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Cao, Yue, Zhang, Xu 
ORCID: https://orcid.org/0000-0001-6557-6607, Zhou, Bingpeng, Duan, Xuting, Tian, Daxin and Dai, Xuewu
(2021)
MEC intelligence driven electro-mobility management for battery switch service.
IEEE Transactions on Intelligent Transportation Systems, 22 (7).
pp. 4016-4029.
ISSN 1524-9050
Abstract
As a key enabler in the green transport system, the popularity of Electric Vehicles (EV) has attracted attention from academia and industrial communities. However, the driving range of EVs is inevitably affected by the insufficient battery volume, as such EV drivers may experience trip discomfort due to a long battery charging time (under traditional plug-in charging service). One feasible alternative to accelerate the service time to feed electricity is the battery switch technology, by cycling switchable (fully-recharged) batteries at Battery Switch Stations (BSSs) to replace the depleted batteries from incoming EVs. Along with recent advance of vehicle cooperation through emerging Information Communication Technology (ICT), in this paper we propose a Mobile Edge Computing (MEC) driven architecture to gear the intelligent battery switch service management for EVs. Here, the decision making on where to switch battery is operated by EVs in a distributed manner. Besides, the Vehicle-to-Vehicle (V2V) communication in line with public transportation bus system is applied to operate flexible information exchange between EVs and BSSs. Dedicated MEC functions are positioned for bus system to efficiently disseminate BSSs status and aggregate EVs' reservations, concerning the massive signalling exchange cost. The Global Controller (GC) is positioned as cloud server to gather BSSs (service providers) status and EVs' reservations (clients), and predict the service availability of BSS (e.g., whether/when a battery can be switched). We conduct performance evaluation to show the advantage of MEC system in terms of reduction of communication cost, and BSS service management scheme regarding reduction of service waiting time (e.g., how long to wait for battery switch) and increase of service satisfaction rate (e.g., how many batteries to switch for EVs).
| Item Type: | Article |
|---|---|
| Additional Information: | Funding Information: The work was supported in part by the Joint Fund of Guangdong Province Foundation and Applied Science under Grant 2019A1515110238. |
| Uncontrolled Keywords: | electric vehicle,mobile edge computing,transportation management,v2v communication,automotive engineering,mechanical engineering,computer science applications ,/dk/atira/pure/subjectarea/asjc/2200/2203 |
| 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: | 01 Feb 2024 03:08 |
| Last Modified: | 10 Dec 2024 01:43 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/94334 |
| DOI: | 10.1109/TITS.2020.3004117 |
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