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Lithium-ion batteries are an essential component of modern energy storage; however, their operating life is limited by complex, path-dependent degradation. This review offers a clear overview of material degradation processes in a path-dependent framework that systematically links material-level degradation mechanisms to resulting macroscopic modes and their measurable electrochemical signatures. It thoroughly investigates the main degradation mechanisms during cycling with critical cross-talk between cell components and the synergistic impact of operating stressors, including the growth of the Solid-Electrolyte Interphase (SEI), Cathode-Electrolyte Interphase (CEI), lithium plating, and mechanical cracks and fractures to resulting modes such as loss of lithium inventory (LLI), Loss of Active Material (LAM), and Impedance growth. A distinct contribution of this work lies in the systematic categorization of degradation modes by tracking their evolution in Nyquist plots, providing a clearer link between impedance features and the underlying aging processes. This review links diagnostic methods, including ex-situ and operando characterization techniques, with electrochemical techniques like Electrochemical Impedance Spectroscopy (EIS) enhanced by Distribution of Relaxation Times (DRT), demonstrating how a complex impedance growth curve can be clearly separated for overlapping degradation modes, which are utilized to identify various failure paths and estimate battery health. Finally, the review brings these insights together into practical battery management strategies, emphasizing the ongoing shift from purely data-driven approaches toward hybrid physics-informed machine learning frameworks. By integrating insights from degradation chemistry, operando diagnostics, and modelling, this review highlights critical gaps in current research, especially in real-time separation of degradation modes, validation under realistic lifetime conditions, and uncertainty-aware health estimation. This study underscores the interrelated, path-dependent nature of battery degradation, emphasizing the importance of an integrated, multi-scale approach to guide the development of longer-lasting batteries and an innovative predictive model.
Arif et al. (Tue,) studied this question.