Microstructure-Resolved Impedance Modeling of Solid-State Batteries
Abstract
Solid-state batteries (SSBs) with solid electrolytes (SEs) are attracting substantial investment from the automotive industry to enable fast-charging and safer next-generation electric vehicles. The microstructure of the SE critically affects the battery performance. Electrochemical impedance spectroscopy (EIS) is a powerful, non-destructive probe of charge-transfer and transport processes within SEs and across electrode/SE interfaces but quantitatively connecting measured impedance spectra to the underlying microstructural features remains an open modeling challenge. Here we present a modeling framework that predicts the microstructure-resolved impedance response of a Li/Li6PS5Cl/Li symmetric cell. Taking the Li6PS5Cl SE microstructures from phase-field sintering simulations, we find that increasing porosity shifts the Nyquist semicircle to higher impedance while improved inter-particle contact reduces it, and that impedance decreases monotonically as sintering proceeds. We also isolate the effect of solid electrolyte interphase (SEI) formation: grain boundaries raise impedance only slightly, but SEI volume fraction, and the conductivity of each SEI phase (Li2S, Li3P, LiCl), both strongly affect it. A realistic multi-phase SEI increases interfacial impedance by about two orders of magnitude compared with a grain boundary only network. We further demonstrate that the same pipeline can operate directly on experimental micrographs by extracting pixel-level phase information with AI-assisted image segmentation and using it to predict an impedance spectrum, illustrating a path toward quantitative microstructure-impedance correlations grounded in real, imaged SE microstructures.
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BibTeX
@misc{islam2026microstructureresolved,
title = {{Microstructure-Resolved Impedance Modeling of Solid-State Batteries}},
author = {Kaniza Islam and Chengyin Wu and Noriko Katsube and Yanzhou Ji},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.15783},
eprint = {2609.15783},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.15783},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Microstructure-Resolved Impedance Modeling of Solid-State Batteries
AU - Kaniza Islam
AU - Chengyin Wu
AU - Noriko Katsube
AU - Yanzhou Ji
PY - 2026
DA - 2026/09/28
PB - arXiv
DO - 10.48550/arxiv.2609.15783
UR - https://arxiv.org/abs/2609.15783
AB - Solid-state batteries (SSBs) with solid electrolytes (SEs) are attracting substantial investment from the automotive industry to enable fast-charging and safer next-generation electric vehicles. The microstructure of the SE critically affects the battery performance. Electrochemical impedance spectroscopy (EIS) is a powerful, non-destructive probe of charge-transfer and transport processes within SEs and across electrode/SE interfaces but quantitatively connecting measured impedance spectra to the underlying microstructural features remains an open modeling challenge. Here we present a modeling framework that predicts the microstructure-resolved impedance response of a Li/Li6PS5Cl/Li symmetric cell. Taking the Li6PS5Cl SE microstructures from phase-field sintering simulations, we find that increasing porosity shifts the Nyquist semicircle to higher impedance while improved inter-particle contact reduces it, and that impedance decreases monotonically as sintering proceeds. We also isolate the effect of solid electrolyte interphase (SEI) formation: grain boundaries raise impedance only slightly, but SEI volume fraction, and the conductivity of each SEI phase (Li2S, Li3P, LiCl), both strongly affect it. A realistic multi-phase SEI increases interfacial impedance by about two orders of magnitude compared with a grain boundary only network. We further demonstrate that the same pipeline can operate directly on experimental micrographs by extracting pixel-level phase information with AI-assisted image segmentation and using it to predict an impedance spectrum, illustrating a path toward quantitative microstructure-impedance correlations grounded in real, imaged SE microstructures.
ER -