| Title | ChaseMe: A Heuristic Scheme for Electric Vehicles Mobility Management on Charging Stations in a Smart City Scenario |
|---|---|
| ID_Doc | 41002 |
| Authors | Kumar, N; Chaudhry, R; Kaiwartya, O; Kumar, N |
| Title | ChaseMe: A Heuristic Scheme for Electric Vehicles Mobility Management on Charging Stations in a Smart City Scenario |
| Year | 2022 |
| Published | Ieee Transactions On Intelligent Transportation Systems, 23, 9 |
| 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. |
| http://irep.ntu.ac.uk/id/eprint/46320/1/1542640_Kaiwartya.pdf |
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