ABSTRACT The cycle life of rechargeable batteries, such as lithium‐ion and sodium‐ion systems, is a critical performance metric that determines their suitability for various applications in the context of a low‐carbon economy, such as electric vehicles and grid‐scale renewable energy storage. Accurate prediction of battery cycle life is vital for optimizing battery design, improving safety, and enabling effective battery management systems. Recent advances demonstrate that machine learning (ML) methods are extremely beneficial for extracting insights from experimental and simulated data to model and predict battery degradation. This review provides a comprehensive and up‐to‐date synthesis of ML applications for predicting the cycle life of lithium‐ion and sodium‐ion batteries. We also outline the core principles of widely used algorithms, including supervised, unsupervised, semi‐supervised, and deep learning methods, and discuss their relative strengths and limitations in this context, thereby accelerating the transition to a low‐carbon economy by reducing experimental waste, optimizing battery utilization, and enabling second‐life applications.
Zhang et al. (Sun,) studied this question.