An activity-based travel and charging behavior model for simulating battery electric vehicle charging demand. (1st November 2022)

• Record Type: Journal Article
• Authors: Liu, Yuechen Sophia; Tayarani, Mohammad; Gao, H. Oliver
• Is Part Of: Energy. Volume 258(2022)
• Publication Date: 2022-11-01
• Subjects: Activity-based model -- Battery electric vehicle -- Charging behavior model -- Charging demand -- Spatio-temporal distribution -- Trip chain; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.124938
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: The 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 comprise 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 locations, and it makes up the largest portion of the non-residential demand in all simulation scenarios. Moreover, charging behaviors have significant impacts on the demand distribution. Peak power demand for use of level-2 chargers is 49% to 91% higher among high-risk-sensitive users than among risk-neutral users. Users' preferences for fast charging rates can change the DCFC demand from 36.4% of the total demand to 53.7% of the total. This study helps to qualitatively analyze the factors that figure in charging demand and their impacts on the demand distribution. The results can be directly used in EVSE planning and electricity load prediction. Highlights: A novel charging behavior model to capture non-linear random utility. Provide high-resolution spatio-temporal charging demand. Non-residential charging can comprise up to 64.5% of the total demand. Direct-current fast charging (DCFC) demand is prominent in all scenarios. High-risk-sensitive users shift demand from home to non-residential locations by using more level-2 chargers.