TY - GEN
T1 - A Spatio-Temporal Simulation Model for Electric Vehicle Charging Demands Considering User and Battery Behaviors
AU - Chen, Feng
AU - Lu, Shaofeng
AU - Huang, Yiwen
AU - Han, Bing
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Monte Carlo method
KW - bayes formula
KW - charging behavior
KW - charging demand
KW - multi-source data
UR - http://www.scopus.com/inward/record.url?scp=85179515846&partnerID=8YFLogxK
U2 - 10.1109/IECON51785.2023.10311941
DO - 10.1109/IECON51785.2023.10311941
M3 - Conference Proceeding
AN - SCOPUS:85179515846
T3 - IECON Proceedings (Industrial Electronics Conference)
BT - IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society
PB - IEEE Computer Society
T2 - 49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023
Y2 - 16 October 2023 through 19 October 2023
ER -
