This research introduces a comprehensive AI-driven framework for secure and efficient load management in multi-station electric vehicle (EV) charging networks, responding to the increasing demand and operational difficulties associated with widespread EV adoption. The suggested architecture has three main parts: a Smart Load Balancer (SLB), an AI-driven intrusion detection system (AIDS), and a Real-Time Analytics Engine (RAE). These parts use advanced machine learning methods like Support Vector Machines (SVMs), autoencoders, and reinforcement learning (RL) to make the system more flexible, secure, and efficient. The framework uses federated learning (FL) to protect data privacy and make decisions in a decentralized way, which lowers the risks that come with centralizing data. The framework makes load distribution 23.5% more efficient, cuts average wait time by 17.8%, and predicts station-level demand with 94.2% accuracy, according to simulation results. The AI-based intrusion detection component has precision, recall, and F1-scores that are all over 97%, which is better than standard methods. The study also finds important gaps in the current literature and suggests new areas for research, such as using graph neural networks (GNNs) and quantum machine learning to make EV charging infrastructures even more scalable, resilient, and intelligent.

The rapid adoption of electric vehicles (EVs) has intensified the demand for intelligent, scalable, and interoperable charging infrastructure. Traditional EV charging networks based on the Open Charge Point Protocol (OCPP) face challenges related to dynamic load management, cybersecurity, and efficient integration with renewable energy sources. This paper presents a novel AI-driven framework that integrates federated learning, predictive analytics, and real-time control within OCPP-compliant networks to enhance performance and sustainability. The proposed system utilizes edge AI modules at charging stations, supported by a central aggregator that employs federated learning to preserve data privacy while enabling network-wide optimization. A case study involving simulated smart charging stations demonstrates significant improvements, including an 18% reduction in peak load demand, a 29% increase in forecasting accuracy (MAPE of 8.5%), a 10% decrease in average charging wait times, and a 12% increase in on-site solar energy utilization. The framework’s compatibility with OCPP and related standards (e.g., IEC 61851, ISO 15118) ensures ease of deployment on existing infrastructure. These results indicate that the proposed AI-OCPP integration provides a scalable and intelligent foundation for next-generation EV charging networks that align with the goals of sustainable transportation and smart grid evolution.

The rapid growth of electric-vehicle (EV) charging networks introduces coupled challenges in grid safety, tariff responsiveness, and protocol interoperability. This paper presents a real-world, tariff-aware reinforcement learning (RL) framework for grid-safe load management in Open Charge Point Protocol (OCPP) networks. The approach combines a projection-based safety layer with a tariff-sensitive policy that optimizes operating cost, peak demand, fairness, and tail delay under flat, time-of-use (ToU), and real-time pricing (RTP) regimes. Using operational OCPP logs from a Malaysian multi-site deployment, we instantiate a multi-scenario evaluation in which the proposed proximal policy optimization (PPO) controller reduces aggregate peak demand by about 25% versus proportional fairness and nearly 40% versus a rule-based baseline, while maintaining comparable or intentionally reduced energy throughput when required by cost–peak trade-offs, without compromising grid safety. Under ToU and RTP, the controller achieves cost reductions up to 30% without violating feeder or station caps; improvements in fairness (lower Gini index) and queueing performance (lower w95 delay) confirm that safety projection and tariff awareness jointly enhance equity and grid compliance. The methodology includes a formal projection operator that enforces hard constraints at dispatch, a multi-objective reward with tariff terms, and statistical validation based on site-level medians, confidence intervals, and Wilcoxon tests. To our knowledge, this is the first demonstration of a tariff-aware, safety-constrained RL controller validated on real OCPP data across multiple tariff scenarios, providing a practical path to scalable, economically adaptive, and grid-stable smart charging.

Electric vehicles (EVs) are changing how people use energy for transportation, and their growing adoption poses new challenges for safe and cost-effective charging operations. When multiple EVs charge simultaneously, aggregate demand can rise sharply, increasing the risk of grid overloads and high electricity costs. The Open Charge Point Protocol (OCPP) enables interoperability between charging stations and backend systems, but it does not provide built-in intelligence for forecasting or charging control. To address this gap, this paper presents a practical AI-driven OCPP framework that integrates short-term demand forecasting, tariff-aware reinforcement learning, and runtime safety enforcement into a unified control loop. The framework is evaluated using real OCPP logs and employs a tariff-aware, safety-constrained Proximal Policy Optimization (PPO) scheduler that issues charging setpoints while respecting feeder and connector limits at every control step through a projection-based safety layer. A lightweight forecasting module provides short-horizon operational support aligned with the 15-minute control resolution, without being treated as a standalone predictive contribution. The proposed approach is evaluated under multiple tariff regimes, including flat rate, time-of-use (ToU), and real-time pricing (RTP), and compared against proportional fairness (PF) and rule-based baselines. Results show that when configured with explicit energy-parity constraints, the PPO controller maintains near-baseline energy delivery while achieving substantial reductions in peak demand and total energy cost. Across evaluated scenarios, peak demand is reduced by up to 30% and energy cost by approximately 25–33%, without violating grid safety constraints. The controller also demonstrates stable behavior across different pricing and loading conditions without extensive manual tuning. Overall, this study demonstrates that combining established learning methods with rigorous runtime safety enforcement and real-world validation can yield deployable and reliable AI-based charging control within OCPP-operated infrastructure. The framework emphasizes practical integration and deployment realism rather than algorithmic novelty, providing a viable pathway toward safer, more efficient, and cost-aware EV charging networks.

Grounding systems are critical for ensuring electrical safety, fault current dissipation, and electromagnetic compatibility in power installations across different voltage levels. This research presents a comparative study on the optimization of grounding configurations for 400 V, 10 kV, and 35 kV electrical installations, focusing on key performance parameters such as grounding resistance, step and touch voltages, and fault current dissipation efficiency. The study employs computational simulations using the finite element method (FEM) alongside empirical field measurements to evaluate the influence of soil resistivity, electrode materials, and grounding configurations, including rod electrodes, grids, deep-driven rods, and hybrid grounding systems. Results indicate that soil resistivity significantly affects grounding efficiency, with deep-driven rods providing superior performance in high-resistivity conditions, while grounding grids demonstrate enhanced fault current dissipation in substations. The integration of conductive backfill materials, such as bentonite and conductive concrete, further reduces grounding resistance and enhances system reliability. This study provides engineering insights into optimizing grounding systems based on installation voltage levels, cost considerations, and compliance with IEEE Std 80-2013 and IEC 60364-5-54. The findings contribute to the development of more resilient and cost-effective grounding strategies for electrical installations.

Electric vehicle (EV) charging networks face major operational challenges, including demand uncertainty, peak-load congestion, and anomalous charging behavior, particularly in multi-station environments. This study proposes an anomaly-aware Q-learning framework for real-time scheduling in multi-station EV charging systems by integrating short-term load forecasting, anomaly detection, and intelligent scheduling within a unified operational pipeline. The framework combines Prophet, XGBoost, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models for short-term demand forecasting, while Convolutional Neural Networks (CNN), Autoencoders, and Isolation Forests are employed for anomaly detection. Forecasting and anomaly information are incorporated into a Q-learning scheduler to support adaptive charger allocation and congestion management. Evaluation using a four-year, real-world dataset comprising more than 2000 EV charging sessions demonstrates improved scheduling performance, achieving reductions in peak load and waiting time while improving energy delivery consistency. The framework further demonstrates low scheduling latency, supporting suitability for real-time deployment in OCPP-compliant smart charging infrastructures.