Using Agent-Based Customer Modeling for the Evaluation of EV Charging Systems
Open accessAbstract
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.
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In colder periods, the EV rated range can be decreased by around 10%.
“For example, in colder periods, the EV rated range can be decreased by around 10%, and high temperatures may affect the battery health.”
From EV Overnight Charging Strategy in Residential Sector: Case of Winter Season in Quebec (2021) · cites this paper as [20]
Section 4.5 (page unavailable) models increased EV energy consumption at lower ambient temperatures, which is consistent with reduced range. It does not state that rated range falls by around 10%, so the magnitude is unsupported. (LLM, conf=0.85)
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- Authors
- Rodemann, T., Eckhardt, T., Unger, R., & Schwan, T.
- Affiliations
- Honda (Germany) (DE)
- EA Systems Dresden GmbH, 01187 Dresden, Germany
- Corresponding author
- Rodemann, Tobias
- ORCID
- Rodemann, