TY - JOUR
T1 - Data-driven electric vehicle usage and charging analysis of logistics vehicle in Shenzhen, China
AU - Meng, Yihao
AU - Zou, Yuan
AU - Ji, Chengda
AU - Zhai, Jianyang
AU - Zhang, Xudong
AU - Zhang, Zhaolong
N1 - Publisher Copyright:
© 2024 Elsevier Ltd
PY - 2024/10/30
Y1 - 2024/10/30
N2 - The electrification of transportation is profoundly reshaping human society and presenting new challenges in terms of travel modes, infrastructure development, and energy supply. Given the potential for large-scale scheduling of electric logistics vehicles (ELVs), it is crucial to thoroughly analyze the usage characteristics and establish reliable models. This study examines the usage patterns and charging behaviors of 29 ELVs in Shenzhen, China, encompassing 34,856 trips and 14,464 charging events. Furthermore, behavior-time probability density models were constructed based on an improved Gaussian mixture model (GMM), which avoids the fitting error caused by misclassification of time series data across time nodes. The article also provides a comprehensive analysis of other statistical findings related to the travel and charging activities of ELVs. The conclusions drawn from this research can serve as valuable references for industries involved in infrastructure construction, power grid management, battery virtual aggregation, and similar sectors.
AB - The electrification of transportation is profoundly reshaping human society and presenting new challenges in terms of travel modes, infrastructure development, and energy supply. Given the potential for large-scale scheduling of electric logistics vehicles (ELVs), it is crucial to thoroughly analyze the usage characteristics and establish reliable models. This study examines the usage patterns and charging behaviors of 29 ELVs in Shenzhen, China, encompassing 34,856 trips and 14,464 charging events. Furthermore, behavior-time probability density models were constructed based on an improved Gaussian mixture model (GMM), which avoids the fitting error caused by misclassification of time series data across time nodes. The article also provides a comprehensive analysis of other statistical findings related to the travel and charging activities of ELVs. The conclusions drawn from this research can serve as valuable references for industries involved in infrastructure construction, power grid management, battery virtual aggregation, and similar sectors.
KW - Charging behaviors
KW - Electric logistics vehicle
KW - Real-world data
KW - Travel pattern
UR - http://www.scopus.com/inward/record.url?scp=85200764869&partnerID=8YFLogxK
U2 - 10.1016/j.energy.2024.132720
DO - 10.1016/j.energy.2024.132720
M3 - Article
AN - SCOPUS:85200764869
SN - 0360-5442
VL - 307
JO - Energy
JF - Energy
M1 - 132720
ER -