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The surging demand for Li-ion batteries (LIBs) has started a quest for innovations in their design and technology. A notable improvement in this regard involves the use of Silicon (Si) as the active anode material in LIBs. However, a major challenge stopping its widespread adoption is the considerable volume change experienced by Si during the lithiation-delithiation process, leading to volumetric stress-induced capacity degradation. This study identifies three primary capacity fade mechanisms in these LIBs: volumetric-stress-induced cracking and delamination, along with the growth of the solid electrolyte interface (SEI) during charging and discharging cycles. These mechanisms are influenced by battery design and operating conditions, such as Si anode thickness, ambient working temperature, and charging rate, introducing uncertainty into the battery’s degradation rate. In this study, multiple finite element (FE) models are constructed to simulate capacity degradation resulting from these three capacity fade mechanisms and their predictions are validated against experimental data. To address the computational demands of multiple FE models simulating capacity degradation from these fade mechanisms, a Gaussian Process Regression (GPR) surrogate model is developed. This GPR model efficiently predicts capacity fade and is validated for accuracy. Subsequently, the GPR model is used in an uncertainty quantification study that is focused on the battery’s design and operating conditions. The objective is to pinpoint the factors that exert the most significant influence on capacity degradation in Si anode-based LIBs.
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This content will become publicly available on March 7, 2026
Modeling the Dynamics of Cylindrical Lithium-ion Battery Aging Due to Evolving Solid Electrolyte Interphase Layer
The solid electrolyte interphase (SEI) layer plays a critical role in the aging and degradation of lithium-ion batteries (LIBs), directly influencing their performance and longevity. This paper presents a physics-based model that quantitatively characterizes SEI layer growth in cylindrical LIBs by incorporating ionic current density as a governing parameter. The presented approach captures localized SEI dynamics by coupled state-space Eqs. (SSEs) within an convex optimization framework. The model accounts for both uniform and nonlinear SEI growth phases, predicting capacity fade and impedance evolution over cycling aging. Validation against experimental charge-discharge profiles, electrochemical impedance spectroscopy (EIS) characterization, and equivalent circuit modeling demonstrates the model’s precision in tracking SEI-related degradation. The proposed framework offers a robust, interpretable, and computationally efficient tool for battery diagnostics and lifetime prediction.
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- Award ID(s):
- 2213918
- PAR ID:
- 10609339
- Publisher / Repository:
- https://iopscience.iop.org/journal/1945-7111
- Date Published:
- Journal Name:
- Journal of The Electrochemical Society
- Volume:
- 172
- Issue:
- 3
- ISSN:
- 0013-4651
- Page Range / eLocation ID:
- 030508
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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