Deep Reinforcement Learning Based Optimal Route and Charging Station Selection

Ki-Beom Lee, Mohamed A. Ahmed, Dong-Ki Kang and Young-Chon Kim
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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
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

Energies, 2020, vol. 13, issue 23, 1-22

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.

Keywords: electric vehicle; electric vehicle charging station; intelligent transport system; electric vehicle charging navigation system; Markov decision process; deep reinforcement learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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