# Intelligent Monitoring Systems for Electric Vehicle Charging
## 4 Intelligent EV Charging Monitoring System
### Core Monitoring Functions  
The system is proposed to implement a set of core monitoring functions, enabling real-time tracking, analysis, and optimization of the charging infrastructure [3; 65]. Thesefunctions are designed to address key challenges associated with EV parking and charging, including charging status monitoring and user behavior analysis [18; 19]. The _charging status monitoring_ system could implement a multi-resolution temporal architecture that enables both instantaneous state assessment and longitudinal trend analysis across charging sessions [3; 11]. Its core monitoring infrastructure would likely employ a distributed sensor network to sample key electrical parameters such as current, voltage, power factor, and energy transfer [17]. System-wide measurement synchronization could be ensured using precise timing protocols [19]. Current monitoring may employ hall-effect sensors with high measurement accuracy, with options available to balance cost and precision [17; 31]. Digital signal processing algorithms could enhance reliability through real-time filtering and adaptive compensation techniques, effectively compensating for temperature-induced drift and environmental noise [11]. _Adaptive sampling and data management_ strategies should utilize adaptive sampling techniques to optimize computational resources [46; 49]. For example, a variable timestep algorithm could increase sampling frequency during periods of rapid power fluctuation, such as transitions between charging phases, while reducing sampling rates during steady-state charging [32]. To track energy usage, accumulated energy transfer calculations should integrate these instantaneous power measurements [31], while the system performs uncertainty quantification using propagation of errors analysis [26]. Maintaining separate accumulator registers for bidirectional energy flow ensures precise tracking of vehicle-to-grid energy transfer where supported [30; 31]. The _charging time estimation_ system is expected to implement a hybrid prediction model combining physical battery modeling with machine learning techniques [24; 46]. The physical model can incorporate temperature-dependent charging characteristics and account for battery chemistry-specific parameters, including internal resistance and capacity [17; 41]. The ML component should adapt to individual battery ageing patterns and environmental conditions [29; 46]. This hybrid approach is designed to provide estimates for remaining charging time, aiming for reasonable accuracy, informed by the performance of similar systems in the literature [7]. Model refinement occurs continuously, with retraining performed at least weekly using real-time charging data to adapt to evolving battery characteristics [50]. For battery state of charge determination, the system should employ multiple estimation techniques based on available data sources [11]. For vehicles supporting direct SOC reporting through standard protocols (e.g., ISO 15118 or manufacturer-specific APIs), the system should maintain bidirectional communication with frequent updates [31]. In scenarios where direct SOC reporting is unavailable, coulomb counting could be employed, with drift compensation techniques to maintain accuracy [17]. This approach could be supplemented by voltage-based SOC estimation during key charging phases [40]. The multi-modal estimation approach aims for reasonable SOC accuracy, based on the capabilities of similar systems in the literature [11; 46]. _Charging phase detection_ can rely on a state machine implementation, augmented by fuzzy logic controllers to manage transitions between charging phases [70; 71]. The system architecture should recognize distinct charging phases, including initial constant current charging (0-80% SOC), the transition to the constant voltage or slow charging phase (80-100% SOC), and the completed charging state [7; 17].