TY - GEN
T1 - GECM
T2 - 87th IEEE Vehicular Technology Conference, VTC Spring 2018
AU - Huang, Pengfei
AU - Li, Changle
AU - Luo, Quyuan
AU - Zhang, Yao
AU - Xia, Bing
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/20
Y1 - 2018/7/20
N2 - The increasing per capita vehicle ownership has led to extremely serious traffic congestion, energy crisis and environment pollution. Electric vehicle, as a representative of energy structure transition and traffic component changes can effectively solve the mentioned issues. In this paper, we propose a novel energy consumption model for electric vehicles, Green wave band based Energy Consumption Model (GECM), which is built upon traffic signal control theory and combines with the distribution of energy consumption in a macroscopic view. This model designs green wave scenarios based on the traffic signal data in real traffic surroundings, and involves specific parameters like offset and green split for the analysis of energy consumption. Simulation results show that the energy saving and efficiency improvement are available and feasible for electric vehicles under the proposed model.
AB - The increasing per capita vehicle ownership has led to extremely serious traffic congestion, energy crisis and environment pollution. Electric vehicle, as a representative of energy structure transition and traffic component changes can effectively solve the mentioned issues. In this paper, we propose a novel energy consumption model for electric vehicles, Green wave band based Energy Consumption Model (GECM), which is built upon traffic signal control theory and combines with the distribution of energy consumption in a macroscopic view. This model designs green wave scenarios based on the traffic signal data in real traffic surroundings, and involves specific parameters like offset and green split for the analysis of energy consumption. Simulation results show that the energy saving and efficiency improvement are available and feasible for electric vehicles under the proposed model.
UR - http://www.scopus.com/inward/record.url?scp=85050986341&partnerID=8YFLogxK
U2 - 10.1109/VTCSpring.2018.8417767
DO - 10.1109/VTCSpring.2018.8417767
M3 - 会议稿件
AN - SCOPUS:85050986341
T3 - IEEE Vehicular Technology Conference
SP - 1
EP - 5
BT - 2018 IEEE 87th Vehicular Technology Conference, VTC Spring 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 3 June 2018 through 6 June 2018
ER -
