High C-rate Li-NMC/graphite pouch cell end-of-life prediction cycle-dependent variations and machine learning.

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.