2025· International Journal of Modern Research in Science & Engineering· Vol 8, pp. 01-16· 0 citations
TL;DR
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 is proposed.
Abstract
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.
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 is proposed.
Bachali Poojitha, Budige Anush, P. K. Reddy· International Journal of Sci...· 0 citations
The growing adoption of electric vehicles (EVs) necessitates intelligent charging strategies to alleviate grid congestion and control rising operational costs. This study introduces an IoT-enabled centralized energy management framework for a PV-BESS-EV integrated smart parking system, leveraging real-time data on carbon emissions, grid pricing, and solar irradiance. A key innovation is its bi-objective optimization model, which simultaneously minimizes both cost and carbon footprint, setting it apart from traditional single-objective approaches. The study evaluates teaching-learning-based optimization (TLBO) and particle swarm optimization (PSO) for addressing the system’s nonlinear challenges. Results indicate that TLBO offers faster convergence and greater robustness, leading to improved load flattening, enhanced PV utilization, and stable battery energy storage system (BESS) state of charge (SoC). Overall, the framework provides a scalable solution that effectively balances economic and environmental objectives for modern grid-integrated EV charging systems.
O. K. Rajesh, N. Shanmugasundaram, V. Rajendran· International Journal of Pow...· 0 citations
Electric vehicle (EV) charging scheduling integrated with photovoltaic (PV)-based charging stations is an important aspect of smart energy management. This paper presents an optimal EV charging scheduling framework considering solar-powered charging infrastructure and bidirectional vehicle-to-grid (V2G) and grid-to-vehicle (G2V) power transfer. A hybrid weighted ebola vector algorithm is proposed to improve charging coordination and minimize operational cost. In addition, photovoltaic power generation is forecasted using an artificial neural network (ANN) for accurate 24-hour solar energy prediction. The objective function considers peak shaving, valley filling, power loss, charging coordination, and charging cost minimization under varying load conditions. The proposed method is evaluated using a 24-hour load duration curve with forecasted PV power and power loss is compared with the Ebola Optimization Search Algorithm and Weighted Mean Vector Optimization (WMVO) technique under Normal, High EV plug-in, Low Solar power availability, and Peak Hour scenarios. Simulation results demonstrate that the proposed algorithm achieves superior performance in convergence speed, scheduling accuracy, and power loss reduction. The proposed method reduces charging cost by 28%, whereas the Ebola and WMVO methods achieve reductions of 17.7% and 8.8%, respectively. The results confirm the effectiveness of the proposed optimization approach for smart EV charging management with renewable energy integration.
N. Rao, Shelly Vadhera, S. Singh· Engineering and Technology H...· 0 citations
The rapid transition toward electric vehicles (EVs) in Saudi Arabia requires reliable and sustainable charging infrastructure capable of supporting long-distance highway transportation. However, the deployment of emergency charging systems is challenged by sparse charging infrastructure, stochastic emergency charging demand, battery degradation under harsh climatic conditions, and the need for cost-effective integration of renewable energy resources. This study presents a three-stage unified techno-economic planning framework for renewable-assisted emergency EV charging networks that integrates strategically located charging hubs with a coordinated fleet of solar-assisted Mobile Emergency Charging Vehicles (MECVs). The proposed framework jointly optimizes charging hub locations, photovoltaic (PV) generation capacity, battery energy storage system (BESS) sizing, and MECV allocation while explicitly accounting for stochastic emergency charging demand, renewable-energy utilization, and temperature-dependent battery degradation. Emergency charging demand is modeled using Monte Carlo simulation based on EV penetration scenarios, and battery aging is incorporated into the optimization through a temperature-dependent degradation model. The planning problem is formulated as a Mixed Integer Nonlinear Programming (MINLP) model and comparatively solved using three independent metaheuristic algorithms, namely the Musical Chairs Algorithm (MCA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO). The proposed framework is evaluated using two representative highway corridors in Saudi Arabia. The results indicate that renewable-assisted charging can reduce annual grid-related CO2 emissions by approximately 25,360 and 57,641 t CO2/year for the Riyadh–Dammam and Riyadh–Makkah corridors, respectively. Battery degradation contributes approximately 12.7–14.2% of the total annualized system cost, highlighting the importance of incorporating lifecycle degradation into infrastructure planning. Temperature sensitivity analysis further indicates the significant influence of harsh climatic conditions on battery lifetime, renewable-energy utilization, and overall system economics. The proposed framework provides a practical planning methodology for developing reliable, sustainable, and economically viable emergency EV charging infrastructure in regions with similar geographical and climatic characteristics.
A. Eltamaly, Majed A. Alotaibi· Sustainability· 0 citations
The increased charging demand of electric vehicles (EVs) and fuel cell EVs has created an additional burden on the fossil fuel‐based electric grid. The article proposes a hybrid energy system (HES)‐based integrated EV charging and hydrogen refueling stations with mobility‐assisted storage flexibility, that is, a mobile charging unit (MCU). As the system has multiple sources and services, a priority‐based energy management framework is designed to maximize use of renewable energy sources (RES). To achieve techno‐economic benefits, a lexicographic optimization with slack‐based demand modeling is introduced to coordinate renewable generation, hydrogen production, grid interaction, and MCU scheduling under realistic operational constraints. Results demonstrate that the proposed system significantly improves RES utilization up to 95% and reduces grid dependency up to 86.53%. Multiobjective optimization enhances economic performance by 19.33% and customer satisfaction by 95% in all seasons. The proposed system provides a significant annual profitability and sustainability for multienergy charging infrastructure.
Kiran Nathgosavi, V. Kalkhambkar, Pratyasa Bhui· Energy Storage· 0 citations
Electric Vehicles are thought to be among the best options for lowering gas emissions and oil consumption. EV users can from a charging station with a well-thought-out schedule and price plan. Advanced energy management techniques are required to guarantee the sustainable, dependable, and effective operation of charging infrastructure due to the quick rise in the usage regarding electric vehicle (EV). In light of recent research, current design restrictions as well as the erratic conduct of EV consumers make classic scheduling techniques, such as set costs for Time-of-Use (ToU), insufficient. Uncoordinated charging results Voltage instability caused by transformer overloading, also wasteful utilization of electricity from renewable as EV use rises. AI is becoming more widely acknowledged in a crucial facilitator of scalable, durable, effective EV charging facilities with intelligence. This paper proposes a hybrid AI-based architecture that integrates real-time Traffic pattern, distance of EV, arrival and departure time of EV state of charge as input. Through real-time monitoring and charge optimization, the EVCS enable intelligent EV charging. The AI framework employs a non-uniform Poisson process in order to dynamically assess user demand also enhances schedule of charging. While the charging demand of electric vehicles (EVs) is intrinsically heterogeneous, decentralized, and stochastic, the intermittent and weather-dependent nature of PV power results in considerable output uncertainty. The purpose of the EV Charging Grid Optimization is to facilitate research on AI-driven energy management for EV charging infrastructure. The two proposed optimization algorithm improves the operational effectiveness of EVCS. Using threshold value Reinforcement Learning make the real-time decision that dynamically schedule the EV. Threshold value is determined from customer preferences. The proposed technology demonstrates scalability, durability, and cost-effectiveness and provides a feasible substitute for upcoming metropolitan EV charging system.
Jose Devaraj, Daphni Paulphin J.· International journal of com...· 0 citations
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