High C-rate Li-NMC/Graphite Pouch Cell End-of-Life Prediction via Cycle-Dependent Variations and Machine Learning

Abstract

The accurate prediction of end-of-life for lithium-ion batteries is crucial for enhancing safety, reliability, and cost-efficiency in electric vehicles and energy storage systems. This study investigates the degradation characteristics of Li-NMC/graphite pouch cells under high C-rate conditions and introduces a machine learning-based predictive model for EoL estimation. Incremental capacity analysis is integrated with ensemble models such as Random Forest, Gradient Boosting, and CatBoost to extract electrochemical degradation features. Our model accurately predicts the cycle number at which state of health reaches 80%, with the Gradient Boosting algorithm achieving the highest prediction accuracy, with a root mean squared error of 17.63 and a mean absolute percentage error of 3.11. These findings demonstrate the potential of data-driven approaches for reliable battery health monitoring. The proposed framework can significantly contribute to the advancement of predictive maintenance strategies in battery management systems.