Demand-aware charger planning for electric vehicle sharing
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Detail(s)
| Original language | English |
|---|---|
| Title of host publication | KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
| Publisher | Association for Computing Machinery |
| Pages | 1330-1338 |
| ISBN (print) | 9781450355520 |
| Publication status | Published - 19 Jul 2018 |
| Externally published | Yes |
Publication series
| Name | Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
|---|
Conference
| Title | 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018 |
|---|---|
| Place | United Kingdom |
| City | London |
| Period | 19 - 23 August 2018 |
Link(s)
Abstract
Cars of the future have been predicted as shared and electric. There has been a rapid growth in electric vehicle (EV) sharing services worldwide in recent years. For EV-sharing platforms to excel, it is essential for them to offer private charging infrastructure for exclusive use that meets the charging demand of their clients. Particularly, they need to plan not only the places to build charging stations, but also the amounts of chargers per station, to maximally satisfy the requirements on global charging coverage and local charging demand. Existing research efforts are either inapplicable for their different problem formulations or are at a coarse granularity. In this paper, we formulate the Electric Vehicle Charger Planning (EVCP) problem especially for EV-sharing. We prove that the EVCP problem is NP-hard, and design an approximation algorithm to solve the problem with a theoretical bound of 1 -e 1 . We also devise some optimization techniques to speed up the solution. Extensive experiments on real-world datasets validate the effectiveness and the efficiency of our proposed solutions. © 2018 Association for Computing Machinery.
Research Area(s)
- Electric Vehicles, Location Selection, Submodularity
Bibliographic Note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
Citation Format(s)
Demand-aware charger planning for electric vehicle sharing. / Du, Bowen; Tong, Yongxin; Zhou, Zimu et al.
KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2018. p. 1330-1338 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).
KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2018. p. 1330-1338 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
