Edge-Cloud Collaborative Architectures for Real-Time EV Charging Control

Abstract

EV charging control is increasingly operating under distributed and timesensitive conditions in which chargers, local controllers, and cloud services must coordinate continuously. Existing studies have examined EV charging operation, intelligent monitoring, cloud-based forecasting, OCPP-based management, and collaborative cloud-edge control, showing that both local responsiveness and centralized optimization are important for real-time charging systems. However, the literature still lacks unified architectures that clearly partition control intelligence between edge and cloud layers. To address this issue, this article presents an edge-cloud collaborative architecture for real-time EV charging control based on edge-side sensing and actuation, cloud-side forecasting and optimization, synchronized control exchange, and adaptive feedback refinement. The results show reduced control response latency, improved charging stability, and stronger load coordination across distributed charging nodes compared with edge-only and cloud-only approaches. Overall, the study demonstrates that edge-cloud collaboration provides a scalable foundation for real-time EV charging control.