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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.

Journal of the Electrochemical Society 169(6), 060517 (2022). doi:10.1149/1945-7111/ac6d13, Published by IOP Publishing, Bristol

Changes to the surface structure and chemistry occurring throughout the functional lifetime of lithium-ion batteries (LIBs) may impact the effectiveness of end-of-life rejuvenation methods. Solvent...

Reduced-order battery lifetime models, which consist of algebraic expressions for various aging modes, are widely utilized for extrapolating degradation trends from accelerated aging tests to real-world aging scenarios. Identifying models with high accuracy and low uncertainty is crucial for ensuring that model extrapolations are believable, however, it is difficult to compose expressions that accurately predict multivariate data trends; a review of cycling degradation models from literature reveals a wide variety of functional relationships. Here, a machine-learning assisted model identification method is utilized to fit degradation in a stand-out LFP-Gr aging data set, with uncertainty quantified by bootstrap resampling. The model identified in this work results in approximately half the mean absolute error of a human expert model. Models are validated by converting to a state-equation form and comparing predictions against cells aging under varying loads. Parameter uncertainty is carried forward into an energy storage system simulation to estimate the impact of aging model uncertainty on system lifetime. The new model identification method used here reduces life-prediction uncertainty by more than a factor of three (86% ± 5% relative capacity at 10 years for human-expert model, 88.5% ± 1.5% for machine-learning assisted model), empowering more confident estimates of energy storage system lifetime.

Liquid-phase infiltration of nickel (Ni) nanoparticles in Ni/yttria-stabilized-zirconia (YSZ) cermet anodes for solid oxide fuel cells can improve anode performance provided that the infiltrated nanoparticles on YSZ connect to form conducting pathways and the Ni nanoparticles do not coarsen significantly. This study explores liquid phase co-infiltration of Ni with mixed conducting oxides, the latter providing microstructural stability and conductive pathways between Ni nanoparticles. Two mixed conducting oxides have been studied: Gd0.1Ce0.9O2−δ (GDC), a predominantly ionic conductor, and La0.6Sr0.3Ni0.15Cr0.85O3−δ (LSNC), a predominantly electronic conductor. Experimental results show that both oxides improve the nickel nanoparticle stability and charge transfer kinetics. However, the electrochemical performance of the Ni-GDC-infiltrated electrode is much better than that of the Ni-LSNC-infiltrated electrode. This is attributed to the citrate–nitrate combustion reaction required to form LSNC, which fills the pores of the anode and inhibits gas diffusion, reducing the performance of the Ni-LSNC-infiltrated electrode.

Nickel – yttria stabilized zirconia (Ni-YSZ) anodes for solid oxide fuel cells were infiltrated with nickel nanoparticles with the objective of improving its performance by increasing the density of triple phase boundaries (TPBs). However, the nanoparticles deposited on the YSZ grains are not connected to one another, so the additional TPBs are not electrochemically active. It is postulated that at highly humid conditions (>95% H2O) created by high current density, nickel nanoparticles near the anode-electrolyte interface will spread and percolate, becoming electrochemically active. To study this behavior, infiltrated nickel nanoparticles were exposed to low, high and extreme humidity conditions during testing. When exposed to low humidity, no performance improvement or changes to the nanoparticle morphology was observed. At high humidity, the cell showed performance improvement and when cooled they showed nanoparticle coarsening. Exposure to extreme humidity (≈100%) caused performance to degrade back towards that of an un-infiltrated cell and severe nickel nanoparticles coarsening was observed in the cooled cell. This study validates the hypothesis that exposure to controlled high local humidity conditions can lead to the right amount of spreading of the deposited nickel nanoparticles to achieve percolation, thereby activating their TPBs and improving cell performance.

Liquid infiltration of NiO followed by reduction to form Ni nanoparticle catalysts in solid oxide fuel cell (SOFC) can produce a high density of electrochemical reaction sites. In recent years, electrode architectures utilizing porous oxide substrates with ionic conductivity or mixed ionic-electronic conductivity and connected networks of nickel produced by liquid infiltration have become a popular approach to improve SOFC anode catalytic performance, especially for operating temperatures less than 800 °C. However, infiltrated nickel structures suffer from poor durability, demonstrating significant loss in performance during the first 100 h of use. In contrast, traditional Ni-yttria stabilized zirconia (Ni-YSZ) cermet SOFC anodes exhibit long-term performance stability. However, Ni-YSZ cermet anodes have micron sized structures, and consequently have a significantly lower density of electrochemical reaction site density than infiltrated nickel structures, which have dimensions of around 100 nm. In this study, the performance impact of liquid phase infiltration of nickel nanoparticles into Ni-YSZ cermet anode supported SOFCs is studied by measuring the electrochemical behavior of infiltrated cells at 800 °C, 700 °C, and 600 °C, and comparing them to the performance of an uninfiltrated cell. Durability of the nanoparticles after electrochemical testing is also assessed using a method for quantifying particle statistics from fracture cross sections.

The performance of SOFCs degrades with increasing fuel utilization, since the fuel oxidation reaction faces increased competition for the three-phase-boundary (TPB) sites in the anode from adsorbed water vapor species. Introduction of addition TPB sites by infiltration of Ni nanoparticles into the anode is an effective way to alleviate this problem. Both liquid and vapor phase infiltration of Ni nanoparticles into commercially available Ni/YSZ cermet anodes have been explored. The microstructure of the anode has been examined by µ-CT and FIB/SEM 3-D reconstructions. The cells have been electrochemically characterized by I-V and EIS techniques in a temperature range of 600°C-800°C. The infiltrated Ni nanoparticles have been characterized by image analysis of SEM micrographs from fracture cross-sections, before and after electrochemical testing. The effect of the nanoparticles on cell electrochemical performance as a function of temperature and anode input water vapor content, the stability of the nanoparticles, as well as the thermodynamics of vapor phase infiltration of Ni will be presented.

