"Womp Womp! Your browser does not support canvas :'("

Demand-Side Grid (dsgrid) TEMPO Light-Duty Vehicle Charging Profiles v2022

Publicly accessible License 

Simulated hourly electric vehicle charging profiles for light-duty household passenger vehicles in the contiguous United States, 2018-2050. Profiles are differentiated by scenario, county, household and vehicle types, and charging type. Data was produced in 2022 using the Transportation Energy & Mobility Pathway Options (TEMPO) model and published in demand-side grid (dsgrid) toolkit format.

Data are available for three adoption scenarios: "AEO Reference Case", which is aligned with the U.S. EIA Annual Energy Outlook 2018 (linked below), "EFS High Electrification", which is aligned with the High Electrification scenario of the Electrification Futures Study (linked below), and "All EV Sales by 2035", which assumes that average passenger light-duty EV sales reach 50% in 2030 and 100% in 2035.

The charging shapes are derived from two key assumptions of which data users should be aware: "ubiquitous charger access", meaning that drivers of vehicles are assumed to have access to a charger whenever a trip is not in progress, and "immediate charging", meaning that immediately after trip completion, vehicles are plugged in and charge until they are either fully recharged or taken on another trip.

These assumptions result in a bounding case in which vehicles' state of charge is maximized at all times. This bounding case would minimize range anxiety, but is unrealistic from the point of view of both electric vehicle service equipment (EVSE) (i.e., charger) access, and plug-in behavior as it can result in dozens of charging sessions per week for battery electric vehicles (BEVs) that in reality are often only plugged in a few times per week.

Citation Formats

TY - DATA AB - Simulated hourly electric vehicle charging profiles for light-duty household passenger vehicles in the contiguous United States, 2018-2050. Profiles are differentiated by scenario, county, household and vehicle types, and charging type. Data was produced in 2022 using the Transportation Energy & Mobility Pathway Options (TEMPO) model and published in demand-side grid (dsgrid) toolkit format. Data are available for three adoption scenarios: "AEO Reference Case", which is aligned with the U.S. EIA Annual Energy Outlook 2018 (linked below), "EFS High Electrification", which is aligned with the High Electrification scenario of the Electrification Futures Study (linked below), and "All EV Sales by 2035", which assumes that average passenger light-duty EV sales reach 50% in 2030 and 100% in 2035. The charging shapes are derived from two key assumptions of which data users should be aware: "ubiquitous charger access", meaning that drivers of vehicles are assumed to have access to a charger whenever a trip is not in progress, and "immediate charging", meaning that immediately after trip completion, vehicles are plugged in and charge until they are either fully recharged or taken on another trip. These assumptions result in a bounding case in which vehicles' state of charge is maximized at all times. This bounding case would minimize range anxiety, but is unrealistic from the point of view of both electric vehicle service equipment (EVSE) (i.e., charger) access, and plug-in behavior as it can result in dozens of charging sessions per week for battery electric vehicles (BEVs) that in reality are often only plugged in a few times per week. AU - Yip, Arthur A2 - Hoehne, Christopher A3 - Jadun, Paige A4 - Ledna, Catherine A5 - Hale, Elaine A6 - Muratori, Matteo A7 - Thom, Daniel A8 - Mooney, Meghan A9 - Liu, Lixi DB - Open Energy Data Initiative (OEDI) DP - Open EI | National Renewable Energy Laboratory DO - 10.25984/2373091 KW - energy KW - power KW - TEMPO KW - dsgrid KW - demand-side grid KW - demand model KW - transportation KW - electric vehicle KW - EV KW - electrification KW - bulk power systems KW - energy modeling KW - projection KW - long-term load forecasting KW - electric vehicle charging KW - passenger vehicles KW - model KW - charging load KW - United States KW - computational science LA - English DA - 2023/08/29 PY - 2023 PB - National Renewable Energy Laboratory T1 - Demand-Side Grid (dsgrid) TEMPO Light-Duty Vehicle Charging Profiles v2022 UR - https://doi.org/10.25984/2373091 ER -
Export Citation to RIS
Yip, Arthur, et al. Demand-Side Grid (dsgrid) TEMPO Light-Duty Vehicle Charging Profiles v2022. National Renewable Energy Laboratory, 29 August, 2023, Open Energy Data Initiative (OEDI). https://doi.org/10.25984/2373091.
Yip, A., Hoehne, C., Jadun, P., Ledna, C., Hale, E., Muratori, M., Thom, D., Mooney, M., & Liu, L. (2023). Demand-Side Grid (dsgrid) TEMPO Light-Duty Vehicle Charging Profiles v2022. [Data set]. Open Energy Data Initiative (OEDI). National Renewable Energy Laboratory. https://doi.org/10.25984/2373091
Yip, Arthur, Christopher Hoehne, Paige Jadun, Catherine Ledna, Elaine Hale, Matteo Muratori, Daniel Thom, Meghan Mooney, and Lixi Liu. Demand-Side Grid (dsgrid) TEMPO Light-Duty Vehicle Charging Profiles v2022. National Renewable Energy Laboratory, August, 29, 2023. Distributed by Open Energy Data Initiative (OEDI). https://doi.org/10.25984/2373091
@misc{OEDI_Dataset_5958, title = {Demand-Side Grid (dsgrid) TEMPO Light-Duty Vehicle Charging Profiles v2022}, author = {Yip, Arthur and Hoehne, Christopher and Jadun, Paige and Ledna, Catherine and Hale, Elaine and Muratori, Matteo and Thom, Daniel and Mooney, Meghan and Liu, Lixi}, abstractNote = {Simulated hourly electric vehicle charging profiles for light-duty household passenger vehicles in the contiguous United States, 2018-2050. Profiles are differentiated by scenario, county, household and vehicle types, and charging type. Data was produced in 2022 using the Transportation Energy & Mobility Pathway Options (TEMPO) model and published in demand-side grid (dsgrid) toolkit format.

