer is required for high-performance than has been previously reported. Through model fitting of electrochemical impedance spectroscopy, we show that this improvement in the catalyst layer - membrane interface reduces the effective catalyst layer resistance, which reduces Ohmic losses and increases catalyst utilization.
33 ADVANCED PROPULSION SYSTEMS↗
AuPd Nanoicosahedra: Atomic-Level Surface Modulation for Optimization of Electrocatalytic and Photocatalytic Energy Conversion
Modulation of bimetallic nanocatalysts with atomic precision would allow for significant increases in catalyst activity through the optimization of heteroatomic interplay. In practice, this level of control over homogeneously turning the heteroatomic states in the surface regions of catalysts remains a great challenge for practical applications requiring mass production. In this work, a one-step aqueous strategy for preparation of AuPd nanoicosahedra core–shell structures has been developed. By turning the molar ratio of Au and Pd precursors, the nanoicosahedra could be made with uniform core composition (Pd ~20%) and largely flexible surface compositions (Pd: 20–70%). Under model reactions of electrochemical ethanol oxidation (EOR) and 4-nitrophenol (4-NP) reduction, the Au 55 Pd 45 surface displayed superior activities. Here, x-ray absorption spectroscopy revealed that AuPd nanoicosahedra with Pd-poor surfaces (Au 79 Pd 21 and Au 60 Pd 40 ) maintain Au-like electronic structures while those with Pd-rich surfaces are more Pd-like. The Au 55 Pd 45 surface struck a delicate balance between the two extremes, leading to the optimal Au–Pd interplay for catalytic performance. Overall, the development of a one-step aqueous strategy for atomic-level control over bimetallic nanoparticle surface states is an important milestone for future advances in catalysis. Our findings in atomic-scale catalyst design will bring deep insights into the fields of nanosynthesis and heterogeneous catalysis, which will be beneficial for further scientific breakthroughs.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Enabling “lithium-free” manufacturing of pure lithium metal solid-state batteries through in situ plating
Abstract The coupling of solid-state electrolytes with a Li-metal anode and state-of-the-art (SOA) cathode materials is a promising path to develop inherently safe batteries with high energy density (>1000 Wh L −1 ). However, integrating metallic Li with solid-electrolytes using scalable processes is not only challenging, but also adds extraneous volume since SOA cathodes are fully lithiated. Here we show the potential for “Li-free” battery manufacturing using the Li 7 La 3 Zr 2 O 12 (LLZO) electrolyte. We demonstrate that Li-metal anodes >20 μm can be electroplated onto a current collector in situ without LLZO degradation and we propose a model to relate electrochemical and nucleation behavior. A full cell consisting of in situ formed Li, LLZO, and NCA is demonstrated, which exhibits stable cycling over 50 cycles with high Coulombic efficiencies. These findings demonstrate the viability of “Li-free” configurations using LLZO which may guide the design and manufacturing of high energy density solid-state batteries.
25 ENERGY STORAGE↗
Isothermal Microcalorimetric Analysis of Li/CF x Battery Discharge
Lithium/carbon monofluoride (Li/CF x ) batteries generate substantial amounts of heat during discharge in part due to the large deviation of the loaded voltage from the measured and thermodynamically predicted open circuit voltage. Here, in this study, we further analyze this system by estimating the equilibrium voltage (V eq ) and its temperature dependence (dV eq /dT) over the entire discharge range. Based on these results, the ohmic and entropic heat contributions to the overall heat flow are calculated from experimental data. The ohmic heat flow is consistent with an electrochemical impedance spectroscopy model and electrode density measurements, indicating decreasing porosity of the electrode during discharge. Entropic heating increases during discharge as the cell reaction becomes increasingly entropically unfavorable. The total energy dissipated by the cell (electrical and thermal) remains similar over the entire discharge, evidence for a two-phase reaction with a constant, and rate-independent, ΔG rxn of about -465 kJ mol -1 . However, near the end of discharge (>90% DOD), ΔG rxn changes significantly indicating possible secondary reactions.
25 ENERGY STORAGE↗
Reduction of Methane Leaks through Corrosion Mitigation Pre-treatments for Pipelines with Field Applied Coatings
Corrosion of buried, coated steel pipelines transporting natural gas is a significant source of methane emissions, from pipeline venting required for maintenance and repairs and from pipeline leaks and incidents. Corrosion of steel under field applied coatings is an important safety concern for the pipeline industry. This project investigates the application of a field applied alloy over girth welds to mitigate external corrosion of buried coated steel pipelines. Various metallic coating options were considered, which were required to meet several criteria: (1) it must resist corrosion under open-circuit or mild cathodic protection conditions, (2) it must protect the substrate steel, and (3) it must not negatively affect the adhesion of the field coating. Finite element models and lab testing were performed of alloy coating compositions to identify promising alloy types underneath disbonded coatings. Polarization curves of coating alloys were generated to provide the boundary conditions for the COMSOL model to compute potential and current distributions around coated areas. Sacrificial and corrosion-resistant metal alloy coatings were evaluated and optimized using corrosion modeling and laboratory electrochemical testing, where aluminum alloy 5356 (5% Mg) and steel alloy B9 (9% Cr) were selected. Corrosion test coupons were designed and fabricated using thermal spray aluminum 5356 and welded B9 steel overlays on API 5L grade X42 line pipe steel. The corrosion test coupons, with simulated pipe coating damage, were tested in a laboratory soil box and a field pipeline site in Texas for 3-months. Corrosion test coupons were then tested for 6-months at field pipeline sites in Texas and Tennessee to quantify corrosion rates and performance of the aluminum and steel alloys under polyethylene tape and 2-part epoxy coatings, various coating holidays, and with and without cathodic protection.
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