(no-name)

Abstract:

Electric vehicles (EVs) have contributed to reducing carbon emissions, promoting the advancement of sustainable transportation. However, a widespread adoption of EV is significantly impeded by the lack of charging stations and inefficient charging systems. To address these issues, we introduce a new Charging Optimization Framework (COF) based on quantitative modeling implemented in Anylogic simulation software. The COF aims to reduce the EV charging time while meeting customers’ demands. The methodology incorporates real-time stochastic simulation of EV arrival patterns using a non-homogeneous Poisson process. Computational results indicate a significant reduction (81.46%) in overall charging time over the baseline using the COF. Based on a new System Efficiency metric, the optimal number of charging stations and waiting stations were computed as 11 and 13, respectively.

See publication:
https://www.proquest.com/openview/60320ae63070362be3b1bc882b49b65f/1?pq-origsite=gscholar&cbl=51908
This publication pertains to:
Charging Stations
Publication Authors:
  • Tenzin Lhaden
  • Honglun Xu
  • Bill Tseng
  • Michael Pokojovy
  • Denisse Urenda Castañeda
  • Fashiar Rahman
  • Chun-Che Huang
It appeared in:
Peer-reviewed conference proceedings
Shout-outs/Achievements:
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Keywords:
EV charging station, Digital Twin,Optimization