Edge Computing-Integrated IoT Architecture for Intelligent EV Charging Coordination

Mushtaq Talib  Khudhur

Authors

Mushtaq Talib Khudhur Ministry of Education, General Directorate of Education in Dhi-Qar

Keywords:

Edge Computing, IoT, EV Charging, Smart Grid, Energy Efficiency, Peak Load Reduction, Real-Time Response

Abstract

The fast rate of adoption of electric vehicles (EVs) is exerting a growing stress on charging infrastructure. The conventional cloud-based designs have problems of latency and inability to handle real-time peak demand. Conversely, the combination of Edge Computing and Internet of Things (IoT) becomes one of the potential solutions to attain a more responsive and more efficient use of energy. The purpose of the research is to develop a combined system that will make smart charging systems more responsive, efficient in energy consumption, and minimized peak loads, which will enhance grid stability and sustainability. A hybrid testbed was created and it comprises of MATLAB/Simulink simulation, Python data analytics and Hardware-in-the-Loop. Actual operational data of EV charging stations were used. This experiment compared the legacy cloud-based model and the proposed Edge–IoT framework based on key performance indicators of response time, energy consumption and peak loads. The proposed system should be able to reduce response time by 65 percent, as well as energy consumption by 20 percent, and peak loads by 22 percent. These results confirm that Edge IoT integration is one of the feasible measures toward creating more effective, reliable and scalable EV charging infrastructures.

References

A. Yıldırım, O. Erdinç, B. Bakır, and J. P. S. Catalão, “Comprehensive Review of Edge Computing for Power Systems: State of the Art, Architecture, and Applications,” Applied Sciences, vol. 15, no. 8, p. 4592, 2025. https://www.mdpi.com/2076-3417/15/8/4592

M. Gozuoglu and H. Dogan, “Edge-Intelligent Electric Vehicle Charging Coordination for Grid Load Balancing and Renewable Integration,” ResearchGate Preprint, Jun. 2025.

https://www.researchgate.net/publication/393001341_Edge-Intelligent_Electric_Vehicle_Charging_Coordination_for_Grid_Load_Balancing_and_Renewable_Integration

F. Alasali, “Innovative investigation of the resilience of EV charging infrastructure using a real-time co-simulation testbed,” IET Cyber-Physical Systems: Theory & Applications, 2025. IET Research Journal.

M. Alsharif, R. Nordin, and M. Ismail, “Internet of Things for Smart Electric Vehicle Charging: A Comprehensive Review,” IEEE Access, vol. 11, pp. 66754–66772, 2023.

J. Wu, H. Zhang, and X. Hu, “IoT-Enabled Vehicle-to-Grid Systems: Opportunities, Challenges, and Applications,” Energies, vol. 16, no. 7, p. 3124, 2023.

P. Siano and G. Mokryani, “Cloud-Based Architectures for Smart Grids and EV Charging Management: Current Status and Future Directions,” Sustainable Energy, Grids and Networks, vol. 34, p. 101101, 2023.

S. Yıldız and A. Küçük, “Edge Computing for EV Charging Infrastructure: Reducing Latency and Improving Reliability,” IEEE Transactions on Smart Grid, vol. 15, no. 2, pp. 1203–1214, 2024.

H. Wang and Z. Li, “Theoretical Models for Load Management in EV Charging Networks: A Review,” Journal of Cleaner Production, vol. 393, p. 136357, 2023.

C. Wu, “Edge computing-based optimal dispatching of charging loads in dynamic distribution systems,” IET Renewable Power Generation, 2024.

L. Xu, Y. Li, and Z. Zhou, “Edge–Fog Hybrid Framework for Real-Time Optimization in Autonomous Electric Vehicle Networks,” ResearchGate Preprint, 2025.

F. Ahamed, M. Ali, and R. Islam, “Real-Time Simulation and Hardware-in-the-Loop Testing for EV Charging Systems Using OPAL-RT and Raspberry Pi,” ResearchGate Preprint, 2024.

A. Anane, H. Ltaief, and Y. Al-Mashhadany, “Hardware-in-the-Loop Simulation for Edge Computing in Smart EV Grids,” Frontiers in Energy Research, vol. 12, no. 3, 2024.

X. Fang, Y. Xue, and H. Liang, “Data-Driven Approaches for Electric Vehicle Charging Behavior Analysis,” IEEE Transactions on Transportation Electrification, vol. 9, no. 1, pp. 22–35, 2023.

G. Hao, “A Novel Hybrid Adaptive Strategy for Real-Time Dispatch Coordination among Renewable Energy, Storage, and Dynamic Grid Pricing,” IET Renewable Power Generation, 2025.

Y. Zhao, X. Li, W. Zhang, and K. Zhang, "Cloud computing-based EV charging management: Challenges and opportunities," IEEE Access, vol. 9, pp. 122331–122342, 2021.

K. Shafique, B. A. Khawaja, F. Sabir, S. Qazi, and M. Mustaqim, "Internet of things (IoT) for next-generation smart systems: A review of current challenges, future trends and prospects for emerging 5G-IoT scenarios," IEEE Access, vol. 8, pp. 23022–23040, 2020.

A. H. Sodhro, Z. Luo, A. K. Sangaiah, and S. W. Baik, "Mobile edge computing based QoS optimization in sustainable IoT," Sustainable Cities and Society, vol. 39, pp. 777–786, 2018.

S. Hussain, S. A. Malik, and N. Javaid, "Demand side management for smart grid using reinforcement learning and edge computing," IEEE Transactions on Industrial Informatics, vol. 18, no. 7, pp. 4952–4962, 2022.