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<p>The increasing adoption of Electric Vehicles (EVs) necessitates advanced charging infrastructure. This manuscript presents a novel approach that integrates Graph Convolutional Networks (GCNs) and the Deep Dyna Reinforcement Learning (D2RL) algorithm to optimize EV charging. GCNs are utilized to model charging stations as interconnected nodes, incorporating factors such as connectivity and distance. The D2RL algorithm effectively schedules charging by considering vehicle location, battery status, station availability, and electricity costs. Our approach demonstrates enhanced efficiency over traditional methods, achieving a high infrastructure utilization rate and substantial energy savings across various sample sizes. The proposed model maintains its effectiveness even at larger scales, showcasing its scalability and adaptability. Additionally, it improves critical performance metrics related to energy impact and demand response, contributing to more efficient EV charging while reducing energy costs and minimizing environmental impact.</p>

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Enhanced Electric Vehicle charging strategy through Graph Convolutional Networks integrated with deep reinforcement learning

  • 期刊论文

  • 发表日期：

    2025年2月6日

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  • Shilpa Ghode,
  • Mayuri Digalwar

摘要

The increasing adoption of Electric Vehicles (EVs) necessitates advanced charging infrastructure. This manuscript presents a novel approach that integrates Graph Convolutional Networks (GCNs) and the Deep Dyna Reinforcement Learning (D2RL) algorithm to optimize EV charging. GCNs are utilized to model charging stations as interconnected nodes, incorporating factors such as connectivity and distance. The D2RL algorithm effectively schedules charging by considering vehicle location, battery status, station availability, and electricity costs. Our approach demonstrates enhanced efficiency over traditional methods, achieving a high infrastructure utilization rate and substantial energy savings across various sample sizes. The proposed model maintains its effectiveness even at larger scales, showcasing its scalability and adaptability. Additionally, it improves critical performance metrics related to energy impact and demand response, contributing to more efficient EV charging while reducing energy costs and minimizing environmental impact.