A Review on Artificial Intelligence Enabled Battery Energy Storage: Fault Diagnosis, Health Estimation and Predictive Optimization

Battery Energy Storage Systems (BESS) are key to improving the reliability, energy efficiency, and flexibility of the contemporary power grid. With their penetration in various applications, including electric vehicles, smart grids, and renewable integration, the safe and secure operation of distributed resources (DRs) becomes more crucial. Traditional Battery Management Systems (BMS) that are usually on rule-based or physics-based models can hardly adapt to various chemistries, operation conditions, and fault scenarios. Approaches based on Artificial Intelligence (AI), such as machine learning (ML) and deep learning (DL), have recently been proposed to provide more promising data-driven options for fault diagnosis, state of health (SoH) estimation, and predictive optimization. This work provides a cutting-edge review of AI deployment in BESSs, which is conceptually classified into three fundamental categories: fault detection and classification, health monitoring and degradation prognosis, and intelligent control and optimization. We discuss the merits and limitations of different AI models, including supervised / unsupervised / hybrid / reinforcement learning, deployment feasibility, interpretability, and data requirements. An analysis of novel techniques, including digital twin modeling, explainable AI, and secure learning frameworks, is also included. Using comparative analysis, taxonomy visualizations, and performance summaries, this review points out the current limitations, standardization issues, and future research directions that are required for industrial-scale deployment. Through capturing the cutting-edge advancements in this field, we hope this work will serve as a guiding reference for the researchers and industry participants to design robust, scalable, and dependable AI-supported battery management systems.