An agent-based electric vehicle ecosystem model: San Francisco case study

Authored by Adedamola Adepetu, Srinivasan Keshav, Vijay Arya

Date Published: 2016

DOI: 10.1016/j.tranpol.2015.11.012

Sponsors: No sponsors listed

Platforms: Python

Model Documentation: Other Narrative Flow charts Mathematical description

Model Code URLs: Model code not found

Abstract

The widespread commercial availability of plug-in electric vehicles (EVs) in recent years motivates policies to encourage EV adoption and infrastructure to cope with the increasing number of EVs. We present an agent-based EV ecosystem model that incorporates EV adoption and usage with spatial and temporal considerations and that can aid different EV industry stakeholders such as policymakers, utility operators, charging station planners, and EV manufacturers. The model follows an ecological modeling approach, and is used to determine how different policies and battery technologies affect EV adoption, EV charging, and charging station activity. We choose model parameters to fit San Francisco as a test city and simulate different scenarios. The results provide insight on potential changes to the San Francisco EV ecosystem as a result of changes in rebates, availability of workplace charging, public awareness of lower EV operational costs, and denser EV batteries. We find that our results match those obtained using other approaches and that the compact geographical size of San Francisco and its relative wealth make it an ideal city for EV adoption. (C) 2015 Elsevier Ltd. All rights reserved.

Tags

Demand Plug-in hybrid