Capacity Estimation and Knee Point Prediction Using Electrochemical Impedance Spectroscopy for Lithium Metal Battery Degradation via Machine Learning.

ABSTRACT: Lithium-metal batteries (LMBs) are emerging as a promising next-generation energy storage due to their exceptionally high energy density. However, accurately predicting their performance remains challenging because of the complex degradation mechanisms. In this study, a machine learning (ML) framework is proposed that combines electrochemical impedance spectroscopy (EIS) with the XGBoost algorithm to develop two predictive models: one for estimating capacity degradation and another for detecting the knee point (KP)-a critical inflection point in the degradation trajectory. SHapley Additive exPlanations (SHAP) analysis is employed to interpret feature importance, revealing that low-frequency imaginary impedance components-associated with diffusion-limited processes such as lithium depletion

SUBMITTER: Si Q 

PROVIDER: S-EPMC12463089 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature