Unraveling Li-Ion Battery Degradation: Mechanisms and Forecasting

Abstract

Lithium-ion batteries are key to the global trend of adopting renewable energy, but they suffer from performance deterioration over use time which reduces lifespan, increases cost and creates safety risks. Tackling these problems requires a fundamental insight into the age mechanisms and the development of accurate prediction tools for systems, including electric vehicle applications through to grid-scale storage. This work offers an in-depth summary of the state-of-the-art insights into degradation phenomena and performance prediction for lithium-ion batteries, from atomic-level mechanisms to full-cell behavior. First, we examine how chemical, electrochemical, mechanical, and thermal processes interact to cause capacity loss and increased impedance, highlighting recent findings from operando studies. Second, we outline the diagnostic toolbox, from basic electrochemical techniques to advanced methods such as synchrotron X-ray probes, neutron imaging, and cryogenic electron microscopy. We further perform a critical comparison of forecasting models assessing empirical, equivalent circuit, physics-based, machine-learning, and hybrid digital twin approaches to Remaining Useful Life estimation for their accuracy, interpretability, and prospects for on-board deployment. This review integrates the newest high-impact research to outline key knowledge gaps and identifies future directions for the development of more durable, safe, and predictable energy storage systems.