@article {pub3999,
	title = {Using Agent-based Customer Modelling for the Evaluation of EV Charging Systems},
	author = {Tobias Rodemann AND Tom Eckhardt  AND Ren{\'e} Unger AND Torsten Schwan},
	year = {2019},
	month = {July},
	abstract = {The development of efficient Electric Vehicle (EV) charging infrastructure requires a modeling of customer behavior at an appropriate level of detail.  Since only limited information about real customers is available, most simulation approaches employ a stochastic approach by combining known or estimated customer features with random variations. A typical example is to model EV charging customers by an arrival and a targeted departure time, plus the requested amount of energy or increased state-of-charge (SOC), where values are drawn from normal (Gaussian) distributions with mean and variance values derived from user studies of obviously limited sample size.
In this work we compare this basic approach with a more detailed customer model employing a Multi-Agent Simulation (MAS) framework in order to investigate  how a customer behavior that responds to external factors (like weather) or historical data (like satisfaction on past charging sessions) impacts on essential key performance indicators of the charging system. Our findings show that small changes in the way customers are modeled can  lead to quantitative and qualitative differences in the simulated performance of EV charging systems.},
	publisher = {MDPI},
	journal = {Energies},
	edition = {Multi-Agent Energy System Simulations},
	volume = {12},
	number = {15}
}