TY - JOUR
T1 - Cooperative charging management for electric vehicles via mobile edge computation
AU - Zhang, Yuanjian
AU - Guo, Chong
AU - Sun, Chao
AU - Chen, Zheng
AU - Li, Guang
AU - Liu, Yonggang
N1 - Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/10/31
Y1 - 2020/10/31
N2 - Unplanned charging supervision of electric vehicles may deteriorate their penetration in alleviating pollution and reducing the driving efficiency, and proper management is critical to reduce charging waiting time and efficiently design driving behaviours from spots to charging stations. Motivated by this, a novel bi-functional charging management strategy in virtue of the mobile edge computation based framework is proposed in this study to effectively book the charging piles with less waiting time and meanwhile achieve better energy efficiency during charging booking. First, a novel charging booking algorithm is developed to determine the most suitable charging station and optimally plan the shortest route to the preferred charging station. Second, a driving behaviour optimization method is designed to plan the efficient velocity profile of the trip to the selected station under the constrained time calculated by the charging booking algorithm. The simulation analysis validates that the proposed bi-functional management strategy can reasonably book suitable charging stations and efficiently reduce energy consumption in the charging booking process, highlighting its anticipated preferable performance.
AB - Unplanned charging supervision of electric vehicles may deteriorate their penetration in alleviating pollution and reducing the driving efficiency, and proper management is critical to reduce charging waiting time and efficiently design driving behaviours from spots to charging stations. Motivated by this, a novel bi-functional charging management strategy in virtue of the mobile edge computation based framework is proposed in this study to effectively book the charging piles with less waiting time and meanwhile achieve better energy efficiency during charging booking. First, a novel charging booking algorithm is developed to determine the most suitable charging station and optimally plan the shortest route to the preferred charging station. Second, a driving behaviour optimization method is designed to plan the efficient velocity profile of the trip to the selected station under the constrained time calculated by the charging booking algorithm. The simulation analysis validates that the proposed bi-functional management strategy can reasonably book suitable charging stations and efficiently reduce energy consumption in the charging booking process, highlighting its anticipated preferable performance.
KW - Charging booking algorithm (CBA)
KW - Charging management strategy (CMS)
KW - Driving behaviour optimization (DBO)
KW - Electric vehicles (EVs)
KW - Mobile edge computation (MEC)
UR - https://www.scopus.com/pages/publications/85088784519
U2 - 10.1016/j.jpowsour.2020.228533
DO - 10.1016/j.jpowsour.2020.228533
M3 - Article
AN - SCOPUS:85088784519
SN - 0378-7753
VL - 474
JO - Journal of Power Sources
JF - Journal of Power Sources
M1 - 228533
ER -