TY - JOUR
T1 - MEC intelligence driven electro-mobility management for battery switch service
AU - Cao, Yue
AU - Zhang, Xu
AU - Zhou, Bingpeng
AU - Duan, Xuting
AU - Tian, Daxin
AU - Dai, Xuewu
N1 - Funding Information: The work was supported in part by the Joint Fund of Guangdong Province Foundation and Applied Science under Grant 2019A1515110238.
PY - 2021/7
Y1 - 2021/7
N2 - 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).
AB - 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).
KW - Electric Vehicle
KW - mobile edge computing
KW - transportation management
KW - V2V communication
UR - http://www.scopus.com/inward/record.url?scp=85110876095&partnerID=8YFLogxK
U2 - 10.1109/TITS.2020.3004117
DO - 10.1109/TITS.2020.3004117
M3 - Article
AN - SCOPUS:85110876095
SN - 1524-9050
VL - 22
SP - 4016
EP - 4029
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 7
M1 - 9142422
ER -