# Intelligent Monitoring Systems for Electric Vehicle Charging
## 2 Related Work
### EVs Smart Parking
The system's performance was evaluatedagainst first-come-first-served baseline scheduling through comparative fairness metrics across charging configurations. [PERSON] and [PERSON] [24] proposed a semi-decentralized real-time charging scheduling framework that optimizes coordination between individual charging stations and a central operator. Their methodology employs a chance-constrained model at the central operator level to estimate aggregate charging demand through rolling optimization at coarse temporal resolution. The framework incorporates a Gaussian mixture model to characterize uncertainty in arrival and departure demand patterns. Individual charging stations determine power allocation based on station-specific urgency metrics and discharge availability, utilizing the centrally computed energy references. The fine-grained scheduling at each station considers charging dynamics and departure uncertainty scenarios through a detailed temporal resolution. Empirical validation demonstrates that the system achieves efficient minute-scale charging optimization for large EV fleets while maintaining near-optimal revenue performance when key parameters, including urgency factors, temporal granularity, and discount rates, are properly calibrated. _User Behavior Analysis._ [PERSON] et al. [6] evaluated profit maximization strategies for parking lot operators while minimizing costs for EV owners. Their analysis demonstrates that intelligent parking lot (IPL) design must account for real-world constraints and the stochastic nature of user behavior. The proposed solutions include implementing incentive mechanisms for partial charging scenarios, which enable increased charging throughput while reducing peak-hour grid demand by load shifting to off-peak periods. [PERSON] et al. [25] conducted a review of managed EV charging implementations in the United States, analyzing deployment costs and reported cost-benefit metrics. Their assessment identified key technical and operational gaps in EV-grid integration, highlighting the value of controlled charging strategies. [PERSON] et al. [26] developed an uncertainty quantification framework for personalized EV charging predictions using probabilistic modeling. Their analysis, which was conducted on both real-world EV journey data and a semi-synthetic mobility dataset, demonstrated effective error control even under high-variance conditions typical of real charging scenarios. The framework's ability to provide individualized uncertainty bounds can be important for user-centric smart charging applications. [PERSON] et al. [27] conducted an empirical analysis of EV charging patterns in Changshu City, China, developing an integrated database to examine charging behavior. Their study analyzed charging initiation timing, state-of-charge transitions, and session durations across charging platforms. Through cluster analysis, they identified distinct behavioral patterns between plug-in hybrid and battery-electric vehicle users, with battery-electric vehicles exhibiting five clusters and plug-in hybrids showing four primary patterns. The analysis revealed varying manifestations of range anxiety across both user groups, including pre-trip complete charging, destination charging, and partial charging strategies. Notable differences emerged in high-range anxiety scenarios, with plug-in hybrid users favoring complete charging while battery electric vehicle users demonstrated route-specific complete charging behavior. The study also identified a significant preference for overnight complete charging among battery-electric vehicle users. _Grid Integration._ [PERSON] et al. [28] proposed a multi-agent simulation architecture for commercial parking facilities that captures individual EV behavior through autonomous agents. Their framework implements two primary agent types: a power distribution center agent that coordinates solar, storage, and grid resources and a scheduling agent that optimizes charging patterns considering stochastic departure times.