# Intelligent Monitoring Systems for Electric Vehicle Charging
## 4 Intelligent EV Charging Monitoring System
### System Architecture Overview
#### 4.1.1 Physical Layer  
The physical layer is the hardware foundation of the system, integrating sensors and components to collect data from the charging environment. This layer includes interconnected monitoring subsystems that operate simultaneously for complete data collection, balancing reliability, accuracy, and cost [10]. _Smart Charging Stations_ serve as the primary interface between vehicles and the monitoring system [46,54]. Each station should incorporate high-precision power monitoring sensors designed to reliably measure EV voltage and current as required by relevant standards (e.g., Level 3 EV chargers) [17]. Sensors should offer sufficient measurement accuracy and consistent sampling capabilities to support the system's monitoring objectives [19]. While a measurement accuracy of at least \(\pm 0.5\%\) and a consistent sampling rate of at least one sample per second is desirable, lower-cost sensors with slightly reduced accuracy (e.g., \(\pm 1\%\)) can be considered for cost optimization [19]. The selected charging station models need to prioritize reliability, adherence to relevant charging standards (e.g., IEC61851 [55], ISO15118 [56], and IEC62196 [57]), and ease of maintenance. Each of these should feature built-in power quality analysis and support bidirectional power flow measurement for energy storage systems (ESS) and vehicle-to-grid (V2G) applications [31], with integrated temperature compensation ensuring measurement stability across varying environmental conditions. Modular construction allows for future upgrades (e.g., integration with additional components like solar panels) and maintenance without requiring extensive system downtime [40]. Figure 1: System architecture overview of the intelligent EV charging monitoring system. Colors differentiate layers and their components. Solid lines indicate data flow, while dotted lines represent component relationships. For _vehicle presence detection_, a dual-technology approach combining magnetic field [58; 59] and ultrasonic sensors [15; 60] is recommended for enhanced reliability. Magnetic sensors should reliably detect metallic objects within a practical range and with a prompt response, while ultrasonic sensors should provide accurate positioning with frequent updates [5; 14]. This combination, suitable to be enhanced by cross-validation logic, could help ensure reliable detection and minimize false positives while incorporating anti-tampering mechanisms. The choice of specific sensor technologies should take into account cost, power consumption, ease of installation, and environmental resilience [51]. The _communication capabilities_ would best be implemented through a multi-protocol architecture centered on 4G LTE technology with future 5G integration (particularly 5G NR Sub-6 GHz and mmWave bands) as it becomes cost-effective [61; 62]. The system will need to support standard 4G LTE bands, with built-in redundancy and automatic fallback to 3G in areas with limited 4G coverage [63]. Additional communication options could include WiFi 6 compatibility with dual-band support and MIMO capability, secured through WPA3 protocols [53]. A gigabit Ethernet backbone can also provide reliable connectivity for fixed installations, while Bluetooth 5.0-5.4 facilitates local diagnostics and maintenance operations [48]. For _local processing_, we recommend cost-effective, industrial-grade computing units (e.g., based on ARM Cortex-A architecture with appropriate memory and storage) [64]. These units can run real-time operating systems and incorporate hardware-based encryption for security [52]. System reliability is further ensured through watchdog timer implementations and uninterruptible power supply backup systems, providing sufficient runtime under full load [65]. The processing capacity should be enough to handle data from multiple sensors and charging stations, with scalability achieved through a distributed processing architecture at the edge computing layer, allowing for load balancing and fault tolerance [49].