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This paper presents data-driven impedance-based state of health (SOH) estimation for commercial lithium-ion batteries across an SOH range of ~96% to ~60%. Battery health indicators at the transition frequency of the battery impedance Nyquist plot are utilized to develop an SOH estimator based on an artificial neural network (ANN). In addition, two more ANN-based SOH estimators utilizing some impedance magnitude and phase values are developed. Spearman correlation analysis is utilized to identify the frequency points at which the impedance magnitude and phase values show strong correlations with SOH values and are thus utilized as SOH indicators. The performance evaluation of the developed SOH estimators shows that the maximum root mean square error (RMSE) is equal to 1.39%, the maximum mean absolute error (MAE) is equal to 1.25%, the maximum mean absolute percentage error (MAPE) is equal to 1.55%, and the minimum coefficient of determination (R2) is equal to 0.983. 

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. 

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. 

This paper presents a state-of-health (SOH) estimation algorithm and hardware platform for ‎lithium-ion batteries. Based on features obtained from the battery’s electrochemical impedance ‎spectroscopy (EIS), an artificial neural network (ANN)-based SOH algorithm is developed. EIS measurements collected at different aging levels are utilized to train and test the SOH estimation algorithm. The minimum impedance magnitude and the impedance magnitude at zero phase show correlations with the battery SOH level and can be utilized to indicate the SOH value. The SOH estimation algorithm performance is evaluated, and the performance evaluation results indicate that the SOH estimation algorithm can be utilized to estimate the SOH. 

This paper presents characterization for equivalent circuit model (ECM) parameters variation for lithium-ion capacitor ‎‎ (LiC) under different voltage values. A set of experimentally obtained electrochemical impedance spectroscopy (EIS) data ‎for LiC is fitted using the simplex algorithm to obtain the values for ECM parameters. The model-‎fit EIS data is compared with the measured EIS data to validate the model.