TY - GEN
T1 - Spatiotemporal Distribution of Electric Vehicle State of Charge Based on Travel Chains and Gravity Model
AU - Ma, Zhuxin
AU - Wang, Zixuan
AU - Yu, Siyan
AU - Zhang, Qian
AU - Wu, Ming
AU - Gao, Jinghui
AU - Zhong, Lisheng
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The rapid increase in electric vehicle usage has turned the establishment of charging stations into a pressing issue. Accurately estimating the demand load for these vehicles is vital for determining optimal station locations and their capacities. Load estimation is important both for research and practical purposes, especially for advancing sustainable practices in electric vehicles. This study utilizes travel chain concepts and a gravity model to investigate how the state of charge (SOC) of electric vehicles is distributed over time and space. By scrutinizing the travel patterns of private EVs in the road network, we can forecast the demand on the network. Furthermore, the gravity model is applied to enhance the process of selecting charging station sites, indicating their appeal to potential users.
AB - The rapid increase in electric vehicle usage has turned the establishment of charging stations into a pressing issue. Accurately estimating the demand load for these vehicles is vital for determining optimal station locations and their capacities. Load estimation is important both for research and practical purposes, especially for advancing sustainable practices in electric vehicles. This study utilizes travel chain concepts and a gravity model to investigate how the state of charge (SOC) of electric vehicles is distributed over time and space. By scrutinizing the travel patterns of private EVs in the road network, we can forecast the demand on the network. Furthermore, the gravity model is applied to enhance the process of selecting charging station sites, indicating their appeal to potential users.
KW - Electric vehicles
KW - spatio-temporal distribution
KW - travel chain
KW - universal gravitation model
UR - https://www.scopus.com/pages/publications/105002223167
U2 - 10.1109/APPEEC61255.2024.10922639
DO - 10.1109/APPEEC61255.2024.10922639
M3 - 会议稿件
AN - SCOPUS:105002223167
T3 - Asia-Pacific Power and Energy Engineering Conference, APPEEC
BT - 2024 IEEE PES 16th Asia-Pacific Power and Energy Engineering Conference
PB - IEEE Computer Society
T2 - 16th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2024
Y2 - 25 October 2024 through 27 October 2024
ER -