Intelligent monitoring systems for electric vehicle charging

Martins, Jaime; Rodrigues, Joao

http://hdl.handle.net/10400.1/26938

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Authors

Martins, Jaime

Rodrigues, Joao

Abstract(s)

Featured Application This paper reviews EV charging challenges and existing monitoring methods to pinpoint key gaps. From our review, we propose a practical monitoring framework that leverages IoT sensors, edge computing, and cloud services for real-time oversight, predictive maintenance, and responsive analysis of user behavior.Abstract The growing adoption of electric vehicles (EVs) presents new challenges for managing parking infrastructure, particularly concerning charging station utilization and user behavior patterns. This review examines the current state-of-the-art in intelligent monitoring systems for EV charging stations in parking facilities. We specifically focus on two key inefficiencies: vehicles occupying charging spots beyond the optimal fast-charging range (80% state-of-charge) and remaining connected even after reaching full capacity (100%). We analyze the theoretical and practical foundations of these systems, summarizing existing research on intelligent monitoring architectures and commercial implementations. Building on this analysis, we also propose a novel monitoring framework that integrates Internet of things (IoT) sensors, edge computing, and cloud services to enable real-time monitoring, predictive maintenance, and adaptive control. This framework addresses both the technical aspects of monitoring systems and the behavioral factors influencing charging station management. Based on a comparative analysis and simulation studies, we propose performance benchmarks and outline critical research directions requiring further experimental validation. The proposed architecture aims to offer a scalable, adaptable, and secure solution for optimizing EV charging infrastructure utilization while addressing key research gaps in the field.

Keywords

Electric vehicle charging Infrastructure monitoring Smart parking systems Edge computing Internet of things Predictive analytics User behavior analysis Energy management systems