Consistent Modeling of SEI Growth via Electron Diffusion Validated Across Diverse Cell Specifications and Operating Conditions in LFP/Graphite Cells
Abstract
Understanding and predicting degradation in lithium-ion batteries is critical for their widespread deployment. The continuous growth of the solid-electrolyte interphase (SEI) remains a dominant aging pathway, yet accurately modeling its kinetics across diverse cell specifications and operating conditions remains a significant challenge. Here, we present and validate a physically grounded SEI growth model mediated by the electron diffusion mechanism. This framework intrinsically captures the state of charge (SOC) dependence of degradation through the impact of the anode potential on the electron concentration, which governs the rate of SEI growth. The presented model explicitly accounts for variations in electrolyte salt, anode material, temperature, and cycling rate. Validation against experimental aging data for LFP/graphite cells demonstrates high predictive accuracy across a broad range of cell specifications and cycling conditions, including confined SOC windows, electrolyte salts (LiFSI, LiPF6), artificial graphites (AGC, AGD), temperatures (40-70°C), and C-rates (C/20-1C). Remarkably, a single, consistent set of parameters is applied to simulate the capacity fade for all experiments without recalibration. While quantitative overestimations occur during high-SOC storage periods, the framework consistently captures the observed degradation trends. Our findings underscore that lower SOC operation mitigates degradation, establishing a physics-based model for predicting long-term lithium-ion battery aging.
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BibTeX
@misc{kobbing2026consistent,
title = {{Consistent Modeling of SEI Growth via Electron Diffusion Validated Across Diverse Cell Specifications and Operating Conditions in LFP/Graphite Cells}},
author = {Lukas Köbbing and William Black and Claire Floras and Saad Azam and Eniko Zsoldos and Ben Tang and J. R. Dahn and Birger Horstmann},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.29775},
eprint = {2610.29775},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.29775},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Consistent Modeling of SEI Growth via Electron Diffusion Validated Across Diverse Cell Specifications and Operating Conditions in LFP/Graphite Cells
AU - Lukas Köbbing
AU - William Black
AU - Claire Floras
AU - Saad Azam
AU - Eniko Zsoldos
AU - Ben Tang
AU - J. R. Dahn
AU - Birger Horstmann
PY - 2026
DA - 2026/10/21
PB - arXiv
DO - 10.48550/arxiv.2610.29775
UR - https://arxiv.org/abs/2610.29775
AB - Understanding and predicting degradation in lithium-ion batteries is critical for their widespread deployment. The continuous growth of the solid-electrolyte interphase (SEI) remains a dominant aging pathway, yet accurately modeling its kinetics across diverse cell specifications and operating conditions remains a significant challenge. Here, we present and validate a physically grounded SEI growth model mediated by the electron diffusion mechanism. This framework intrinsically captures the state of charge (SOC) dependence of degradation through the impact of the anode potential on the electron concentration, which governs the rate of SEI growth. The presented model explicitly accounts for variations in electrolyte salt, anode material, temperature, and cycling rate. Validation against experimental aging data for LFP/graphite cells demonstrates high predictive accuracy across a broad range of cell specifications and cycling conditions, including confined SOC windows, electrolyte salts (LiFSI, LiPF6), artificial graphites (AGC, AGD), temperatures (40-70°C), and C-rates (C/20-1C). Remarkably, a single, consistent set of parameters is applied to simulate the capacity fade for all experiments without recalibration. While quantitative overestimations occur during high-SOC storage periods, the framework consistently captures the observed degradation trends. Our findings underscore that lower SOC operation mitigates degradation, establishing a physics-based model for predicting long-term lithium-ion battery aging.
ER -