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Design+ EV charging capacity through queuing model

The composite cost is 266.24 yuan/h, which is the lowest Acknowledgments
of all scenarios. If the decision maker places high emphasis
on the comprehensive cost, then the number of charging None.
terminals in the public fast CS should be set to 8. If the Funding
user experience is more preferred, then 9 or 10 charging
terminals should be selected to avoid queuing or reduce This work was supported by the Natural Science Foundation
the queuing time of users. of Inner Mongolia Autonomous Region of China under
grant 2023MS07014; and “Inner Mongolia Science and
Our optimized solution is compared with the charging
station’s original configuration consisting of one 800kVA Technology Achievement Transfer and Transformation
Demonstration Zone, University Collaborative Innovation
box-type charging transformer and 15 charging terminals.
The comprehensive cost of the original scheme is calculated Base, and University Entrepreneurship Training Base”
to be 370.39, and the sojourn time is 67.5 min. The public Construction Project (Supercomputing Power Project)
fast-CS configuration optimization model proposed in this under grant 21300 – 231510.
study saves a huge cost and significantly reduces the user’s Conflict of interest
stay time at the CS. The configuration of eight charging
terminals reduces the comprehensive cost by 28.12% and The authors declare that they have no competing interests.
the sojourn time by 41.76% compared to the original
configuration. Author contributions
In this study, a CS capacity optimization model was Conceptualization: Hong Zhang
established by analyzing the operation data of public fast- Methodology: Feifan Shi
CSs in Hohhot City, and the model was solved to draw the Writing–original draft: Feifan Shi
following conclusions. Characterization of the charging Writing–review & editing: Hong Zhang
behavior of CS users shows that there is an obvious time
distribution characteristic of charging demand, and there Ethics approval and consent to participate
are two peak hours. There are also differences in users’ Not applicable.
charging duration and charging power. These data provide
an important reference for CS planning and operation. Based Consent for publication
on queuing theory analysis, a mathematical model of the Not applicable.
charging service system is established and key operational
indicators, such as average queue length and average Availability of data
waiting time, are calculated. This provides a theoretical
basis for evaluating and optimizing CS performance. The Data for used in the study can be obtained at:
CS capacity optimization model considers two objectives, https://10.6084/m9.figshare.26014237.
investment cost, and user time cost, and sets constraints References
such as facility utilization rate and maximum waiting
time. By solving the model, the optimal configuration of 1. Huang X, Wu D, Boulet B. Metaprobformer for charging
eight charging terminals is obtained. Compared with the load probabilistic forecasting of electric vehicle charging
original configuration scheme of 15 charging terminals, stations. IEEE Trans Intell Transp Syst. 2023;24(10):
the optimized scheme of 8 charging terminals reduces the 10445-10455.
comprehensive cost by 28.12% and the user’s stay time doi: 10.1109/TITS.2023.3276947
by 41.76%, which significantly improves the operational 2. Li H, Son D, Jeong B. Electric vehicle charging scheduling
efficiency and user experience of the CS. with mobile charging stations. J Clean Prod. 2024;434:140162.
This study proposes more practical and operable doi: 10.1016/j.jclepro.2023.140162
suggestions in the optimization of CS capacity. The findings
would help to promote the development and improvement 3. Du Z, Zheng L, Lin B. Influence of charging stations
accessibility on charging stations utilization. Energy.
of urban EV charging infrastructure. In future research, 2024;298:131374.
the relationship between CS capacity optimization and the
development of charging equipment technology, as well as doi: 10.1016/j.energy.2024.131374
the combination with urban EV development planning, 4. Zhao T, Li N, Kong N, Xie X. Optimal location of electrical
can be further explored to improve the operational vehicle charging stations with both self-and valet-charging
efficiency and service quality of CSs. service. IEEE Trans Autom Sci Eng. 2023.

Volume 2 Issue 2 (2025) 12 doi: 10.36922/dp.4225