4.6 Article Proceedings Paper

A Two-Stage Charging Facilities Planning Method for Electric Vehicle Sharing Systems

Journal

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
Volume 57, Issue 1, Pages 149-157

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIA.2020.3034557

Keywords

Charging stations; Planning; Electric vehicles; Clustering algorithms; Capacity planning; Urban areas; Space vehicles; Charging facility; mean clustering; mean shift algorithm; queuing theory; sharing electric vehicle (SEV); two-stage planning

Funding

  1. National Key R&D Program of China [2018YFE0208400]

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This study proposes a two-stage planning method that combines charging stations and scattered charging piles to balance the economy of SEV operators and the convenience of SEV users. The first stage determines the location and capacity of charging stations, while the second stage plans unsatisfied charging demand points and compensated charging piles.
The large-scale sharing electric vehicle (SEV) need adequate charging infrastructure to maintain the optimal operation. Considering the convenience of SEV users' charging during driving, a two-stage planning method combining charging stations and scattered charging piles for SEV is proposed to balance the economy of SEV operators and the convenience of SEVs users. In the first stage, according to the urban area division and trip characteristics of SEV, the charging power demand of SEV in each SEV placement point can be estimated exactly. Then, aiming for the minimum annual total cost, the mean clustering algorithm and queuing theory are used to determine the location and capacity of charging stations. In the second stage, an improved S-shaped function is used to assess the unsatisfied charging demand points of SEV users, and the compensated charging piles without in the stations are planned based on the mean shift algorithm. Based on the re-division of the service scope, the capacity of the charging stations is updated. Finally, the simulation results verifying the proposed model and algorithm is effective and feasible.

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