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

TitleModeling plug-in electric vehicle charging demand with BEAM, the framework for behavior energy autonomy mobility
Publication TypeReport
LBNL Report Number2017_EV_BEAM
Year of Publication2017
AuthorsSheppard, Colin, Rashid Waraich, Anand R. Gopal, Andrew Campbell, and Alexei Pozdnukov
Date Published05/2017
Keywordsbeam, electric vehicles, transportation
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.

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