# Intelligent Monitoring Systems for Electric Vehicle Charging
###### 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. e 29 January 202519 February 20251  
point availability during peak hours, significantly impacting both commercial viability and user experience [3]. While next-generation parking technologies have achieved notable successes, e.g., reducing search times by 43% and parking-related congestion by 30% through automated systems and smart controls [4], the unique challenges of EV charging infrastructure demand more advanced solutions. Current implementations using Internet of things (IoT) sensors and artificial intelligence (AI) demonstrate promising approaches for optimizing both parking efficiency and charging infrastructure utilization [5]. However, these systems often operate in isolation, lacking the integrated approach necessary for addressing the inherent complexity of EV charging management. A fundamental challenge lies in the stochastic nature of user behavior and charging patterns [6]. Critical variables, including arrival times, battery requirements, and charging durations, exhibit significant uncertainty [7]. This variability undermines traditional optimization approaches that assume deterministic behavior, highlighting the need for more adaptive models capable of responding to the probabilistic nature of user interactions. Current systems lack the monitoring and control mechanisms needed to effectively manage these uncertainties while maintaining optimal infrastructure utilization. This paper addresses these challenges through two primary contributions:  
1. A review of current EV charging monitoring technologies, evaluating their effectiveness and limitations. The review analyzes key performance indicators, including anomaly response times, detection accuracy, and station resource utilization, to identify potential best practices and areas for improvement. 2. An architectural framework that builds on established technologies while introducing approaches to system integration and behavioral analysis. This framework combines distributed processing for fault detection and improved security [8], real-time data management for high-availability systems [9], and IoT-based monitoring for anomaly detection in car parks [10] with the goal of achieving improvements in system performance and reliability. This framework reviews existing methodologies and proposes performance benchmarks to inform future implementations, focusing on system-level architectural patterns and integration strategies for EV charging monitoring systems. This strategy aims to support operational capabilities and provide a scalable platform for incorporating emerging technologies.