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Kumar, N., Chaudhry, R., Kaiwartya, O. 
ORCID: 0000-0001-9669-8244 and Kumar, N.,
2022.
ChaseMe: a heuristic scheme for electric vehicles mobility management on charging stations in a smart city scenario.
IEEE Transactions on Intelligent Transportation Systems.
ISSN 1524-9050
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1542640_Kaiwartya.pdf - Post-print Download (12MB) | Preview |
Abstract
Towards achieving the goal of green transportation, the usage of battery powered electric vehicles (BEVs) has been continuously growing across the globe. However, considering the limited number of Charging Stations (CSs) in the cities, electric vehicle charging problem has become a challenging task, especially, due to the constraints of longer waiting time and dynamic pricing at the CHs. This issue has led to the degradation in Quality of Experience (QoE) for BEV drivers. Moreover, Charging Point (CP) service providers in the cities also suffer from lack of space which causes higher congestion at the CSs. In this context, we propose ChaseMe, a heuristic scheme for optimizing CS management by scheduling BEVs based on availability and type (fast/ultra-fast) of CPs by considering delay and charging time for CPs reservation. The proposed heuristic scheme consists of two soft computing techniques i) Harris Hawk Optimization (HHO) and ii) Fuzzy Inference System (FIS). Former technique is used to map the CP reservation requests to the best-suited CS by considering Quality of Service (QoS) parameters and acting as a global optimizer. FIS locally manages CPs at a particular CS in coordination with proposed meta-heuristic technique. The experimental results prove the benefits of the proposed ChaseMe framework as compared to the state-of-the-art techniques considering various charging metrics for BEVs.
| Item Type: | Journal article | ||||||
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| Publication Title: | IEEE Transactions on Intelligent Transportation Systems | ||||||
| Creators: | Kumar, N., Chaudhry, R., Kaiwartya, O. and Kumar, N. | ||||||
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) | ||||||
| Date: | 2022 | ||||||
| ISSN: | 1524-9050 | ||||||
| Identifiers: |
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| Rights: | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||
| Divisions: | Schools > School of Science and Technology | ||||||
| Record created by: | Laura Ward | ||||||
| Date Added: | 13 May 2022 15:51 | ||||||
| Last Modified: | 13 May 2022 15:51 | ||||||
| URI: | https://irep.ntu.ac.uk/id/eprint/46320 |
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