Name
Abstract | ||
|---|---|---|
Electric vehicles (EV) are emerging as a promising transportation medium because they provide better energy efficiency and are more environmentally friendly compared to gasoline-fueled vehicles. However, long charge times and low charger coverage hinder EV charging services. To overcome these problems, this paper proposes a reinforcement learning (RL) based optimal management policy that can maximize the utilization of EV chargers while guaranteeing quality of service. The proposed RL based scheme learns the arrival pattern of EVs and adjusts the service area of each charging station in a dynamic environment. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3208903.3212057 | E-ENERGY'18: PROCEEDINGS OF THE 9TH ACM INTERNATIONAL CONFERENCE ON FUTURE ENERGY SYSTEMS |
Field | DocType | Citations |
Automotive engineering,Efficient energy use,Environmentally friendly,Computer science,Charging station,Quality of service,Optimal management,Reinforcement learning | Conference | 1 |
PageRank | References | Authors |
0.39 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiae Lee | 1 | 1 | 0.72 |
| Zhamila Issimova | 2 | 1 | 0.39 |
| Hyuk Lim | 3 | 161 | 11.83 |