Scheduled System Maintenance:
On Monday, April 27th, IEEE Xplore will undergo scheduled maintenance from 1:00 PM - 3:00 PM ET (17:00 - 19:00 UTC). No interruption in service is anticipated.
By Topic

PEV Charging Profile Prediction and Analysis Based on Vehicle Usage Data

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Ashtari, A. ; Dept. of Mech. & Manuf. Eng., Univ. of Manitoba, Winnipeg, MB, Canada ; Bibeau, E. ; Shahidinejad, S. ; Molinski, T.

Present-day urban vehicle usage data recorded on a per second basis over a one-year period using GPS devices installed in 76 representative vehicles in the city of Winnipeg, Canada, allow predicting the electric load profiles onto the grid as a function of time for future plug-in electric vehicles. For each parking occurrence, load profile predictions properly take into account important factors, including actual state-of-charge of the battery, parking duration, parking type, and vehicle powertrain. Thus, the deterministic simulations capture the time history of vehicle driving and parking patterns using an equivalent 10 000 urban driving and parking days for the city of Winnipeg. These deterministic results are then compared to stochastic methods that differ in their treatment of how they model vehicle driving and charging habits. The new stochastic method introduced in this study more accurately captures the relationship of vehicle departure, arrival, and travel time compared to two previously used stochastic methods. It outperforms previous stochastic methods, having the lowest error at 3.4% when compared to the deterministic method for an electric sedan with a 24-kWhr battery pack. For regions where vehicle usage data is not available to predict plug-in electric vehicle load, the proposed stochastic method is recommended. In addition, using a combination of home, work, and commercial changing locales, and Level 1 versus Level 2 charging rates, deterministic simulations for urban run-out-of-charge events vary by less than 4% for seven charging scenarios selected. Using the vehicle usage data, charging scenarios simulated have no significant effect on urban run-out-of-charge events when the battery size for the electric sedan is increased. These results contribute towards utilities achieve a more optimal cost balance between: 1) charging infrastructure; 2) power transmission upgrades; 3) vehicle battery size; and 4) the addition of new renewable generation to add- ess new electric vehicle loads for addressing energy drivers.

Published in:

Smart Grid, IEEE Transactions on  (Volume:3 ,  Issue: 1 )