Using Agent-Based Customer Modeling for the Evaluation of EV Charging Systems

Tobias Rodemann, Tom Eckhardt, René Unger and Torsten Schwan
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Tobias Rodemann: Honda Research Institute Europe GmbH, 63073 Offenbach am Main, Germany
Tom Eckhardt: EA Systems Dresden GmbH, 01187 Dresden, Germany
René Unger: EA Systems Dresden GmbH, 01187 Dresden, Germany
Torsten Schwan: EA Systems Dresden GmbH, 01187 Dresden, Germany

Energies, 2019, vol. 12, issue 15, 1-16

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 in past charging sessions) impacts the 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.

Keywords: EV charging; multi-agent system; digital twin; customer satisfaction indicator (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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