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International Journal of Science, Strategic Management and Technology

An International, Peer-Reviewed, Open Access Scholarly Journal Indexed in recognized academic databases · DOI via Crossref The journal adheres to established scholarly publishing, peer-review, and research ethics guidelines set by the UGC

ISSN: 3108-1762 (Online)
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SMART EV CHARGING INFRASTRUCTURE USING IOT AND AI FOR INTELLIGENT ENERGY MANAGEMENT AND GRID OPTIMIZATION

AUTHORS:
Bachali Poojitha
Budige Anush
Mentor
P Kalpana Reddy
Affiliation
Department Of ECE, SVS Group of Institutions, Hanmakonda, Telangana
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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.
Keywords
Electric Vehicle (EV) Smart Charging Infrastructure Internet of Things (IoT) Artificial Intelligence (AI) Renewable Energy Integration Battery Energy Storage System (BESS) Smart Grid Cloud Computing Energy Management Load Balancing.
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Poojitha, B. & Anush, B. (2026). Smart EV Charging Infrastructure Using IOT and AI for Intelligent Energy Management and Grid Optimization. International Journal of Science, Strategic Management and Technology, 02(7), 1-9. https://doi.org/10.55041/ijsmt.v2i7.087

Poojitha, Bachali, and Budige Anush. "Smart EV Charging Infrastructure Using IOT and AI for Intelligent Energy Management and Grid Optimization." International Journal of Science, Strategic Management and Technology, vol. 02, no. 7, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijsmt.v2i7.087.

Poojitha, Bachali, and Budige Anush. "Smart EV Charging Infrastructure Using IOT and AI for Intelligent Energy Management and Grid Optimization." International Journal of Science, Strategic Management and Technology 02, no. 7 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijsmt.v2i7.087.

References

  1. Singh, A. R., Rathore, R. S., Jiang, W., Thakare, A., Kumar, R. S., Khadse, C. B., & Addis, H. K. (2025). A scalable cloud-integrated AI platform for real-time optimization of EV charging and resilient microgrid energy management. Scientific Reports15(1), 37692.

  2. Mohan, A., Pal, K., & Meena, H. K. (2026). Comprehensive review of electric vehicle charging infrastructure: Technologies, standards, and smart energy management. Future Batteries10, 100177.

  3. Singh, A. R., Kumar, R. S., Madhavi, K. R., Alsaif, F., Bajaj, M., & Zaitsev, I. (2024). Optimizing demand response and load balancing in smart EV charging networks using AI integrated blockchain framework. Scientific Reports14(1), 31768.

  4. Ramkumar, G., Durairajan, S., Rajeshkumar, L., Sharavanan, M., Chandana, K., & Chandrasekar, P. (2024, September). AI-driven optimization of EV charging: enhancing efficiency and grid stability. In 2024 Asian Conference on Intelligent Technologies (ACOIT)(pp. 1-6). IEEE.

  5. Dubey, C., Singh, A. K., & Dwivedi, V. K. (2026). Intelligent EV Charging Infrastructure: A Review of AI Techniques, System Challenges, and Deployment Barriers. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 1-28.

  6. Sarker, M. T., Al Qwaid, M., Shern, S. J., & Ramasamy, G. (2025). AI-Driven optimization framework for smart EV charging systems integrated with solar PV and BESS in High-Density residential environments. World Electric Vehicle Journal16(7), 385.

  7. Bagyaveereswaran, V., Arun, S. L., Manimozhi, M., & Pandian, B. J. (2026). AI‐Based Smart Charging Infrastructures: Revolutionizing Electric Vehicle Integration. Smart Charging Infrastructures, 57-89.

  8. Bhupathi, H. P. (2022). Smart Charging Revolution: AI and ML Strategies for Efficient EV Battery Use. ESP Journal of Engineering & Technology Advancements.

  9. Narayanaswamy, H. K., Venkataswamy, S. B., Ramesh, A. B., & Patil, K. S. (2026). Optimizing and securing electric vehicle charging infrastructure for resilient energy systems using AI and quantum key empowered in smart grids. International Journal of System Assurance Engineering and Management17(3), 759-778.

  10. Kumar, T. S. (2026). AI-Optimized Smart Charging Infrastructure for Electric Vehicles in Urban Power Grids. National Journal of Intelligent Power Systems and Technology, 1-10.

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This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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