Tools
Tools
Zhang, Xu 
ORCID: https://orcid.org/0000-0001-6557-6607, Cao, Yue, Peng, Linyu, Li, Jichun, Ahmad, Naveed and Yu, Shengping
(2020)
Mobile charging as a service: A reservation-based approach.
IEEE Transactions on Automation Science and Engineering, 17 (4).
pp. 1976-1988.
ISSN 1545-5955
Preview |
PDF (Mobile Charging as a Service A Reservation-Based)
- Accepted Version
Download (7MB) | Preview |
Abstract
This article aims to design an intelligent mobile charging control mechanism for electric vehicles (EVs), by promoting charging reservations (including service start time, expected charging time, and charging location). EV mobile charging could be implemented as an alternative recharging solution, wherein charge replenishment is provided by economically mobile plug-in chargers, capable of providing on-site charging services. With intelligent charging management, readily available mobile chargers are predictable and could be efficiently scheduled toward EVs with charging demand, based on updated context collected from across the charging network. The context can include critical information relating to charging sessions and charging demand. Furthermore, with reservations introduced, accurate estimations on charging demand for a future moment are achievable, and correspondingly, optimal mobile chargers selection can be obtained. Therefore, charging demands across the network can be efficiently and effectively satisfied, with the support of intelligent system-level decisions. In order to evaluate critical performance attributes, we further carry out extensive simulation experiments with practical concerns to verify our insights observed from the theoretical analysis. Results show great performance gains by promoting the reservation-based mobile charger selection, especially for mobile chargers equipped with suffice power capacity. Note to Practitioners-The convenience of charging service is one major concern for EVs, especially when an urgent charging is required while none charging points are reachable. Recently, a Chinese EV company (NIO, Inc., Shanghai, China) is promoting its mobile charger (ES8 model) to Tesla. Driven by such market trend, this article proposes an efficient approach toward intelligent scheduling of mobile chargers toward parked EVs. Different from fixed charging stations focusing on the problem of long waiting times, the proposed solution is applicable to charging-on-demand with precharging appointment at mobile chargers. Preliminary experiments show great charging efficiency achieved by concerning the issue of where to reserve, i.e., the consideration of optimal selection on mobile chargers. Such mobile charging services can coexist with the governmental or pilots' initiated charging station deployment. However, future research will need to evaluate the holistic service platform.
| Item Type: | Article |
|---|---|
| Additional Information: | Funding Information: This work was supported by the Joint Fund of Guangdong Province Foundation and Applied Science under Grant 2019A1515110238 and National Natural Science Foundation of China under Grant U1864206. Publication history: Manuscript received June 26, 2019; revised February 7, 2020; accepted March 23, 2020. Date of publication April 15, 2020; date of current version October 6, 2020. |
| Uncontrolled Keywords: | battery charging,electric vehicles (evs),mobile charging services,queuing theorem,control and systems engineering,electrical and electronic engineering ,/dk/atira/pure/subjectarea/asjc/2200/2207 |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Data Science and AI |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 27 Jan 2024 03:14 |
| Last Modified: | 12 Dec 2024 01:51 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/94279 |
| DOI: | 10.1109/TASE.2020.2983819 |
Downloads
Downloads per month over past year
Actions (login required)
 |
View Item |