Yuxi Shen, Alyssa Ryan, Melrose Pan, Xiaoqin Fu, Liang Zhang
Electric vehicle (EV) adoption reshapes travel and electricity demand. We integrate a Cumulative Prospect Theory (CPT) model with a dynamic reference-point mechanism into daily travel routines derived from the 2022 U.S. National Household Travel Survey. Using empirical data from 283 EV users, the simulation generates charging behavior for 10,000 synthetic individuals. Four behavioral archetypes emerge: Home-Focused, Work-Focused, Anxious Opportunistic, and Long-Distance drivers, with unique charging preferences and infrastructure requirements. While behavioral factors significantly influence individual charging decisions, our sensitivity analysis demonstrates that varying CPT parameters across their observed ranges alters aggregate daily energy consumption by less than 1%. Conversely, infrastructure changes, such as increased workplace charging, significantly shift evening charging to morning (up to 50.9%) and reduce evening peak load (9.3% reduction). These results suggest that while behavioral modeling is important at the individual level, infrastructure design plays a more critical role in managing overall grid impacts.