Microstructure Quantification and Random Forest Regression Models for Li4Ti5O12–Ni Property Prediction

William Huddleston; Frederick Dynys; Alp Sehirlioglu

NASA NTRS · 20205011820

Microstructure Quantification and Random Forest Regression Models for Li4Ti5O12–Ni Property Prediction

William Huddleston·Frederick Dynys·Alp Sehirlioglu

Abstract

All-solid-state structural lithium-ion batteries are sought to enable all-electric propulsion in next generation aerospace concepts through improved safety and systems level weight savings. In this work, the influence of processing conditions on microstructural evolution was evaluated for anode composites of strain-free Li4Ti5O12 and metallic nickel current collector. Beyond size distributions, this study explored methods of quantifying microstructural features that describe changes in the spatial distribution and coalescence of nickel particles as a function of sample composition and sintering conditions. Processing-microstructure-property relationships were described by microstructure quantifiers including nickel particle count per area, nearest neighbor distance distribution, and edge-to-edge distance distribution. Machine learning methods were applied to compare the relative influence of processing conditions and microstructural features on electrical conductivity and mechanical strength to optimize for simultaneous energy storage and load bearing performance. Insights gained from this work inform future evaluation of alternative energy storage materials and microstructures for multifunctional performance, and generation of microstructural descriptors strengthens modeling across length scales.

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William Huddleston, Frederick Dynys, Alp Sehirlioglu. Microstructure Quantification and Random Forest Regression Models for Li4Ti5O12–Ni Property Prediction. https://ntrs.nasa.gov/citations/20205011820

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