An Agent-based Travel and Charging Behavior Model for Forecasting High-resolution Spatio-temporal Battery Electric Vehicle Charging Demand

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dc.contributor.authorGao, H. Oliver
dc.date.accessioned2022-02-14T16:29:47Z
dc.date.available2022-02-14T16:29:47Z
dc.date.issued2021-08-20
dc.descriptionProject Descriptionen_US
dc.description.abstractThe novelties of this work are twofold. First, we proposed an agent-based battery electric vehicle charging demand simulation model integrating travel and charging behaviors, which was able to estimate the high-resolution spatiotemporal distribution of charging demand. Second, we constructed a novel charging behavior model for charging mode choice, which was able to capture non-linear charges in random utility, and the impact on charging choice of various factors, namely risk sensitivity, range buffer, and preference for charging rate. It focused on the modeling and forecasting of renewable energy consumption in the transportation sector, which could be directly applied in the optimal design of energy supply systems and the modeling framework allowed it to be generally adopted for broad application.en_US
dc.description.sponsorshipU.S. Department of Transportation 69A3551747119en_US
dc.identifier.urihttps://hdl.handle.net/1813/110952
dc.language.isoen_USen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleAn Agent-based Travel and Charging Behavior Model for Forecasting High-resolution Spatio-temporal Battery Electric Vehicle Charging Demanden_US
dc.typefact sheeten_US
schema.accessibilityFeaturereadingOrderen_US
schema.accessibilityFeaturestructuralNavigationen_US
schema.accessibilityFeaturetaggedPDFen_US
schema.accessibilityHazardunknownen_US
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