A Spatio-Temporal Simulation Model for Electric Vehicle Charging Demands Considering User and Battery Behaviors

Feng Chen, Shaofeng Lu*, Yiwen Huang, Bing Han

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

Abstract

Accurate spatio-temporal estimation of electric vehicle (EV) charging demands is crucial for the operation and construction of charging networks. The complex behaviors of EV users (travel and charging) and batteries (discharge and charging) have a significant impact on charging demands. This study proposes a bottom-up simulation model (STEP-TV) for EV charging demands with the Monte Carlo method, considering the influence of user behavior, ambient temperature, and road congestion. 1) Firstly, vehicle trajectories are simulated based on multi-source data; 2) Secondly, the probability of users' destination charging is obtained using the Bayes formula; 3) Finally, an open-source tool, PyChargeModel, is introduced to simulate the non-uniform charging process and obtain the spatio-temporal distribution of charging demands. In a case study of Sioux Falls, we compared the simulation results of the proposed STEP-TV model and the EVI-Pro developed by the National Renewable Energy Laboratory (NREL). We found that user behavior and ambient temperature significantly impacted the charging demand estimation.

Original languageEnglish
Title of host publicationIECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society
PublisherIEEE Computer Society
ISBN (Electronic)9798350331820
DOIs
Publication statusPublished - 2023
Event49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023 - Singapore, Singapore
Duration: 16 Oct 202319 Oct 2023

Publication series

NameIECON Proceedings (Industrial Electronics Conference)
ISSN (Print)2162-4704
ISSN (Electronic)2577-1647

Conference

Conference49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023
Country/TerritorySingapore
CitySingapore
Period16/10/2319/10/23

Keywords

  • Monte Carlo method
  • bayes formula
  • charging behavior
  • charging demand
  • multi-source data

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