Edge-intelligent electric vehicle charging coordination for grid load balancing and renewable integration

Digital Twin-Assisted Optimization of Electric Vehicle Charging Infrastructure

The rapid adoption of electric vehicles (EVs) has increased the need for intelligent charging infrastructure capable of addressing challenges such as charging congestion, uneven energy distribution, grid instability, and long waiting times. Conventional charging management approaches based on static scheduling are inadequate for dynamic charging environments. This paper proposes a Digital Twin-Assisted Optimization Framework for Electric Vehicle Charging Infrastructure (DTO-EVCI) that integrates IoT, cloud computing, artificial intelligence (AI), machine learning, and optimization techniques to enable real-time monitoring, predictive analytics, and intelligent charging management. The framework synchronizes physical charging stations with a virtual digital twin, enabling accurate simulation, charging demand prediction, occupancy forecasting, optimized scheduling, predictive maintenance, and adaptive energy management. By improving charging efficiency, resource utilization, grid reliability, and renewable energy integration, the proposed framework reduces operational costs, minimizes charging delays, and supports sustainable large-scale EV deployment while contributing to smart city and carbon-neutral transportation initiatives.

Smart EV Charging Infrastructure Using IOT and AI for Intelligent Energy Management and Grid Optimization

The rapid adoption of electric vehicles (EVs) has created an urgent need for intelligent charging infrastructure capable of supporting increasing energy demand while maintaining grid stability and charging efficiency. Conventional EV charging stations often suffer from challenges such as unbalanced power distribution, long charging durations, inefficient energy management, and limited integration with renewable energy resources. This paper proposes a Smart EV Charging Infrastructure that integrates Internet of Things (IoT)-enabled sensing, cloud-based monitoring, renewable energy sources, battery energy storage, and artificial intelligence (AI)-based energy management to provide an efficient, reliable, and sustainable charging solution. The proposed framework continuously monitors charging parameters, battery state-of-charge, grid conditions, and renewable energy availability to optimize charging schedules using intelligent decision-making algorithms. A cloud platform enables real-time monitoring, remote control, predictive maintenance, and data analytics, while dynamic load balancing minimizes peak demand and improves power quality. Renewable energy integration reduces dependence on the utility grid and lowers carbon emissions, whereas battery storage enhances charging reliability during peak demand periods. The proposed system also supports secure communication between charging stations, EVs, and utility operators to facilitate smart grid interaction. Experimental evaluation demonstrates improvements in charging efficiency, energy utilization, grid load balancing, and operational reliability compared with conventional charging approaches. The proposed infrastructure provides a scalable, energy-efficient, and environmentally sustainable solution for future smart transportation ecosystems and intelligent energy management.

Sustainable Energy, Grids and Networks

Electric Vehicles Integration into the Power Grid Using HGAIPSO Optimization Algorithm

The gradual depletion of conventional fossil-fuel resources, growing environmental concerns, and the increasing complexity associated with modern smart-grid deployment have accelerated the integration of renewable energy distributed generators (REDGs) and electric vehicles (EVs) into electrical power systems. Over recent decades, the global EV industry has experienced considerable growth in both vehicle production and market penetration. This study investigates the ancillary services offered by EVs and evaluates the implications of their large-scale integration into power networks. An optimization-based framework is developed to estimate EV hosting capacity, formulate an enhanced objective function, determine the optimal locations and capacities of charging stations, and evaluate the associated system operating costs. Simulation findings demonstrate that the proposed EV capacity estimation approach effectively represents vehicle charging and discharging behavior. Furthermore, a hybrid genetic algorithm-improved particle swarm optimization (HGAIPSO) technique is introduced to optimize the allocation of EV charging stations while incorporating coordinated charging strategies and renewable distributed generation. The proposed framework mitigates voltage-limit violations, minimizes energy losses and system costs, and improves the overall power-quality performance of the network. The effectiveness of the developed approach is validated through MATLAB-based simulations conducted on the IEEE 118 bus test system.

Edge AI and IoT for smart sustainable transportation: Real-time renewable energy optimization – A comprehensive review

The adoption rate of electric mobility, renewable energy systems, and smart transportation infrastructures has exacerbated the demand for real-time, high-performance and energy-efficient systems. While high latency, low bandwidth, and low responsiveness are commonly encountered drawbacks in existing cloud-based energy optimization techniques used in these mobility-driven systems. The deployment of Edge AI and IoT technologies will pave the path to efficient, low-latency and real-time distributed renewable energy optimization within the smart sustainable transportation system. This paper presents a detailed survey on the latest trends in AI based renewable energy integration, smart grid, EV, battery management systems, and real-time transportation data analytics. The application of deep learning, reinforcement learning, federated learning, and predictive analytics toward improved renewable energy generation forecasting, smart charging, load balancing, and Vehicle-to-Grid (V2G) co-ordination is also elaborated. Edge computing platforms and IoT devices can support a high-performance real-time monitoring, predictive maintenance and a self-sufficient energy distribution network for smart transportation. The experimental validation on contemporary research works reveal an average 70% reduction in transmission latency, more than 50% increase in the renewable energy consumption, approximately 30% increase in battery lifetime, and nearly 80% reduction in charging infrastructure offline duration for edge systems with AI assistance. Important concerns regarding the edge system’s security, scale-ability, connectivity, privacy, and the direction of future research on enabling smart transportation were also discussed.

ML-Guided Coordinated EV Charging: LSTM Forecasting and Multi-Objective Optimization for Real-Time Grid Operation

In this paper, an integrated real-time framework for coordinated Electric Vehicle (EV) charging is proposed based on load forecasting and multi-objective optimization techniques. The framework integrates load forecasting using Long Short-Term Memory (LSTM) and multi-objective optimization to minimize peak load, charging cost, and grid stress while maintaining high user satisfaction under dynamic smart-grid conditions. The proposed framework combines multi-step LSTM forecasting with a convex optimization scheduler operating on a 15-min rolling horizon that incorporates feeder constraints, electricity tariffs, charger limits, and departure state-of-charge requirements. Unlike conventional charging strategies, the proposed method enables adaptive and grid-aware charging decision-making in real time. The framework is evaluated under various EV penetration scenarios ranging from 30 to 100 EVs and is compared with uncontrolled charging, off-peak charging, and load-balancing strategies. The results demonstrate an average 25% reduction in peak load, a 15–20% reduction in charging costs, smoother feeder operation, and a user satisfaction rate exceeding 95% in meeting charging requirements. Furthermore, the proposed framework improves operational stability, reduces computational burden, and enhances charging coordination compared with conventional forecasting and heuristic scheduling approaches. These findings demonstrate the feasibility and scalability of integrating Machine Learning (ML)-based forecasting with real-time optimization for future smart-grid and EV energy management systems.