An Agent-based Travel and Charging Behavior Model for Forecasting High- resolution Spatio-temporal Battery Electric Vehicle Charging Demand Center for Transportation, Environment, and Community Health Final Report

The expansion of the battery electric vehicle (BEV) market requires considerable changes in the supply of electricity to fulfill the charging demand. To this end, understanding the spatio-temporal distribution of BEV charging demand at a micro-level is crucial for optimal electric vehicle supply equipment (EVSE) planning and electricity load management. This research proposes an integrated activity-based BEV charging demand simulation model, which considers both realistic travel and charging behaviors and provides high-resolution spatio-temporal demand in real-world applications. Moreover, a novel charging choice model is proposed which provides more realistic demand modeling by allowing critical non-linearities in random utility to better describe observed charging behaviors. The results of a case study for the Atlanta metropolitan area imply that work/public charging has a substantial potential market, which can serve up to 64.5% of the total demand. Out of multiple charging modes, demand for direct-current fast charging (DCFC) is prominent at work/public, and it takes the largest portion of the non-residential demand in all simulation scenarios. Moreover, charging behaviors have significant impacts on the demand distribution. Comparing to risk-neutral users, high-risk sensitive users require 49% to 91% higher peak power demand of level 2 chargers at work/public. Users’ preferences for fast charging rates can change DCFC demand from 36.4% to 53.7% of the total demand. This study helps to qualitatively analyze the factors of charging demand and their impacts on the demand distribution. The results can be directly used in EVSE planning and electricity load prediction.