Demand-aware charger planning for electric vehicle sharing

Bowen Du; Yongxin Tong; Zimu Zhou; Qian Tao; Wenjun Zhou

doi:10.1145/3219819.3220032

Demand-aware charger planning for electric vehicle sharing

Bowen Du
, Yongxin Tong^*
, Zimu Zhou
, Qian Tao
, Wenjun Zhou

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

41 Scopus citations

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.

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
Number of pages	9
ISBN (Print)	9781450355520
DOIs	https://doi.org/10.1145/3219819.3220032
State	Published - 19 Jul 2018
Event	24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018 - London, United Kingdom Duration: 19 Aug 2018 → 23 Aug 2018

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference	24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018
Country/Territory	United Kingdom
City	London
Period	19/08/18 → 23/08/18

Keywords

Electric Vehicles
Location Selection
Submodularity

Access to Document

10.1145/3219819.3220032

Cite this

Du, B., Tong, Y., Zhou, Z., Tao, Q., & Zhou, W. (2018). Demand-aware charger planning for electric vehicle sharing. In KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1330-1338). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). Association for Computing Machinery . https://doi.org/10.1145/3219819.3220032

Du, Bowen ; Tong, Yongxin ; Zhou, Zimu et al. / Demand-aware charger planning for electric vehicle sharing. KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery , 2018. pp. 1330-1338 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

@inproceedings{6b58099c6e6b46daa31f746daa084f41,

title = "Demand-aware charger planning for electric vehicle sharing",

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.",

keywords = "Electric Vehicles, Location Selection, Submodularity",

author = "Bowen Du and Yongxin Tong and Zimu Zhou and Qian Tao and Wenjun Zhou",

note = "Publisher Copyright: {\textcopyright} 2018 Association for Computing Machinery.; 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018 ; Conference date: 19-08-2018 Through 23-08-2018",

year = "2018",

month = jul,

day = "19",

doi = "10.1145/3219819.3220032",

language = "英语",

isbn = "9781450355520",

series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",

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address = "美国",

}

Du, B , Tong, Y, Zhou, Z, Tao, Q & Zhou, W 2018, Demand-aware charger planning for electric vehicle sharing. in KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery , pp. 1330-1338, 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018, London, United Kingdom, 19/08/18. https://doi.org/10.1145/3219819.3220032

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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Tong, Yongxin

AU - Zhou, Zimu

AU - Tao, Qian

AU - Zhou, Wenjun

PY - 2018/7/19

Y1 - 2018/7/19

N2 - 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.

AB - 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.

KW - Electric Vehicles

KW - Location Selection

KW - Submodularity

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T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Y2 - 19 August 2018 through 23 August 2018

ER -

Du B , Tong Y, Zhou Z, Tao Q, Zhou W. Demand-aware charger planning for electric vehicle sharing. In 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). doi: 10.1145/3219819.3220032

Demand-aware charger planning for electric vehicle sharing

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Keywords

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