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Deep Reinforcement Learning Based Optimal Route and Charging Station Selection

Author

Listed:
  • Ki-Beom Lee

    (Division of Electronic and Information, Department of Computer Engineering, Jeonbuk National University, Jeonju 54896, Korea)

  • Mohamed A. Ahmed

    (Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
    Department of Communications and Electronics, Higher Institute of Engineering & Technology–King Marriott, Alexandria 23713, Egypt)

  • Dong-Ki Kang

    (Division of Electronic and Information, Department of Computer Engineering, Jeonbuk National University, Jeonju 54896, Korea)

  • Young-Chon Kim

    (Division of Electronic and Information, Department of Computer Engineering, Jeonbuk National University, Jeonju 54896, Korea)

Abstract

This paper proposes an optimal route and charging station selection (RCS) algorithm based on model-free deep reinforcement learning (DRL) to overcome the uncertainty issues of the traffic conditions and dynamic arrival charging requests. The proposed DRL based RCS algorithm aims to minimize the total travel time of electric vehicles (EV) charging requests from origin to destination using the selection of the optimal route and charging station considering dynamically changing traffic conditions and unknown future requests. In this paper, we formulate this RCS problem as a Markov decision process model with unknown transition probability. A Deep Q network has been adopted with function approximation to find the optimal electric vehicle charging station (EVCS) selection policy. To obtain the feature states for each EVCS, we define the traffic preprocess module, charging preprocess module and feature extract module. The proposed DRL based RCS algorithm is compared with conventional strategies such as minimum distance, minimum travel time, and minimum waiting time. The performance is evaluated in terms of travel time, waiting time, charging time, driving time, and distance under the various distributions and number of EV charging requests.

Suggested Citation

  • Ki-Beom Lee & Mohamed A. Ahmed & Dong-Ki Kang & Young-Chon Kim, 2020. "Deep Reinforcement Learning Based Optimal Route and Charging Station Selection," Energies, MDPI, vol. 13(23), pages 1-22, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6255-:d:452210
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    References listed on IDEAS

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    Cited by:

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    3. Ahmed M. Abed & Ali AlArjani, 2022. "The Neural Network Classifier Works Efficiently on Searching in DQN Using the Autonomous Internet of Things Hybridized by the Metaheuristic Techniques to Reduce the EVs’ Service Scheduling Time," Energies, MDPI, vol. 15(19), pages 1-25, September.
    4. Ruisheng Wang & Zhong Chen & Qiang Xing & Ziqi Zhang & Tian Zhang, 2022. "A Modified Rainbow-Based Deep Reinforcement Learning Method for Optimal Scheduling of Charging Station," Sustainability, MDPI, vol. 14(3), pages 1-14, February.

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