Market Analysis of Electric Transportation in the USA : Vehicles, Charging Solutions, Grid Integration, and Pricing Schemes

Abstract

The rapid expansion of electric vehicles (EVs) in the United States has created new challenges for market planning, charging-infrastructure deployment, and grid operations. This thesis analyses these developments using state-level EV data from 2018 to 2023 and high-resolution records of workplace charging sessions from the Caltech Adaptive Charging Network (ACN). A combined empirical approach is applied, including trend analysis, charging-infrastructure assessment, regression modelling, and a Monte Carlo simulation to project future registrations. National EV market share increased steadily over the study period, with the sharpest rise occurring after 2020. Charging-station counts grew in parallel, although Level 2 chargers dominated the 2023 infrastructure mix, while Level 1 chargers accounted for only a small fraction of outlets. Electricity prices varied widely across states but showed no strong relationship with EV adoption. State-level analysis for 2023 revealed substantial variation in EV penetration, with Washington, Oregon, the District of Columbia, and California among the highest-adopting regions. The regression results showed statistically significant associations between EV share and variables such as gasoline price, income, education, and the number of charging stations. At the same time, state financial incentives did not exhibit an independent effect in the pooled model. A Monte Carlo simulation using post-2020 log-growth rates estimates that national EV registrations could reach roughly 90 million by 2030, with a 90% confidence interval ranging from about 77 to 108 million. ACN session data provided insight into grid-side impacts: charging events were short and highly clustered, leading to sharp peaks in feeder loading. Under the baseline (uncontrolled) scenario, the 70 kW feeder limit was violated multiple times. A simple valley-filling load-smoothing strategy preserved users’ charging requirements while reducing the peak hourly load from 75.1 kW to 64.3 kW and marginally lowering the 95th-percentile load from 28.2 kW to 28.0 kW. The load-duration curve indicated that these benefits were concentrated during peak hours rather than changing the average daily energy use. Overall, the findings show that U.S. EV adoption is accelerating faster than public charging infrastructure growth in many states. That unmanaged charging can create operational stress on local distribution assets. Even basic load smoothing methods can significantly reduce peak demand, highlighting the importance of coordinated infrastructure planning and load-management strategies as EV penetration continues to increase.