Super-linear Scaling Behavior for Electric Vehicle Chargers and Road Map to Addressing the Infrastructure Gap

Abstract

Enabling widespread electric vehicle (EV) adoption requires substantial build-out of charging infrastructure in the coming decade. We formulate the charging infrastructure needs as a scaling analysis problem and use it to estimate the EV infrastructure needs of the US at a county-level resolution. Surprisingly, we find that the current EV infrastructure deployment scales super-linearly with population, deviating from the sub-linear scaling of gasoline stations and other infrastructure. We discuss how this demonstrates the infancy of EV station abundance compared to other mature transportation infrastructures. By considering the power delivery of existing gasoline stations, and appropriate EV efficiencies, we estimate the EV infrastructure gap at the county level, providing a road map for future EV infrastructure expansion. Our reliance on scaling analysis allows us to make a unique forecast in this domain.

Figures (3)

• Figure 1: \ref{['fig:charger_scale_stations']}) Power Law Scaling for Gas Station and Electric Vehicle Supply Equipment for United State Counties (n = 3143), showing novel super-linear behavior for EVSE stations and expected sub-linear behavior for gas stations. Super-linear behavior suggests EVSE infrastructure has been consolidated in larger population centers. 95% confidence intervals for the scaling exponent $\beta$ are shown in the legend. \ref{['fig:charger_scale_power']}) Comparison of the power delivery of existing gas stations (Assuming 12 pumps per station and improved efficiency of EVs) to existing EVSE infrastructure. While the EVSE infrastructure of some counties has reached parity, in quantity with gasoline stations, no counties have reached parity in terms of power delivery.
• Figure 2: \ref{['fig:home_chargers_avg_speed']}) Average vehicle speed vs. time charging for varying charger power outputs. The solid black lines of constant driving speed ($v$) reflect the impact of increasing charge time, when the vehicle is stationary, on the average speed ($\bar{v}$). The maximum $\bar{v}$ for a given charger power occurs at $\alpha = 1/3$, indicated by the dashed vertical bar. \ref{['fig:home_chargers_driving_speed']}) Trade-off between driving speed $v$ and charger power for various charging times, with vertical lines at notable charger power levels. For example, a vehicle driving at 30mph would spend 10% of it's time charging at 11.5kW vs. 0.2% at 400kW.
• Figure 3: The EVSE Station Gap between the number of existing EVSE stations and the number predicted with Eq. \ref{['eq:ev_charger_prediction']}, assuming all current and future chargers are capable of 400kW. At present, no counties have sufficient EVSE stations to meet power parity, even when assuming all existing stations have been upgraded to 400 kW.