Jul 2026· International Journal of Science, Strategic Management and Technology· 0 citations
TL;DR
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.
Abstract
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.
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.
Andrey Ershov, Alexey Lyapunov· International Journal of Mod...· 0 citations
The increasing adoption of electric vehicles (EVs) necessitates the development of efficient, sustainable, and intelligent charging infrastructure. This paper presents a smart solar-assisted Internet of Things (IoT)-based EV charging system that integrates photovoltaic (PV) power generation, battery energy storage, intelligent energy management, and real-time cloud monitoring. The proposed system utilizes solar energy as the primary power source, while the utility grid serves as a backup during periods of insufficient solar generation. An ESP32-based microcontroller continuously monitors key charging parameters, including battery voltage, charging current, power, temperature, and battery status, and uploads the measured data to an IoT cloud platform for remote monitoring and analysis. The developed prototype was experimentally validated using a 60 V, 8 Ah electric scooter battery under both AC and DC charging modes. Experimental results demonstrate that the AC charger (60 V, 5 A) fully charges the battery from 55 V to 60 V in approximately 110 minutes, whereas the proposed DC fast charger (60 V, 15 A) completes the charging process in 60 minutes, resulting in a 45.5% reduction in charging time. The intelligent energy management system automatically prioritizes solar power, performs seamless source switching between solar, battery, and grid supplies, and ensures uninterrupted charging operation. The proposed system significantly improves charging efficiency, reduces dependence on the conventional power grid, enhances renewable energy utilization, and provides a cost-effective, scalable, and environmentally sustainable solution for residential, commercial, and institutional EV charging applications.
Sathyanarayana P, S. Bhat, Shaileshwari S et al.· International Journal of Sci...· 0 citations
ABSTRACT Ultra-fast electric vehicle (EV) charging stations operating at power levels above 350 kW introduce critical challenges related to grid peak demand, high operating cost, renewable intermittency, and battery stress. This paper presents a machine learning-enabled energy management system for a hybrid renewable-powered ultra-fast charging station integrating photovoltaic generation, battery energy storage system, dispatchable auxiliary sources, and grid supply. The proposed EMS operates at a supervisory level and coordinates energy flows under stochastic EV charging demand, time-varying electricity tariffs (₹4–₹10/kWh), and uncertain renewable generation. A learning-based decision framework is developed using a reinforcement learning policy trained over 150 episodes, incorporating renewable and EV demand forecasts with ±10% uncertainty. The EMS performs multi-objective optimization by minimizing grid energy cost and peak power demand while achieving a balanced trade-off between renewable energy utilization, grid stability, and economic performance, and maintaining battery state-of-charge within safe operating limits (0.2–0.9). Simulation results over a 24-hour operating horizon demonstrate that the proposed ML-EMS achieves a 20–35% reduction in total grid energy cost, 25–40% peak grid power reduction, and achieves a balanced trade-off between renewable utilization, grid stability, and economic performance compared to a conventional rule-based EMS. The results validate the effectiveness of machine learning-driven energy management for reliable, grid-friendly, and cost-efficient operation of next- generation ultra-fast EV charging infrastructure.
K. Reddy, Mallapu Vijaya Kumar· Matéria· 0 citations
Nowadays, EV vehicles are increasing day by day. In the present day, charging stations rely on grid electricity, which results in increased carbon emissions and higher energy costs. To overcome this problem, this paper introduces an EcoPulse EV charging system using IoT, BMS, and an automatic surplus energy system. This framework integrates solar, wind, and the grid to ensure continuous and optimized EV charging. The IoT-based monitoring system will monitor the charging status. The BMS will protect the battery from overcharging, overheating, and voltage imbalance, improving battery life and safety. The GSM will provide instant notifications regarding the charging process. Once the EV battery is fully charged, the remaining charge will be transferred to a nearby home instead of that electricity being wasted. This framework will improve renewable energy utilization and reduce dependence on the grid, keeping electricity costs low. It is eco-friendly and reliable for future electric vehicle charging.
Keywords – electric vehicles, IoT, BMS, GSM, surplus energy.
A. T., B. Kumar, Manoj Ks et al.· International Journal of Cre...· 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
The proposed intelligent energy harvesting framework provides an efficient and sustainable power solution for next-generation Internet of Things (IoT) devices by integrating multi-source ambient energy harvesting, Maximum Power Point Tracking (MPPT), hybrid energy storage, and machine learning-based energy management. The framework effectively harvests energy from solar, thermal, radio frequency (RF), vibration, and wind sources while optimizing power utilization through adaptive energy prediction and intelligent task scheduling. Experimental evaluation demonstrates that the proposed system achieves higher energy utilization, lower power consumption, improved communication reliability, and extended operational lifetime compared with conventional battery-powered IoT systems. Furthermore, the integration of cloud and edge computing enables real-time monitoring, predictive analytics, and scalable deployment across diverse IoT applications. Overall, the proposed framework offers a reliable, cost-effective, and environmentally sustainable solution for smart cities, healthcare, industrial automation, environmental monitoring, and precision agriculture, while providing a strong foundation for future research on AI-driven energy optimization and next-generation wireless-enabled self-powered IoT networks.
B. Vaishnavi, K. Siddhartha, B. Ramprasad· International Journal of Cre...· 0 citations
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