Various modeling techniques are used to predict the capacity fade of Li-ion batteries. Algebraic reduced-order models, which are inherently interpretable and computationally fast, are ideal for use in battery controllers, technoeconomic models, and multi-objective optimizations. For Li-ion batteries with graphite anodes, solid-electrolyte-interphase (SEI) growth on the graphite surface dominates fade. This fade is often modeled using physically informed equations, such as square-root of time for predicting solvent-diffusion limited SEI growth, and Arrhenius and Tafel-like equations predicting the temperature and state-of-charge rate dependencies. In some cases, completely empirical relationships are proposed. However, statistical validation is rarely conducted to evaluate model optimality, and only a handful of possible models are usually investigated. This article demonstrates a novel procedure for automatically identifying reduced-order degradation models from millions of algorithmically generated equations via bi-level optimization and symbolic regression. Identified models are statistically validated using cross-validation, sensitivity analysis, and uncertainty quantification via bootstrapping. On a LiFePO4/Graphite cell calendar aging data set, automatically identified models utilizing square-root, power law, stretched exponential, and sigmoidal functions result in greater accuracy and lower uncertainty than models identified by human experts, and demonstrate that previously known physical relationships can be empirically “rediscovered” using machine learning.

Reduced-order physics-based life models are extremely useful for rapidly predicting battery state-of-health and for simulating battery lifetime in arbitrary aging conditions. However, identification of well-parameterized models is difficult. This is because, for maximum usefulness in predicting lifetime under a variety of conditions, aging test data exhibits many degradation mechanisms, which all need to be accurately modeled. However, because aging tests are time-consuming and expensive, especially for large-format batteries, a minimum of tests are conducted while probing many stress factors. Building a well-parameterized model is then very challenging: an under-parameterized model will neglect critical degradation modes, and an over-parameterized model will extrapolate poorly to new testing conditions. To complicate this matter, the functional form of the model for any individual degradation rate can be very difficult to identify. In this work, the statistical tools of penalized regression and bilevel optimization are used to help identify both the functional forms of and optimize the parameters of reduced-order life models, accelerating identification of robust models. Model robustness is demonstrated through traditional statistics methods of cross-validation and sensitivity analysis, uncertainty quantification through bootstrap resampling and Monte-Carlo simulation, and simulation of real-world use cases.

Infiltration of nickel nanoparticles into nickel – yttria-stabilized-zirconia (Ni-YSZ) cermet anodes improves anode performance by reducing the average nickel feature size of the electrode, thus increasing the density of triple phase boundaries (TPBs). However, the TPBs of nickel nanoparticles are not fully utilized because there is no conductive pathway between nanoparticles, and nickel nanoparticles suffer from rapid coarsening during operation. This work explores the infiltration of Gd0.1Ce0.9O2-δ (GDC) as a material for connecting and stabilizing infiltrated nickel nanoparticles. Anodes were infiltrated with Ni, GDC, and Ni-GDC to study their stability and electrochemical performance. Measurements show that the simultaneous infiltration of nickel and GDC results in a greater performance improvement than either nickel or GDC alone. From this result, it is speculated that GDC provides a conducting pathway between nickel nanoparticles, better utilizing their TPBs. Fitting of electrochemical impedance spectra with an equivalent circuit model is used to measure individual cell resistances, revealing that infiltration of GDC reduces the activation energy of the anodic charge transfer reaction from 1.29 eV to 0.74 eV. The Ni-GDC nanoparticles are also more durable than nickel nanoparticles, demonstrating microstructural stability after 120 h of constant current at 800 °C, while nickel alone shows extensive coarsening.

Electron-beam (EB) melting is used for the processing of refractory metals, such as Ta, Nb, Mo, and W. These metals have high value and are critical to many industries, including the semiconductor, aerospace, and nuclear industries. EB melting can also purify secondary feedstock, enabling the recovery and recycling of these materials. Currently, there is no method for measuring melt composition in situ during EB melting. Optical emission spectroscopy of the plasma generated by EB impact with vapor above the melt, a technique here termed electron-beam atomic spectroscopy, can be used to measure melt composition in situ, allowing for analysis of melt dynamics, facilitating improvement of EB melting processes and aiding recycling and recovery of these critical and high-value metals. This paper reviews the physics of the plasma generation by EB impact by characterizing the densities and energies of electrons, ions, and neutrals, and describing the interactions between them. Then several plasma models are introduced and their suitability to this application analyzed. Lastly, a potential method for calibration-free composition measurement is described and the challenges for implementation addressed.

Spent primary alkaline batteries present an unused source of secondary metals in Europe and the US, with at least 300,000 metric tons of batteries being landfilled each year. While battery recycling programs exist, current hydrometallurgical and pyrometallurgical processes are not profitable when used for dedicated alkaline battery recycling, so industry growth is difficult. A novel mechanical separation process consisting of shredding, baking, magnetic separation, and specific gravity separation was developed to recycle one metric ton per hour of alkaline batteries at lower cost than current methods, while being environmentally beneficial. Financial analysis was conducted using a Process-Based Cost Model to address the challenges of modeling a recycling process. At full capacity, the cost to recycle alkaline batteries via the developed process is $529 per metric ton, +/- 25%, not including transportation, with revenue of $383 per metric ton. This cost is lower than that of other reported processes, but is still not economically feasible. With supplemental revenue of $0.3 per kg, which could come from various sources, the return on investment can occur in just under 3 years. The low value of alkaline battery recovery material is identified as the most significant economic barrier for the recycling.