1563 Copyright © 2015, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 75 DOI: 10.4018/978-1-4666-8473-7.ch075 Adding Electric Vehicle Modeling Capability to an Agent-Based Transport Simulation ABSTRACT Battery-electric and plug-in hybrid-electric vehicles are envisioned by many as a way to reduce CO 2 traf- fc emissions, support the integration of renewable electricity generation, and increase energy security. Electric vehicle modeling is an active feld of research, especially with regards to assessing the impact of electric vehicles on the electricity network. However, as highlighted in this chapter, there is a lack of capability for detailed electricity demand and supply modeling. One reason for this, as pointed out in this chapter, is that such modeling requires an interdisciplinary approach and a possibility to reuse and integrate existing models. In order to solve this problem, a framework for electric vehicle modeling is presented, which provides strong capabilities for detailed electricity demand modeling. It is built on an agent-based travel demand and trafc simulation. A case study for the city of Zurich is presented, which highlights the capabilities of the framework to uncover possible bottlenecks in the electricity network and detailed feet simulation for CO 2 emission calculations, and thus its power to support policy makers in taking decisions. INTRODUCTION Battery and Plug-in Hybrid Electric Vehicles (BEV resp. PHEV) are seen by many as a key component to a future transport sector with lower greenhouse gas emissions. These vehicles do not only have a more efficient driving cycle than conventional vehicles, but also allow a diversi- fication of energy sources for driving (MacKay, 2008). BEV and PHEV are abbreviated to electric vehicles (EV), with the exception of cases where the distinction is required. Rashid A. Waraich ETH Zurich, Switzerland Gil Georges ETH Zurich, Switzerland Matthias D. Galus ETH Zurich, Switzerland Kay W. Axhausen ETH Zurich, Switzerland