Name
Abstract | ||
|---|---|---|
Nowadays, Electric Vehicles (EVs) have gone through a rapid development since people pay more attention to environmental conservation. However, the availability of EVs is seriously restricted by the scarcity of efficient charging infrastructures. In this paper, we propose a congestion-aware charging scheme to deploy Charging Stations (CSs). We first leverage vehicles' dwell events to define the charging demand. Concretely, we take advantage of M/M/x/N queueing model to calculate the probability of a vehicle being rejected by a CS. We further formulate a charging rejection probability minimization problem based on assessable information of CSs. We have proposed heuristic algorithms to determine the optimal deployment strategy. Finally, we perform extensive experiments using a real taxi trajectory dataset. Results demonstrate that our proposed algorithms are effective in minimizing vehicles' charging rejection probability compared with baselines.
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3321408.3321605 | Proceedings of the ACM Turing Celebration Conference - China |
Keywords | Field | DocType |
charging stations deployment, electric vehicles, queueing theory, taxi trajectory | Automotive engineering,Software deployment,Electric vehicle,Computer science | Conference |
ISBN | Citations | PageRank |
978-1-4503-7158-2 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feng Yao | 1 | 30 | 5.58 |
| Xiong Wang | 2 | 0 | 1.01 |
| Wenkuan Dai | 3 | 2 | 1.10 |
| Shujuan Gao | 4 | 0 | 0.34 |
| Xiaoying Gan | 5 | 344 | 48.16 |
| Siyi Wang | 6 | 329 | 25.98 |