Electric vehicles (EVs) play a crucial role in addressing the energy crisis and mitigating the greenhouse effect. Lithium-ion batteries are the primary energy storage medium for EVs due to their numerous advantages. State of health (SOH) is a critical parameter for managing the health of lithium-ion batteries, and accurate SOH estimation forms the foundation of battery management systems (BMS), ensuring the safe operation of EVs. Data-driven deep learning techniques are attracting significant attention because of their strong ability to model complex nonlinear relationships, which makes them highly suitable for SOH estimation in lithium-ion batteries. This paper provides a comprehensive introduction to the common deep learning techniques used for SOH estimation of lithium-ion batteries, with a focus on model architectures. It systematically reviews the application of various deep learning algorithms in SOH estimation in recent years. Building on this, the paper offers a detailed comparison of these deep learning methods and discusses the current challenges and future directions in this field, with the aim of providing an extensive review of the role of deep learning in SOH estimation.

Machine learning models have become a prominent technique for predicting battery state in electric vehicles (EVs). However, due to the significant variability in the operating environments and conditions of different EVs, traditional machine learning models often exhibit limited generalization capabilities. Additionally, the computational limitations of on-board chips in conventional battery management systems (BMSs) can lead to considerable computational overhead. Furthermore, acquiring large-scale battery data for model training can be economically prohibitive. To address these challenges, this article proposes an Internet of Batteries (IoB) approach for battery health management, leveraging large language model (LLM) to monitor battery health. First, this article introduces the concept of IoB in EVs. Subsequently, an experimental IoB system is established. Through comparisons with other machine learning methods, the study demonstrates that LLM exhibit strong generalization capabilities for predicting the battery data of EVs, even with small-scale data fine-tuning. The experimental results suggest that the combination of LLM and IoB may represent a promising advancement over traditional machine learning approaches.

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.