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DOE OSTI · 3002704

Evaluating system responses to electric vehicle charging infrastructure expansion through data-driven simulation

Abstract

Understanding the system responses to electric vehicle (EV) charging infrastructure expansion, including vehicle charging needs, station utilization, and energy consumption, is critical for effective planning to meet growing charging demand without unnecessary resource investment. This study evaluates the system responses to EV charging infrastructure expansion, focusing on charging needs, station utilization, and energy consumption. Using trip data from the National Household Travel Survey and origin–destination patterns, we simulated trip chains in downtown Atlanta with 10 % EV penetration. We assessed 32 scenarios involving different charging port power levels and siting strategies. Furthermore, we found that higher-power ports were more sensitive to placement, with concentrated expansion boosting station utilization more than uniform expansion. Adding high-power ports did not always increase peak energy consumption; in some cases, a few 400 kW ports reduced overall consumption compared to 150 kW ports by enabling faster charging and higher vehicle turnover.

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BibTeXRIS

Pan, Meiyu (Melrose) [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:000000031627448X), Li, Wan [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000306900272), Wang, Chieh (Ross) [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000180737683), Wang, Shian [Univ. of Kansas, Lawrence, KS (United States)], Li, Fengqi (Frank) [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000238879968), Dong, Jin [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000257531588), New, Joshua [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000180150583). 2025-09-10. Evaluating system responses to electric vehicle charging infrastructure expansion through data-driven simulation. https://doi.org/10.1016/j.seta.2025.104565

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