# Intelligent Monitoring Systems for Electric Vehicle Charging
## 6 Concluding Remarks  
This study presents a detailed review of EV charging challenges and proposes an architectural framework for intelligent EV charging monitoring systems, addressing critical infrastructure management challenges. Our analysis highlights persistent operational inefficiencies stemming from prolonged station occupancy during slow charging (80-100% SOC) and post-charging overstays [6; 27]. These behaviors, influenced by range anxiety and incentive structures [25; 82], limit charging availability while existing solutions often lack adaptive optimization [3; 19; 24; 26].  
The proposed architecture integrates the following: (1) edge computing for localized decision-making [49; 64], (2) IoT sensor networks for high-resolution spatiotemporal monitoring [10; 48], and (3) cloud-based analytics for system-wide optimization [50; 66]. This security-conscious framework [21; 52] uses distributed machine learning at edge nodes and federated learning in the cloud [24; 29], enabling real-time responsiveness and scalability [9; 63]. The system goes beyond conventional parameter tracking by integrating behavioral analytics, correlating electrical signatures [19] with contextual factors, including temporal patterns, environmental conditions, and user preferences.  
Three critical research trajectories emerge: First, developing explainable AI with local interpretability [29; 50] to clarify relationships between battery parameters and charging behaviors [6; 27]. Second, implementing causal inference to disentangle mechanisms governing charging patterns [24; 26]. Third, integrating physics-informed neural networks with reinforcement learning [7; 32] to optimize charging, balancing immediate demands with long-term infrastructure preservation. Complementary extensions could address privacy-preserving computation [53; 73], battery degradation models [17; 31], and quantum machine learning.  
System validation requires empirical evaluation in high-density urban environments with complex spatiotemporal dynamics [7; 27]. Key metrics include sensor network robustness [19; 65], adaptive control performance in grid stabilization [28; 29], and protocol efficacy across heterogeneous ecosystems [17; 36; 40; 42]. While performance projections are currently based on analytical models, this framework provides a structured approach for addressing infrastructure-behavior-energy interdependencies in evolving EV landscapes.  
As transportation electrification accelerates, next-generation architectures must accommodate autonomous fleet charging and implement advanced privacy-preserving computation. By integrating technical innovation, socio-economic insights, and rigorous validation, monitoring systems will become active enablers of sustainable mobility.  
Conceptualization, J.A.M. and J.M.F.R.; Methodology, J.A.M. and J.M.F.R.; Investigation, J.A.M.; Writing--original draft preparation, J.A.M. and J.M.F.R.; Writing--review and editing, J.A.M. and J.M.F.R.; Visualization, J.A.M. and J.M.F.R.; Project administration, J.M.F.R.; Funding acquisition, J.M.F.R. All authors have read and agreed to the published version of the manuscript.  
This work was supported by national funds (FCT/MEC, Fundacao para a Ciencia e a Tecnologia/ Ministerio da Educacao e Ciencia) through projects UIDB/00631/2020 CEOT Base, UIDP/00631/2020 CEOT Programatico, and UID/04516/NOVA Laboratory for Computer Science and Informatics (NOVA LINCS). This study also received funding from Diametro Numerico Unipressoal, Lda. (Faro, Portugal). The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.  
The authors declare no conflicts of interest.