146 Search Results

This study compares two battery modeling approaches for capacity expansion models: discrete-duration and continuous-duration formulations. In the discrete approach, battery duration is fixed, and power capacity is optimized. In the continuous approach, both power and energy capacities are decision variables, allowing storage duration to be optimized endogenously. Although both discrete-duration and continuous-duration battery formulations are used in long-term power system planning models, the literature has provided limited direct, systematic comparisons of their implications within a common modeling framework. To address this gap, this study implements both approaches in the Regional Energy Deployment System (ReEDS TM) capacity expansion model using twomore » resource adequacy methods, across a range of future system conditions, and with varying battery cost projections. Results show continuous-duration and high-resolution discrete approaches produce similar capacity expansion outcomes. The continuous formulation achieves faster runtimes compared to discrete-duration runs with many discrete-duration options. However, the discrete-duration approach allows users to choose to have limited fidelity for storage duration options, which in some cases can outperform the continuous formulation. The continuous formulation has the lowest overall system costs, indicating its ability to fine-tune storage duration to better meet specific system needs. This study's findings provide a side-by-side evaluation of discrete and continuous battery modeling approaches and offer guidance for improving the representation of real-world systems, flexibility, and computational efficiency for representing energy storage in long-term power system planning models.« less

Atomistic structures of materials offer valuable insights into their functionality. Determining these structures remains a fundamental challenge in materials science, especially for systems with defects. While both experimental and computational methods exist, each has limitations in resolving nanoscale structures. Core-level spectroscopies, such as X-ray absorption (XAS) or electron energy-loss spectroscopies (EELS), have been used to determine the local bonding environment and structure of materials. Recently, machine learning (ML) methods have been applied to extract structural and bonding information from XAS/EELS data. However, frameworks relying solely on a single data stream, defined as characterization data derived from a single element usingmore » one technique, are often insufficient because multiple local environments can yield similar spectral features, making it challenging to differentiate between competing structural hypotheses. Here, in this work, we address this challenge by integrating multimodal ab initio simulations, experimental data acquisition, and ML techniques for structure characterization. Our goal is to determine local structures and properties using EELS and XAS data from multiple elements and edges. To showcase our approach, we use various lithium nickel manganese cobalt (NMC) oxide compounds which are used for lithium ion batteries, including those with oxygen vacancies and antisite defects, as the sample material system. We successfully inferred local element content, ranging from lithium to transition metals, with quantitative agreement with experimental data. Beyond local element inference, we find that ML model based on multimodal spectroscopic data is able to determine whether local defects such as oxygen vacancy and antisites are present, a task which is impossible for single mode spectra or other experimental techniques. Furthermore, our framework is able to provide physical interpretability, bridging spectroscopy with the local atomic and electronic structures.« less

The Precision Reactor Oscillation and Spectrum Experiment, PROSPECT, was a segmented antineutrino detector that successfully operated at the High Flux Isotope Reactor in Oak Ridge, TN, during its 2018 run. Despite challenges with photomultiplier tube base failures affecting some segments, innovative machine learning approaches were employed to perform position and energy reconstruction, and particle classification. This work highlights the effectiveness of convolutional neural networks and graph convolutional networks in enhancing data analysis. By leveraging these techniques, a 3.3% increase in effective statistics was achieved compared to traditional methods, showcasing their potential to improve analysis performance. Furthermore, these machine learning methodologiesmore » offer promising applications for other segmented particle detectors, underscoring their versatility and impact.« less

Rapid electrochemical diagnostics, like DC pulse sequences or electrochemical impedance spectroscopy, are known to be useful for capacity prediction. However, it is unclear how previous results will map to different cell types and use cases and whether rapid diagnostics are useful for remaining useful life prediction or for detecting potential safety issues. To that end, we have collected a data set with ∼50,000 DC pulse measurements from four types of commercial lithium-ion batteries to enable training of state-of-charge, health, and safety diagnostic models via machine-learning. We demonstrate that 120-second DC pulse sequences can be used to predict capacity with 2%–9%more » average error, which can separate high- from low-capacity cells with only a 0.3% false positive rate but is not accurate enough to estimate remaining useful life. We also find that no safety related targets can be accurately predicted, highlighting the critical need for other non-invasive methods to diagnose battery safety.« less

Electrodepositing metal materials in large capacity, at low potential, and with high reversibility serves as the foundation for any aqueous rechargeable battery chemistry to realize the promises of high energy, low cost, and high safety. However, such a foundation is not solid because of the natural tendency of metals to form irregular, nonplanar, and often dendritic morphologies during electrochemical crystallization, which is further amplified in an acidic environment due to the faster kinetics of the coupled proton and mass-transport processes between hydrated metal ions and free metal atoms. As a typical representative, tin metal (Sn⁰) has potential to achieve highmore » energy in acidic batteries, but its nonuniform large-particle morphology, obtained from traditional electrodeposition, leads to dead Sn⁰ formation and deteriorating reversibility, accompanied by the sustained hydrogen evolution reaction (HER) and active Sn⁰ loss. Here, in this study, we report quaternary onium salts as effective interfacial cocations that, via selective adsorption, steadily texturize Sn⁰ deposition along the (211) plane, which is intrinsically inert to the HER, thus regulating the film deposition process by favoring the formation of planar Sn⁰ film. Such Sn⁰ film brings exceptional reversibility in acidic electrolytes, which translates into sustained cycling stability at applicable areal capacities in both anode-half cells (similar to 1500 deposition/dissolution cycles at 5 mAh cm^-2) and full cells (350 charge/discharge cycles at 5 mAh cm^-2). Textured electrodeposition with intrinsic HER-suppression capability provides a universal solution for diverse metal anode materials in rechargeable energy-dense aqueous batteries.« less

