skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Mesoscopic approach to modeling electric vehicle fleets based upon driving activity data to investigate recharge strategies’ impact on grid loads

Abstract

This paper presents a mesoscopic model to populate a set of sub-fleets of battery electric vehicles (BEVs) that simulates how grid power demand changes with varied mixes of different charging strategies. The proposed model considers real driving activities of vehicles based on the National Household Travel Survey (NHTS) and various charging strategies while parked (at home and in other locations). To be easily scaled to any fleet size, a mesoscopic model is implemented, where BEVs with the same physical properties and driving schedules and clustered together into sub-fleets. The degree of granularity is explored for stable results. Monte Carlo simulations were run to demonstrate the potential of this model's use in grid loads under various charging strategies. Furthermore, an example cost function was optimized to demonstrate finding optimal allocations of charging strategies.

Authors:
;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1556922
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Conference
Resource Relation:
Conference: 2019 IEEE Transportation Electrification Conference and Expo, 06/19/19 - 06/21/19, Novi, MI, US
Country of Publication:
United States
Language:
English
Subject:
Electric Vehicles, Grid Integration, Monte Carlo Simulations

Citation Formats

Duoba, Michael, and Fernandez Canosa, Alejandro. Mesoscopic approach to modeling electric vehicle fleets based upon driving activity data to investigate recharge strategies’ impact on grid loads. United States: N. p., 2019. Web. doi:10.1109/ITEC.2019.8790617.
Duoba, Michael, & Fernandez Canosa, Alejandro. Mesoscopic approach to modeling electric vehicle fleets based upon driving activity data to investigate recharge strategies’ impact on grid loads. United States. doi:10.1109/ITEC.2019.8790617.
Duoba, Michael, and Fernandez Canosa, Alejandro. Wed . "Mesoscopic approach to modeling electric vehicle fleets based upon driving activity data to investigate recharge strategies’ impact on grid loads". United States. doi:10.1109/ITEC.2019.8790617.
@article{osti_1556922,
title = {Mesoscopic approach to modeling electric vehicle fleets based upon driving activity data to investigate recharge strategies’ impact on grid loads},
author = {Duoba, Michael and Fernandez Canosa, Alejandro},
abstractNote = {This paper presents a mesoscopic model to populate a set of sub-fleets of battery electric vehicles (BEVs) that simulates how grid power demand changes with varied mixes of different charging strategies. The proposed model considers real driving activities of vehicles based on the National Household Travel Survey (NHTS) and various charging strategies while parked (at home and in other locations). To be easily scaled to any fleet size, a mesoscopic model is implemented, where BEVs with the same physical properties and driving schedules and clustered together into sub-fleets. The degree of granularity is explored for stable results. Monte Carlo simulations were run to demonstrate the potential of this model's use in grid loads under various charging strategies. Furthermore, an example cost function was optimized to demonstrate finding optimal allocations of charging strategies.},
doi = {10.1109/ITEC.2019.8790617},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {6}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share: