EVs@Scale Next-Gen Profiles - EV Profile Capture 2024

Program Document · Thu Jan 30 04:00:00 EST 2025

OSTI ID:2572891

Thurston, Sam; Wells, Landon; Ratrout, Sadam; Tolbert, Isaac; Gadamsetty, Pranav; Davidson, Keith; Kogalur, Namrata; Schmidt, Steven Knudson

As part of the U.S. DOE EVs@Scale consortium Next-Gen Profiles (NGP) project, the profile capture and analysis of production electric vehicles undergoing high power charging (HPC) is conducted over a wide range of conditions to explore variance and performance. Charge session parameters are collected from both the electric vehicle (EV) and electric vehicle supply equipment (EVSE) at a rate of 10Hz and entered into a time-series database for analysis. These charge profiles are captured under nominal and off-nominal conditions, exploring the impact of battery state of charge (SOC), battery temperature, vehicle condition, smart charge management (SCM), and EVSE limitations. Nominal conditions are defined to be ideal conditions that should transfer the maximum allowable energy in the minimum possible amount of time. Nominal condition profiles are compared across EVs to characterize state-of-the-art EV charging performance against one another. Off-nominal condition profiles are compared against its nominal condition profile counterpart to highlight the variance across less desirable starting conditions within a single EV. This EV Profile Capture 2024 report stands as an update from the EV Profile Capture 2023 report to include the additional EV & EVSE assets tested and analyzed in 2024. The major updates within this report include the addition of three next-generation electric vehicles, added test cases, and further analysis. This expansion of analysis includes power profiles, power distribution, quantifying SOC, energy and range performance, EVSE limitation impacts, boost converter performance, etc. Additionally, NGP time-series data has been used as input towards three national laboratory-led grid modelling efforts: ANL’s IEEE-37 HIL model, INL’s Caldera model, and NREL’s EVI-X model. A summary of these platforms and how NGP has worked to improve their effectiveness has also been added to this years’ report.

• Research Organization: Argonne National Laboratory (ANL)
• Sponsoring Organization: US Department of Energy; USDOE Office of Energy Efficiency and Renewable Energy (EERE) - Office of Vehicle Technologies (VTO)
• DOE Contract Number: AC02-06CH11357
• OSTI ID: 2572891
• Report Number(s): ANL/TAPS-25/2; 194536
• Country of Publication: United States
• Language: English