Comparing costs and climate impacts of various electric vehicle charging systems across the United States

“Source_Data.xlsx” contains source data for Figs. 1 to 4 and Supplementary Figs. 1, 2, and 4 to 10. The “INTRO” sheet contains instructions and hyperlinks to the data sheets in the workbook.

Below is the file structure for “EV_Paper_Figs.zip”, aligning with the figure numbers from the research article and supplementary materials:

Folder (Fig1): Total change in cost due to electric vehicle (EV) adoption. County level results are presented for the change in total cost of ownership due to the transition from internal combustion engine vehicles (ICEVs) to EVs (a-c) as a percentage and (d-f) in billions (B) of 2022 United States Dollars (USD). Each map corresponds to EVs charged via (a, d) Direct Current Fast Charging (DCFC), (b, e) Battery Swapping (BSS), and (c, f) Dynamic Wireless Power Transfer (DWPT). Source data are provided as a Source Data file and base map layer is available from OpenStreetMap (openstreetmap.org/copyright).
• Trailing numbers following the figure name (e.g., Fig1a_) represent different scenario values for four variables: [EV adoption, capital cost, electricity price, fuel price]. Each variable is coded with 1 for optimistic, 2 for baseline, and 3 for conservative. For instance, Fig1a_2222 indicates that all four variables are set to baseline levels in Fig1a.

Folder (Fig2): Breakdown of the 10-year total cost of ownership. Results are presented for an average a passenger car, b light duty truck, c medium duty vehicle, and d heavy duty vehicle in the contiguous United States. The vehicle types include electric vehicles (EVs) charged via Direct Current Fast Charging (DCFC-EV), Battery Swapping (BSS-EV), and Dynamic Wireless Power Transfer (DWPT-EV). The EVs are compared to an average internal combustion engine vehicle (ICEV) and hybrid electric vehicle (HEV) from each vehicle category. Abbreviations: 2022 United States Dollars (USD). Source data are provided as a Source Data file.
• Trailing numbers following the figure name (e.g., Fig2_) represent different scenario values for four variables: [EV adoption, capital cost, electricity price, fuel price]. Each variable is coded with 1 for optimistic, 2 for baseline, and 3 for conservative. For instance, Fig2_1111 indicates that all four variables are set to optimistic levels in Fig2.
Folder (Fig3): Total change to greenhouse gas (GHG) emissions from electric vehicle (EV) adoption. The maps are for the change in GHG emissions of on-road vehicle transportation in United States counties by switching from internal combustion engine vehicles (ICEVs) to EVs charged via (a, d) Direct Current Fast Charging (DCFC), (b, e) Battery Swapping (BSS), and (c, f) Dynamic Wireless Power Transfer (DWPT). The results are presented as (a-c) a percentage and (d-f) in billions (B) of kilograms (kg) of Carbon Dioxide equivalent. Source data are provided as a Source Data file and base map layer is available from OpenStreetMap (openstreetmap.org/copyright).
• Trailing numbers following the figure name (e.g., Fig3a_) represent different scenario values for two variables: [EV adoption, electricity mix]. Each variable is coded with 1 for optimistic, 2 for baseline, and 3 for conservative. For instance, Fig3a_12 indicates that EV adoption is set to optimistic and electricity mix is set to baseline in Fig3a.
Folder (Fig4): Breakdown of the lifetime greenhouse gas intensity. Results are for an average a passenger car, b light-duty truck, c medium duty vehicle, and d heavy duty vehicle in the contiguous United States. The vehicle scenarios include electric vehicles (EVs) charged via Direct Current Fast Charging (DCFC-EV), Battery Swapping (BSS-EV), and Dynamic Wireless Power Transfer (DWPT-EV). Results are compared to an internal combustion engine vehicle (ICEV) and hybrid electric vehicle (HEV) from each vehicle category. Abbreviations: Carbon Dioxide equivalent (CO2e). Source data are provided as a Source Data file.
• Trailing numbers following the figure name (e.g., Fig4_) represent different scenario values for two variables: [EV adoption, electricity mix]. Each variable is coded with 1 for optimistic, 2 for baseline, and 3 for conservative. For instance, Fig4_23 indicates that EV adoption is set to baseline and electricity mix is set to conservative in Fig4.

Folder (Sup_Fig8): Levelized cost of charging in United States counties. Results are presented for a Direct Current Fast Charging (DCFC), b Battery Swapping (BSS), and c Dynamic Wireless Power Transfer (DWPT). Abbreviations: 2022 United States Dollars (USD); kilowatt-hour (kWh). Source data are provided as a Source Data file and base map layer is available from OpenStreetMap (openstreetmap.org/copyright).
• Trailing numbers following the figure name (e.g., Sup_Fig8a_) represent different scenario values for three variables: [EV adoption, capital cost, electricity price]. Each variable is coded with 1 for optimistic, 2 for baseline, and 3 for conservative. For instance, Sup_Fig8a_222 indicates that all three variables are set to baseline levels in Supplementary Fig8a.

Folder (Sup_Fig9): Breakdown of the charging/fuel cost. Average costs in the contiguous United States are shown for electric vehicles charged via Direct Current Fast Charging (DCFC-EV), Battery Swapping (BSS-EV), and Dynamic Wireless Power Transfer (DWPT-EV). Results are compared to an average internal combustion engine vehicle (ICEV) and hybrid electric vehicle (HEV) fueled by gasoline or diesel for the vehicle categories of a passenger car, b light duty truck, c medium duty vehicle, and d heavy duty vehicle. Abbreviations: 2022 United States Dollars (USD). Source data are provided as a Source Data file.
• Trailing numbers following the figure name (e.g., Sup_Fig9_) represent different scenario values for four variables: [EV adoption, capital cost, electricity price, fuel price]. Each variable is coded with 1 for optimistic, 2 for baseline, and 3 for conservative. For instance, Sup_Fig9_2222 indicates that all four variables are set to baseline levels in Supplementary Fig9.