TY - JOUR
T1 - Deploying Battery Swap Stations for Electric Freight Vehicles based on Trajectory Data Analysis
AU - Wang, Shiqi
AU - Shao, Chunfu
AU - Zhuge, Chengxiang
AU - Sun, Mingdong
AU - Wang, Pinxi
AU - Yang, Xiong
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 52002345 and in part by the Hong Kong Polytechnic University under Grant 1-BE2J; P0038213.
Publisher Copyright:
© 2015 IEEE.
PY - 2022/9/1
Y1 - 2022/9/1
N2 - This article proposed a biobjective model to deploy battery swap stations for electric freight vehicles (EFVs) based on big data analysis. We particularly extracted trip, parking, and charging information of EFVs in Beijing from a one-week dataset containing trajectories of 17 716 EFVs (with a sample rate of 99.8%) in 2019 to define rules in the model and parameterize the model, so as to improve the model realism and accuracy. The biobjective model aimed to minimize the total cost of building battery swap stations and maximize operational efficiency of EFVs. The model was solved by a genetic algorithm. Parameter sensitivity analysis was also conducted. The test case of Beijing suggested that the biobjective model, together with genetic algorithm, could help freight companies find a set of Pareto optimal solutions to the deployment of battery swap stations. Among the solutions, the one with the highest investment in battery swap stations could reduce the average charging time of EFVs by 96.56%. In addition, the sensitivity analysis results suggested that the parameters related to battery, infrastructure, and number of EFVs were influential to both the total costs and operational efficiency of EFVs and should be considered carefully in the deployment of battery swap stations.
AB - This article proposed a biobjective model to deploy battery swap stations for electric freight vehicles (EFVs) based on big data analysis. We particularly extracted trip, parking, and charging information of EFVs in Beijing from a one-week dataset containing trajectories of 17 716 EFVs (with a sample rate of 99.8%) in 2019 to define rules in the model and parameterize the model, so as to improve the model realism and accuracy. The biobjective model aimed to minimize the total cost of building battery swap stations and maximize operational efficiency of EFVs. The model was solved by a genetic algorithm. Parameter sensitivity analysis was also conducted. The test case of Beijing suggested that the biobjective model, together with genetic algorithm, could help freight companies find a set of Pareto optimal solutions to the deployment of battery swap stations. Among the solutions, the one with the highest investment in battery swap stations could reduce the average charging time of EFVs by 96.56%. In addition, the sensitivity analysis results suggested that the parameters related to battery, infrastructure, and number of EFVs were influential to both the total costs and operational efficiency of EFVs and should be considered carefully in the deployment of battery swap stations.
KW - Battery swap station
KW - biobjective model
KW - electric vehicle (EV)
KW - freight transport
KW - infrastructure deployment
KW - trajectory data
UR - http://www.scopus.com/inward/record.url?scp=85126682092&partnerID=8YFLogxK
U2 - 10.1109/TTE.2022.3160445
DO - 10.1109/TTE.2022.3160445
M3 - Journal article
AN - SCOPUS:85126682092
SN - 2332-7782
VL - 8
SP - 3782
EP - 3800
JO - IEEE Transactions on Transportation Electrification
JF - IEEE Transactions on Transportation Electrification
IS - 3
ER -