Abstract
With the technical advancement of transportation electrification and Internet of vehicle, an increasing number of electric vehicles (EVs) and related infrastructures (e.g., service stations with both charging and communication services) are deployed in the intelligent highway systems. Not only can EVs enter the service station areas for charging, but they can also upload/download cached data at service stations to access multiple networking services. However, as EVs are operated individually with their unique travelling patterns, questions arise as how to incent EVs so that both energy and communication resources are optimally allocated. In this paper, we propose an online pricing mechanism of EV charging and data caching for service stations along the highway. First, we design an online reservation system at each EV to decide the best service station to park when the EV enters the highway. Furthermore, based on the variant power system status, an online pricing mechanism is devised to update the charging and caching price based on Q-learning, by which EVs can be motivated to arrive at the designated station for services. Finally, simulation results validate the effectiveness of the proposed scheme in improving the station's utility.
| Original language | English |
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| Title of host publication | Proceedings - 2020 16th International Conference on Mobility, Sensing and Networking, MSN 2020 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 81-85 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781728199160 |
| DOIs | |
| Publication status | Published - Dec 2020 |
| Externally published | Yes |
| Event | 16th International Conference on Mobility, Sensing and Networking, MSN 2020 - Tokyo, Japan Duration: 17 Dec 2020 → 19 Dec 2020 |
Publication series
| Name | Proceedings - 2020 16th International Conference on Mobility, Sensing and Networking, MSN 2020 |
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Conference
| Conference | 16th International Conference on Mobility, Sensing and Networking, MSN 2020 |
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| Country/Territory | Japan |
| City | Tokyo |
| Period | 17/12/20 → 19/12/20 |
Bibliographical note
Publisher Copyright:© 2020 IEEE.
Keywords
- Edge computing
- Energy regulation
- Online pricing
- Q-learning
- Smart grid