Routing of Electric Vehicles With Intermediary Charging Stations: A Reinforcement Learning Approach.
| Autor: | Dorokhova M; Photovoltaics and Thin Film Electronics Laboratory (PV-Lab), Institute of Microengineering (IMT), École Polytechnique Fédérale de Lausanne (EPFL), Neuchâtel, Switzerland., Ballif C; Photovoltaics and Thin Film Electronics Laboratory (PV-Lab), Institute of Microengineering (IMT), École Polytechnique Fédérale de Lausanne (EPFL), Neuchâtel, Switzerland., Wyrsch N; Photovoltaics and Thin Film Electronics Laboratory (PV-Lab), Institute of Microengineering (IMT), École Polytechnique Fédérale de Lausanne (EPFL), Neuchâtel, Switzerland. |
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| Jazyk: | angličtina |
| Zdroj: | Frontiers in big data [Front Big Data] 2021 May 26; Vol. 4, pp. 586481. Date of Electronic Publication: 2021 May 26 (Print Publication: 2021). |
| DOI: | 10.3389/fdata.2021.586481 |
| Abstrakt: | In the past few years, the importance of electric mobility has increased in response to growing concerns about climate change. However, limited cruising range and sparse charging infrastructure could restrain a massive deployment of electric vehicles (EVs). To mitigate the problem, the need for optimal route planning algorithms emerged. In this paper, we propose a mathematical formulation of the EV-specific routing problem in a graph-theoretical context, which incorporates the ability of EVs to recuperate energy. Furthermore, we consider a possibility to recharge on the way using intermediary charging stations. As a possible solution method, we present an off-policy model-free reinforcement learning approach that aims to generate energy feasible paths for EV from source to target. The algorithm was implemented and tested on a case study of a road network in Switzerland. The training procedure requires low computing and memory demands and is suitable for online applications. The results achieved demonstrate the algorithm's capability to take recharging decisions and produce desired energy feasible paths. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer BS declared a shared affiliation, with no collaboration, with one of the authors CB. (Copyright © 2021 Dorokhova, Ballif and Wyrsch.) |
| Databáze: | MEDLINE |
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