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The Scientific World Journal
Volume 2015, Article ID 354952, 9 pages
http://dx.doi.org/10.1155/2015/354952
Research Article

Charging Guidance of Electric Taxis Based on Adaptive Particle Swarm Optimization

National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China

Received 16 September 2014; Revised 6 December 2014; Accepted 2 January 2015

Academic Editor: Suleiman M. Sharkh

Copyright © 2015 Liyong Niu and Di Zhang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Electric taxis are playing an important role in the application of electric vehicles. The actual operational data of electric taxis in Shenzhen, China, is analyzed, and, in allusion to the unbalanced time availability of the charging station equipment, the electric taxis charging guidance system is proposed basing on the charging station information and vehicle information. An electric taxis charging guidance model is established and guides the charging based on the positions of taxis and charging stations with adaptive mutation particle swarm optimization. The simulation is based on the actual data of Shenzhen charging stations, and the results show that electric taxis can be evenly distributed to the appropriate charging stations according to the charging pile numbers in charging stations after the charging guidance. The even distribution among the charging stations in the area will be achieved and the utilization of charging equipment will be improved, so the proposed charging guidance method is verified to be feasible. The improved utilization of charging equipment can save public charging infrastructure resources greatly.