# Intelligent Monitoring Systems for Electric Vehicle Charging
## 4 Intelligent EV Charging Monitoring System
### System Architecture Overview
#### 4.1.2 Edge Computing Layer  
The edge computing layer performs distributed processing and real-time analytics at charging sites, reducing cloud bandwidth and latency [49; 64]. This layer uses a hierarchical architecture with multiple processing tiers, each specialized for specific tasks and data management [3; 46]. This design prioritizes rapid local decisions through real-time analytics, anomaly detection, and adaptive data handling while maintaining security and interoperability [52]. These capabilities are essential for autonomous operation and reliability. _Local processing units (LPUs)_ form the base of the hierarchy, integrated within each charging station for real-time analytics of raw sensor data streams [19; 46]. These units utilize parallel processing hardware, such as multi-core ARM processors, to concurrently monitor multiple charging parameters [64; 65]. Specifically, current draw, voltage levels, and power factor are measured with a high resolution (e.g., 100 ms) using sliding window analysis, which facilitates the rapid detection of charging state transitions and potential anomalies [3; 11]. Adaptive thresholding mechanisms should be implemented to ensure accuracy across diverse EV models and varying environmental conditions by dynamically adjusting based on learned patterns and real-time context [18]. Another critical function of the edge layer is _anomaly detection_, which is designed to enhance the safety and efficiency of the charging process [3; 21]. To identify and classify deviations from expected charging patterns, a multi-stage filtering approach should be implemented [69]. This approach can include an initial stage relying on statistical methods such as Kalman filtering and exponential smoothing, with parameters dynamically ad -justed based on historical data and operational context to identify potential anomalies [3]. Subsequently, lightweight neural networks optimized for edge deployment with a small memory footprint (e.g., under 100 MB) may be used to classify these deviations [46]. These neural networks should be designed to achieve high detection accuracy for known anomaly types [43]. The neural network models can be trained using a dataset of charging session data, incorporating both supervised and unsupervised learning techniques and continuous online learning to adapt to evolving anomaly patterns [50]. A secure model update system, allowing for incremental over-the-air updates, could also ensure optimal performance while minimizing disruption to charging operations [18], and backwards compatibility can be maintained by allowing local processing units (LPUs) to temporarily operate multiple model versions. Efficient _data management_ at the edge layer requires adaptive sampling based on charging session phases [3; 49]. During critical transitions like charge initiation and termination, the sampling frequency increases to capture detailed behavior patterns, whereas routine charging phases can employ reduced rates to conserve resources [19]. To optimize data handling efficiency, the system could use compression algorithms with dynamic ratios, which are adjusted according to network conditions and storage availability [63]. For autonomous operation and rapid response, LPUs implement _Local Decision-Making_ through a hybrid system combining rule-based expert systems with fuzzy logic controllers [70; 71]. The expert system encodes industry standards and regulatory requirements into deterministic rules--for example, automatically terminating charging if battery temperature exceeds safety thresholds [17]. Fuzzy logic controllers manage scenarios with inherent uncertainty, such as optimizing charging rates under variable grid conditions [24]. Membership functions for fuzzy variables derive from empirical data and domain expertise [7]. This architecture enables intelligent local decisions with low response latencies for critical events while identifying optimization opportunities, such as load balancing across charging stations [28; 32].