Recent advancements and perspectives in lithium-ion battery aging: Mechanism, characterization, and prediction

Lithium-ion battery aging represents a fundamental challenge affecting both performance degradation and safety risks in energy storage systems. This review presents a systematic examination of aging mechanisms, advanced characterization techniques, and state-of-the-art prediction methodologies. The analysis begins with a comprehensive evaluation of intrinsic (electrode materials, electrolyte decomposition, separator degradation, current collector corrosion) and extrinsic (temperature, C-rate, depth of discharge, state of charge) factors governing capacity fade. We establish a critical equivalence framework between natural aging and accelerated aging protocols, followed by an in-depth assessment of characterization approaches including: (1) in situ material analysis, (2) electrochemical diagnostics, and (3) multiphysics modeling, with particular emphasis on emerging in situ ultrasonic detection and a novel electrochemical impedance spectroscopy online monitoring scheme. The review further develops predictive algorithms for state-of-health assessment, remaining useful life estimation, and knee-point detection, introducing a robust neural network-based aging estimation method optimized for dynamic operating conditions. By synthesizing current research frontiers and persistent challenges, this work provides both fundamental insights into battery aging phenomena and practical solutions for aging state evaluation, offering valuable guidance for next-generation battery management system development.