Data are available for three adoption scenarios: "AEO Reference Case", which is aligned with the U.S. EIA Annual Energy Outlook 2018 (linked below), "EFS High Electrification", which is aligned with the High Electrification scenario of the Electrification Futures Study (linked below), and "All EV Sales by 2035", which assumes that average passenger light-duty EV sales reach 50% in 2030 and 100% in 2035.

The charging shapes are derived from two key assumptions of which data users should be aware: "ubiquitous charger access", meaning that drivers of vehicles are assumed to have access to a charger whenever a trip is not in progress, and "immediate charging", meaning that immediately after trip completion, vehicles are plugged in and charge until they are either fully recharged or taken on another trip.

These assumptions result in a bounding case in which vehicles' state of charge is maximized at all times. This bounding case would minimize range anxiety, but is unrealistic from the point of view of both electric vehicle service equipment (EVSE) (i.e., charger) access, and plug-in behavior as it can result in dozens of charging sessions per week for battery electric vehicles (BEVs) that in reality are often only plugged in a few times per week. }, url = {https://data.openei.org/submissions/5958}, year = {2023}, howpublished = {Open Energy Data Initiative (OEDI), National Renewable Energy Laboratory, https://doi.org/10.25984/2373091}, note = {Accessed: 2025-04-27}, doi = {10.25984/2373091} }
https://dx.doi.org/10.25984/2373091

Details

Data from Aug 29, 2023

Last updated Jun 17, 2024

Submitted Jun 12, 2024

Organization

National Renewable Energy Laboratory

Contact

Elaine T. Hale

303.384.7812

Authors

Arthur Yip

National Renewable Energy Laboratory

Christopher Hoehne

National Renewable Energy Laboratory

Paige Jadun

National Renewable Energy Laboratory

Catherine Ledna

National Renewable Energy Laboratory

Elaine Hale

National Renewable Energy Laboratory

Matteo Muratori

National Renewable Energy Laboratory

Daniel Thom

National Renewable Energy Laboratory

Meghan Mooney

National Renewable Energy Laboratory

Lixi Liu

National Renewable Energy Laboratory

DOE Project Details

Project Name Demand-side Grid (dsgrid)

Project Number 29323

Share

Submission Downloads