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Gering, Kevin

Publications and source records attributed to Gering, Kevin.

6 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Differentiable modeling and optimization of non-aqueous Li-based battery electrolyte solutions using geometric deep learning

Electrolytes play a critical role in designing next-generation battery systems, by allowing efficient ion transfer, preventing charge transfer, and stabilizing electrode-electrolyte interfaces. In this work, we develop a differentiable geometric deep learning (GDL) model for chemical mixtures, DiffMix, which is applied in guiding robotic experimentation and optimization towards fast charging battery electrolytes. In particular, we extend mixture thermodynamic and transport laws by creating GDL-learnable physical coefficients. We evaluate our model with mixture thermodynamics and ion transport properties, where we show improved prediction accuracy and model robustness of Diff-Mix than its purely data-driven variants. Furthermore, with a robotic experimentation setup, Clio, we improve ionic conductivity of electrolytes by over 18.8% within 10 experimental steps, via differentiable optimization built on DiffMix gradients. By combining GDL, mixture physics laws, and robotic experimentation, DiffMix expands the predictive modeling methods for chemical mixtures and enables efficient optimization in large chemical spaces.

25 - ENERGY STORAGE↗
DOE OSTI2023

Localized high-concentration electrolytes get more localized through micelle-like structures

Liquid electrolytes in batteries are typically treated as macroscopically homogeneous ionic transport media despite having a complex chemical composition and atomistic solvation structures, leaving a knowledge gap of the microstructural characteristics. Here, we reveal a unique micelle-like structure in a localized high-concentration electrolyte, in which the solvent acts as a surfactant between an insoluble salt in a diluent. The miscibility of the solvent with the diluent and simultaneous solubility of the salt results in a micelle-like structure with a smeared interface and an increased salt concentration at the centre of the salt–solvent clusters that extends the salt solubility. These intermingling miscibility effects have temperature dependencies, wherein a typical localized high-concentration electrolyte peaks in localized cluster salt concentration near room temperature and is used to form a stable solid–electrolyte interphase on a Li metal anode. Importantly, these findings serve as a guide to predicting a stable ternary phase diagram and connecting the electrolyte microstructure with electrolyte formulation and formation protocols of solid–electrolyte interphases for enhanced battery cyclability.

25 ENERGY STORAGE↗
DOE OSTI2023

Rational designs to enable 10-min fast charging and long cycle life in lithium-ion batteries

A daunting challenge in the design of lithium ion batteries (LiBs) is enabling 10-min extreme fast charging (XFC) while achieving appreciable charge acceptance and cycle life. This desirable outcome requires both a comprehensive understanding of LiB operation and aging behavior at different length scales and careful optimization. Lithium plating has been a critical bottleneck because, at XFC rates, it consumes cyclable lithium causing distinct aging and safety concerns even in moderate-loading LiBs. Here, we propose combining multiple solutions, including materials-to-electrode design-to-charging protocols, that are intended to overcome limitations in lithium-ion transport in the electrolyte phase, thus enabling 10-min XFC in LiBs. Some implemented strategies include cathode chemistry, optimized carbon binder domain in the cathode, dual layer anode design, improved separator and advanced electrolyte. Further, innovative charging protocols in moderately loading (~3 mAh/cm 2 anode/2.7 mAh/cm 2 cathode) single-layer pouch cells are proposed, together with demonstrated 10-min XFC with higher charge acceptance between 87.3 and 92.1% (or 2–2.1 mAh/cm 2 ) for 600 cycles without lithium plating. This methodical study with well-defined cells shows promise in combining multiple solution strategies to enable 10-min XFC, charting a pathway to achieve XFC in higher-loading energy-optimized LiBs.

25 ENERGY STORAGE↗
DOE OSTI2021

Tutorial: Machine Learning and Artificial Intelligence in Batteries

Machine learning (ML) promises to compress the time needed to characterize battery performance, lifetime and safety. By coupling ML with physical models and metrics, that learning can bridge across materials, chemistries and cell designs. This tutorial will discuss the most popular ML techniques and resources and review recent work in the electrochemical literature. Applications include materials discovery, image recognition for quantitative microscopy analysis, fast charge algorithm development and life prediction.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning

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 LiFePO 4 /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.

25 ENERGY STORAGE↗