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

Title: Modeling plug-in electric vehicle charging demand with BEAM: the framework for behavior energy autonomy mobility

Abstract

This report summarizes the BEAM modeling framework (Behavior, Energy, Mobility, and Autonomy) and its application to simulating plug-in electric vehicle (PEV) mobility, energy consumption, and spatiotemporal charging demand. BEAM is an agent-based model of PEV mobility and charging behavior designed as an extension to MATSim (the Multi-Agent Transportation Simulation model). We apply BEAM to the San Francisco Bay Area and conduct a preliminary calibration and validation of its prediction of charging load based on observed charging infrastructure utilization for the region in 2016. We then explore the impact of a variety of common modeling assumptions in the literature regarding charging infrastructure availability and driver behavior. We find that accurately reproducing observed charging patterns requires an explicit representation of spatially disaggregated charging infrastructure as well as a more nuanced model of the decision to charge that balances tradeoffs people make with regards to time, cost, convenience, and range anxiety.

Authors:
; ; ; ;
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)
OSTI Identifier:
1398472
Report Number(s):
LBNL-2001018
ark:/13030/qt55p1w1vk
DOE Contract Number:  
AC02-05CH11231
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
33 ADVANCED PROPULSION SYSTEMS; 97 MATHEMATICS AND COMPUTING

Citation Formats

Sheppard, Colin, Waraich, Rashid, Campbell, Andrew, Pozdnukov, Alexei, and Gopal, Anand R. Modeling plug-in electric vehicle charging demand with BEAM: the framework for behavior energy autonomy mobility. United States: N. p., 2017. Web. doi:10.2172/1398472.
Sheppard, Colin, Waraich, Rashid, Campbell, Andrew, Pozdnukov, Alexei, & Gopal, Anand R. Modeling plug-in electric vehicle charging demand with BEAM: the framework for behavior energy autonomy mobility. United States. doi:10.2172/1398472.
Sheppard, Colin, Waraich, Rashid, Campbell, Andrew, Pozdnukov, Alexei, and Gopal, Anand R. Mon . "Modeling plug-in electric vehicle charging demand with BEAM: the framework for behavior energy autonomy mobility". United States. doi:10.2172/1398472. https://www.osti.gov/servlets/purl/1398472.
@article{osti_1398472,
title = {Modeling plug-in electric vehicle charging demand with BEAM: the framework for behavior energy autonomy mobility},
author = {Sheppard, Colin and Waraich, Rashid and Campbell, Andrew and Pozdnukov, Alexei and Gopal, Anand R.},
abstractNote = {This report summarizes the BEAM modeling framework (Behavior, Energy, Mobility, and Autonomy) and its application to simulating plug-in electric vehicle (PEV) mobility, energy consumption, and spatiotemporal charging demand. BEAM is an agent-based model of PEV mobility and charging behavior designed as an extension to MATSim (the Multi-Agent Transportation Simulation model). We apply BEAM to the San Francisco Bay Area and conduct a preliminary calibration and validation of its prediction of charging load based on observed charging infrastructure utilization for the region in 2016. We then explore the impact of a variety of common modeling assumptions in the literature regarding charging infrastructure availability and driver behavior. We find that accurately reproducing observed charging patterns requires an explicit representation of spatially disaggregated charging infrastructure as well as a more nuanced model of the decision to charge that balances tradeoffs people make with regards to time, cost, convenience, and range anxiety.},
doi = {10.2172/1398472},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon May 01 00:00:00 EDT 2017},
month = {Mon May 01 00:00:00 EDT 2017}
}

Technical Report:

Save / Share: