Models for Forecasting Electric Vehicle Charging Demand—A Review

The rapid expansion of electric vehicles (EVs) introduces highly dynamic, unpredictable load profiles that threaten power grid stability and complicate operation across distribution systems and battery swapping facilities. Accurately forecasting EV charging demand is critical for dynamic load management, infrastructure planning, and risk mitigation. This paper presents a comprehensive, systematic review of the operational pipeline and predictive methodologies for EV charging power and energy demand. We analyze the complete data processing workflow, including examining data sources, spatial–temporal resolution trade-offs, advanced imputation, denoising, and anomaly detection techniques. Furthermore, we establish a structured taxonomy of forecasting methods, critically evaluating classical time-series models, machine learning, deep architectures, graph spatiotemporal networks, large language models (LLMs), hybrid ensembles, and fuzzy-logic systems. By offering detailed comparative syntheses across methodologies, this review provides a solid foundational framework for risk assessment, smart grid management, and operational decision-making in evolving electromobility ecosystems.