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Deep Reinforcement Learning Based Optimal Route and Charging Station Selection
Content Provider | MDPI |
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Author | Lee, Ki-Beom Ahmed, Mohamed A. Kang, Dong-Ki Kim, Young-Chon |
Copyright Year | 2020 |
Description | 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. |
Starting Page | 6255 |
e-ISSN | 19961073 |
DOI | 10.3390/en13236255 |
Journal | Energies |
Issue Number | 23 |
Volume Number | 13 |
Language | English |
Publisher | MDPI |
Publisher Date | 2020-11-27 |
Access Restriction | Open |
Subject Keyword | Energies Transportation Science and Technology Electric Vehicle Electric Vehicle Charging Station Intelligent Transport System Electric Vehicle Charging Navigation System Markov Decision Process Deep Reinforcement Learning |
Content Type | Text |
Resource Type | Article |