Charging Guidance of Electric Taxis Based on Adaptive Particle Swarm Optimization
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...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-01-01
|
| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2015/354952 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832548478254841856 |
|---|---|
| author | Liyong Niu Di Zhang |
| author_facet | Liyong Niu Di Zhang |
| author_sort | Liyong Niu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-948ca6422fd04dfd83fb933e69b231a3 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-948ca6422fd04dfd83fb933e69b231a32025-02-03T06:13:57ZengWileyThe Scientific World Journal2356-61401537-744X2015-01-01201510.1155/2015/354952354952Charging Guidance of Electric Taxis Based on Adaptive Particle Swarm OptimizationLiyong Niu0Di Zhang1National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, ChinaNational Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, ChinaElectric 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.http://dx.doi.org/10.1155/2015/354952 |
| spellingShingle | Liyong Niu Di Zhang Charging Guidance of Electric Taxis Based on Adaptive Particle Swarm Optimization The Scientific World Journal |
| title | Charging Guidance of Electric Taxis Based on Adaptive Particle Swarm Optimization |
| title_full | Charging Guidance of Electric Taxis Based on Adaptive Particle Swarm Optimization |
| title_fullStr | Charging Guidance of Electric Taxis Based on Adaptive Particle Swarm Optimization |
| title_full_unstemmed | Charging Guidance of Electric Taxis Based on Adaptive Particle Swarm Optimization |
| title_short | Charging Guidance of Electric Taxis Based on Adaptive Particle Swarm Optimization |
| title_sort | charging guidance of electric taxis based on adaptive particle swarm optimization |
| url | http://dx.doi.org/10.1155/2015/354952 |
| work_keys_str_mv | AT liyongniu chargingguidanceofelectrictaxisbasedonadaptiveparticleswarmoptimization AT dizhang chargingguidanceofelectrictaxisbasedonadaptiveparticleswarmoptimization |