A comprehensive review of artificial intelligence techniques for energy storage systems: Charging demand forecasting, and grid integration

The rapid electrification of road transport introduces large, stochastic, and spatially distributed loads that challenge traditional planning and real-time operation of power systems. This review synthesizes recent advances in artificial intelligence (AI) that enable accurate Electric Vehicle (EV) Energy Storage charging demand forecasting, scalable smart-charging and routing policies, and bidirectional Vehicle-to-Grid (V2G) services while addressing data privacy and deployment constraints. This article systematically surveys deep learning approaches (Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Transformer), Graph Neural Networks (GNNs) that exploit topology and mobility graphs, Reinforcement Learning (RL) and multi-agent methods for sequential decision making and federated and privacy-preserving paradigms for distributed model training. For each class of methods modeling assumptions, input representations, loss functions, and evaluation metrics are analyzed; it further connects forecasting outputs to downstream tasks including charging station siting, fleet routing, market participation of EV battery, and ancillary-service provision. The review highlights practical deployment architectures (edge/cloud partitioning, communication requirements, latency bounds) and discusses algorithmic trade-offs between optimality, scalability, and robustness. Finally, it concludes with a taxonomy of AI methods for EV battery energy systems, a critique of current datasets and benchmarks, and a roadmap of open challenges including data heterogeneity, transferability across regions, battery degradation-aware control, adversarial resilience, and standards for privacy and interoperability. This comprehensive, AI-centric treatment aims to guide researchers and practitioners toward integrated, secure, and scalable EV–grid solutions.