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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.authorSophia Liu, Yuechen
dc.contributor.authorTayarani, Mohammad
dc.contributor.authorGao, H. Oliver
dc.date.accessioned2022-02-15T16:45:36Z
dc.date.available2022-02-15T16:45:36Z
dc.date.issued2021-08-20
dc.descriptionFinal Reporten_US
dc.description.abstractThe expansion of the battery electric vehicle (BEV) market requires considerable changes in the supply of electricity to fulfill the charging demand. To this end, understanding the spatio-temporal distribution of BEV charging demand at a micro-level is crucial for optimal electric vehicle supply equipment (EVSE) planning and electricity load management. This research proposes an integrated activity-based BEV charging demand simulation model, which considers both realistic travel and charging behaviors and provides high-resolution spatio-temporal demand in real-world applications. Moreover, a novel charging choice model is proposed which provides more realistic demand modeling by allowing critical non-linearities in random utility to better describe observed charging behaviors. The results of a case study for the Atlanta metropolitan area imply that work/public charging has a substantial potential market, which can serve up to 64.5% of the total demand. Out of multiple charging modes, demand for direct-current fast charging (DCFC) is prominent at work/public, and it takes the largest portion of the non-residential demand in all simulation scenarios. Moreover, charging behaviors have significant impacts on the demand distribution. Comparing to risk-neutral users, high-risk sensitive users require 49% to 91% higher peak power demand of level 2 chargers at work/public. Users' preferences for fast charging rates can change DCFC demand from 36.4% to 53.7% of the total demand. This study helps to qualitatively analyze the factors of charging demand and their impacts on the demand distribution. The results can be directly used in EVSE planning and electricity load prediction.en_US
dc.description.sponsorshipU.S. Department of Transportation 69A3551747119en_US
dc.identifier.urihttps://hdl.handle.net/1813/110961
dc.language.isoen_USen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectBattery electric vehicleen_US
dc.subjectCharging demanden_US
dc.subjectSpatial-temporal distributionen_US
dc.subjectTrip chain simulationen_US
dc.subjectCharging behavior modelen_US
dc.titleAn Agent-based Travel and Charging Behavior Model for Forecasting High-resolution Spatio-temporal Battery Electric Vehicle Charging Demanden_US
dc.typereporten_US
schema.accessibilityFeaturereadingOrderen_US
schema.accessibilityFeaturestructuralNavigationen_US
schema.accessibilityFeaturetaggedPDFen_US
schema.accessibilityHazardunknownen_US

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