Early battery life prediction models are most useful for R&D if they help us understand the early changes in battery electrochemical response that correspond with long-term degradation and failure. Linear regression models such as Fused lasso and Partial Least Squares can fit coefficients directly to high-dimensional electrochemical data like capacity-voltage and ΔV–state-of-charge, i.e., Q(V) and ΔV(SOC) curves, learning coefficients that can be physically interpreted. We leverage the ISU-ILCC battery aging data set to learn high-dimensional coefficients for early battery life prediction from traditional slow-rate capacity check data, demonstrating learning on Q(V), dQ·dV⁻¹, and ΔV(SOC) curves. A thorough study on themore » dependence of coefficient values on train/test size and data preprocessing methods is made, demonstrating the reliability of high-dimensional regression approaches unless very small amounts of data are used for model training. For this data set, coefficients from Q(V) and dQ·dV⁻¹ models highlight changes in electrode stoichiometry due to lithium loss, while ΔV(SOC) coefficients highlight changes in positive electrode diffusivity due to particle cracking as well as electrode stoichiometry shifts. By directly interpreting the coefficients of a regression model, we make physical insights into battery degradation mechanisms without requiring the assumptions of traditional battery data analysis methods.« less

Recycling Li-ion batteries from electric vehicles is critical for reducing costs and supporting the development of a domestic battery supply chain. Direct recycling of cathodes, like LiNixMnyCozO2 (NMC), is attractive due to its low cost, energy use, and emissions compared to traditional recycling techniques. However, a comprehensive understanding of the active material properties at end-of-life is needed to guide direct recycling processes and the performance-dependent reuse applications. Here, NMC material from an end-of-life commercial pouch cell is characterized and bench-marked against pristine non-cycled counterparts with respect to capacity, impedance, crystallography, morphology, and microstructure to identify major degradation modes and understandmore » variability in the end-of-life material. The spatial heterogeneity of each property throughout the cell is also quantified. While the degraded material demonstrated similar capacity as the pristine, its impedance and rate capability are severely diminished. Furthermore, samples from the periphery of the electrode layers showed more severe performance loss compared to samples extracted from central regions. The dominant culprit of performance loss is the material microstructure, where the magnitude of particle cracking showed the strongest correlation to the impedance components that are most unfavorably impacted. This work suggests severe cracks in cathode active materials are the primary challenge that direct recycling methods must overcome.« less

Levelized cost of storage (LCOS) can be a simple, intuitive, and useful metric for determining whether a new energy storage plant would be profitable over its life cycle and to compare the cost of different energy storage technologies. However, researchers and industry decision makers still use conflicting definitions of LCOS. For example, some include charging cost, while others only include round trip efficiency (RTE) losses. Additionally, inputs to the existing formulations are not specific enough to generate repeatable results across studies, which reduces trust in the metric. To push for standardization in economic assessment of batteries and other energy storagemore » devices, the authors review existing definitions of LCOS and identify the desired characteristics for a standard. They then propose a new definition and demonstrate that it fits these characteristics very well relative to other prominent options. Unit analysis is applied to this proposed definition to provide a deeper understanding of the equations and to demonstrate its effectiveness. Finally, the sensitivity of LCOS to different input parameters is investigated to help users understand how to compare analyses from literature to their own. The authors also provide a spreadsheet and a Python script to streamline adoption of the proposed definition.« less

Mn-rich disordered rocksalt materials with Li-excess (DRX) materials have emerged as a promising class of earth-abundant and energy-dense next-generation cathode materials for lithium-ion batteries. Recently, an electrochemical transformation to a spinel-like “δ” phase has been reported in Mn-rich DRX materials, with improved capacity, rate capability, and cycling stability compared with previous DRX compositions. However, this transformation unfolds slowly over the course of cycling, complicating the development and understanding of these materials. In this work, it is reported that the transformation of Mn-rich DRX materials to the promising δ phase can be promoted to occur much more rapidly by electrochemical pulsingmore » at elevated temperature, rate, and voltage. To extend this concept, micron-sized single-crystal DRX particles are also transformed to the δ phase by the same method, possessing greatly improved cycling stability in the first demonstration of cycling for large, single-crystal DRX particles. To shed light on the formation and specific structure of the δ phase, X-ray diffraction, scanning electron nanodiffraction (SEND) and atomic resolution STEM-HAADF are used to reveal a nanodomain spinel structure with minimal remnant disorder.« less

Solid lithium peroxide (Li₂O₂) is the major discharge product in Li-air batteries. However, the electronically insulating nature of Li₂O₂ tends to affect the battery’s performance such as the polarization gap and cyclability. On the other hand, lithium superoxide (LiO₂), generated through a one-electron transfer process, offers greater electronic conductivity, lower charge transfer resistance, and thus reduced charge potential. Nevertheless, LiO₂ long-term stabilization as a final product remains a significant challenge. Here, in this study, we present the molybdenum (Mo)-based Janus chalcogenide family featuring asymmetric structures as a new generation of cathode catalysts for Li-air batteries. These catalysts demonstrate remarkable efficacymore » in stabilizing LiO₂ discharge products, even under high current densities of 5000 mA/g (corresponding to 0.5 mA/cm²). Our density functional calculations provide an understanding of why the asymmetric Mo-Janus chalcogenides result in LiO₂ formation whereas the symmetric Mo-dichalcogenides produce Li₂O₂ as the discharge product. These results pave the way to explore a new generation of advanced catalysts for superoxide-based Li-air batteries.« less