Toward Pre-Trained Model-Enabled Intelligent Fault Prognosis for Lithium-Ion Batteries

As safety and reliability requirements continue to rise in energy storage systems and related applications, fault prognosis has become a key enabler of stable operation and proactive safety management for lithium-ion batteries. Unlike conventional fault detection and diagnosis, fault prognosis focuses on predicting the occurrence time, evolution trend, and severity of potential faults, thereby strengthening risk awareness and decision-making proactivity in battery management systems (BMSs). However, existing prognosis methods still face substantial challenges under complex operating conditions, heterogeneous data sources, and highly nonlinear degradation dynamics, resulting in limited cross-scenario generalization, unstable long-horizon prediction, and insufficient uncertainty characterization. These limitations are becoming increasingly critical as lithium-ion batteries are widely deployed in electric vehicles and large-scale energy storage systems, creating an urgent need for prognosis approaches that are more accurate, robust, and scalable. Against this backdrop, this review provides a structured and forward-looking overview of lithium-ion battery fault prognosis with three objectives: systematically summarizing representative methodological routes, clarifying key technical challenges, and identifying research priorities for intelligent prognosis enabled by pre-trained models (PTMs). Specifically, we examine recent developments across three major technical routes—model-based, signal processing-based, and artificial intelligence (AI)-based methods. Building on this synthesis, we further discuss the opportunities introduced by PTMs for battery health management and analyze the key challenges of integrating PTMs into fault prognosis. Finally, in line with the evolution of intelligent BMSs, we outline future directions for enabling efficient, reliable, and trustworthy PTM-driven applications in lithium-ion battery fault prognosis, offering forward-looking insights for next-generation intelligent battery health management.