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A GRL for rapid charging stations


Xu, P., Zhang, J., Gao, T., Chen, S., Wang, X., Jiang, H., & Gao, W. (2022). Real-time fast charging station recommendation for electric vehicles in coupled power-transportation networks: A graph reinforcement learning method. International Journal of Electrical Power & Energy Systems141, 108030.


Fast charging requirements are a major factor affecting power-transportation networks as electric vehicle usage expands. This research develops a multi-objective system-level recommendation mechanism dynamically assigns cars to appropriate stations in order to address this. This is presented as a deep reinforcement learning sequential decision-making task. Graph attention networks integrate information from power grid buses, traffic nodes, and charging stations to control system states. A DQN(𝜆) training method that double-prioritizes tasks increases efficiency while dealing with lengthy delays. This approach improves the viability and resilience of urban systems by efficiently managing real-time requests, as demonstrated by testing it on a power-transportation simulation platform.

Key issues of other papers highlighted in this paper include:

  • Increased EV charging strains power grids, threatening stability.
  • Traffic congestion rises due to extended charging times and station queues.
  • Studies propose pricing, dynamic charging, and route optimization.
  • More efficient solutions are needed to manage EV proliferation.
  • Current algorithms face challenges in meeting real-time demands.
  • Coordinating EV charging with power grids and transport adds complexity.


These problems led the author of EV strategies that led author to propose Graph Reinforcement Learning (GRL) in which DQN(𝜆) uses a double-prioritized training approach to control action delays, whereas Graph Attention Networks (GATs) use a graph formulation based on physical connections.

Author proposes the  multi-objective fast charging station recommendation problem as the  Dijkstra algorithm and the objective function serve as the foundation for the construction of the objective-oriented high-level characteristics and the goal-originated incentives, which propel the agent’s self-evolving


In time-varying stochastic coupled systems, the research proposes employing a deep graph reinforcement learning (GRL) methodology for rapid charging station selection. This aims to enhance awareness of multi-dimensional states, where charging stations act as mediators between traffic networks and power grids. This is facilitated by a Graph Neural Network (GNN) based on the adjacency matrix and node properties of Graph Attention Networks (GAT). A groundbreaking off-policy technique integrates experience replay with 𝜆-return. Prioritized Replay utilizes large-scale replay memory to store and refresh precomputed 𝜆-returns, while 𝜆-return effectively handles credit assignment for delayed action execution. In contrast, the Attention-Prioritized Cache enhances training effectiveness by preserving the percentage of real suggestion transitions. This helps avoid making poor judgments during both deployment and training phases.

Framework of GRL Adapted from “Real-time fast charging station recommendation for electric vehicles in coupled power-transportation networks” by Xu et al. (2022).


  • The paper proposes a GRL strategy for fast charging station suggestion.
  • GAN combined with graph construction handles irregular environment characteristics.
  • DQN (𝜆) training mechanism enhances recommendation effectiveness.
  • The approach reduces user time costs, maintains traffic conditions, and prevents voltage variations.
  • It balances service among fast charging stations.
  • Outperforms opportunistic methods in dynamic recommendation systems.
  • Further refinement is needed for practical applicability.
  • Adjustments required for handling concurrent requests and large-scale systems.

Sakthivel R

I am a First-year M.Sc., AIML student enrolled at SASTRA University

I possess a high level of proficiency in a variety of programming languages and frameworks including Python. I have experience with cloud and database technologies, including SQL, Excel, Pandas, Scikit, TensorFlow, Git, and Power BI

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