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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Modeling and validation for performance analysis and impedance spectroscopy characterization of lithium-ion batteries
A parameterized mathematical model for Lithium-ion battery cell is presented in this paper for performance analysis with a particular focus on battery discharge behavior and electrochemical impedance spectroscopy profile. The model utilizes various physical properties as input and consists of two major sub-models in a complementary manner. The first sub-model is an adapted Doyle-Fuller-Newman (DFN) framework to simulate electrochemical, thermodynamic, and transport phenomena within the battery. The second sub-model is a calibrated solid-electrolyte interphase (SEI) layer formation model. This model emphasizes the electrical dynamic response in terms of the reaction process, layer growth, and conductance change. The equivalent circuit component values are derived from the outputs of both sub-models, reflecting the battery’s changing physical parameters. The simulated discharge curves and electrochemical impedance spectroscopy (EIS) profiles are then provided with a comparison against empirical results for validation, which exhibit good agreement. This modeling methodology aims to bridge the gap between the physical model and the equivalent circuit model (ECM), enabling more accurate battery performance predictions and operation status tracking.
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- Award ID(s):
- 2213918
- PAR ID:
- 10517149
- Publisher / Repository:
- Elsevier (https://www.sciencedirect.com/)
- Date Published:
- Journal Name:
- Next Energy
- Volume:
- 5
- Issue:
- C
- ISSN:
- 2949-821X
- Page Range / eLocation ID:
- 100153
- Subject(s) / Keyword(s):
- Li-ion battery Mathematical modeling Physics model Simulation and validation EIS ECM
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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