AI Based Optimisation of EV Charging in Smart Energy Systems

Abstract

Artificial intelligence plays a transformative role in optimizing electric vehicle charging within smart energy systems. By leveraging predictive analytics, machine learning, and dynamic scheduling, AI enables efficient demand forecasting, real time load balancing, and integration of renewable energy sources. This reduces peak load stress, enhances grid reliability, and minimizes charging costs for consumers. Furthermore, AI driven optimization supports sustainable energy management by aligning EV charging patterns with renewable generation, thereby reducing carbon emissions. The approach fosters eco friendly transportation, cost effective infrastructure, and intelligent energy distribution, contributing to the advancement of smart cities and the global transition toward cleaner mobility solutions.

Introduction

Electric vehicles (EVs) are increasingly creating challenges for power grids due to rising charging demand, peak load stress, and inefficient energy distribution. Traditional charging systems depend heavily on the grid and lack intelligence, leading to higher costs and poor integration of renewable energy sources. To address these issues, the study proposes the use of artificial intelligence (AI) techniques such as machine learning, predictive analytics, and reinforcement learning to optimize EV charging.

The literature review shows that various AI and optimization methods (e.g., PSO, genetic algorithms, deep reinforcement learning, and IoT-based smart systems) are effective in improving load balancing, demand forecasting, and renewable energy integration. However, challenges like user behavior uncertainty, scalability, and grid instability still remain.

The proposed methodology involves collecting real-time data from EVs, charging stations, grid sensors, and renewable sources, then using AI models to predict demand and optimize charging schedules. A decision-making engine dynamically allocates charging loads while ensuring grid stability and aligning with renewable energy availability. The system is evaluated through simulations based on cost, efficiency, and reliability.

The flowchart describes a process where an EV connects to a charger, data is collected and analyzed, AI predicts demand, and an optimized charging plan is created. Load balancing ensures grid safety, followed by real-time monitoring and adaptive adjustments.

The system can be applied in parking areas, highways, rural solar stations, bus depots, emergency vehicles, and delivery fleets.

Conclusion

The study demonstrates that artificial intelligence can significantly enhance the efficiency and sustainability of electric vehicle charging within smart energy systems. By integrating predictive demand forecasting, dynamic scheduling, and load balancing, the framework reduces peak load stress, improves renewable energy utilization, and ensures grid stability. The results confirm that AI driven optimization not only lowers charging costs but also supports eco friendly mobility and smart city development.

References

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Copyright

Copyright © 2026 Pooja Chavan, Pooja Manur, Poorvi , Sindhu Manjunath, Naveen Kumar S. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.