Scalable Accelerated Intelligent Charging Strategy Recommendation for Electric Vehicles Based on Deep Q-Networks

• Published in: International journal of advanced computer science & applications 2024, Vol.15 (1)
• Main Authors: Shen, Xianhao, Wu, Zhen, Zhang, Yexin, Niu, Shaohua
• Format: Article
• Language: English
• Subjects: Algorithms; Electric vehicle charging; Electric vehicles; Markov processes; Shortest-path problems
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2024.0150175

With the rapid development of electric vehicles, their charging strategies significantly impact the overall power grid. Solving the spatiotemporal scheduling problem of vehicle charging has become a hot research topic. This paper focuses on recommending suitable charging stations for electric vehicles and proposes a scalable accelerated intelligent charging strategy recommendation algorithm based on Deep Q-Networks (DQN). The strategy recommendation problem is formulated as a Markov decision process, where the continuous sequence of regional charging requests within a time slice is fed into the DQN network as the input state, enabling optimal charging strategy recommendations for each electric vehicle. The algorithm aims to maintain regional load balance while minimizing user waiting time. To enhance the algorithm's applicability, a scalable, accelerated charging strategy framework is further proposed, which incorporates information filtering and shared experience pool mechanisms to adapt to different expansion scenarios and expedite strategy iterations in new scenarios. Simulation results demonstrate that the proposed DQN-based strategy recommendation algorithm outperforms the shortest path-first strategy, and the scalable, accelerated charging strategy framework achieves a 64.3% improvement in iteration speed in new scenarios, which helps to reduce the cloud server load and saves overheads.