SEVA: A Data driven model of Electric Vehicle Charging Behavior

Abstract

Governments and cities around the world are currently facing rapid growth in the use of Electric Vehicles and therewith the need for Charging Infrastructure. For these cities, the struggle remains how to further roll out charging infrastructure in the most efficient way, both in terms of cost and use. Forecasting models are not able to predict more long-term developments, and as such more complex simulation models offer opportunities to simulate various scenarios. Agent based simulation models provide insight into the effects of incentives and roll-out strategies before they are implemented in practice and thus allow for scenario testing. This paper describes the build up of an agent based model that enables policy makers to anticipate on charging infrastructure development. The model is able to simulate charging transactions of individual users and is both calibrated and validated using a dataset of charging transactions from the public charging infrastructure of the four largest cities in the Netherlands.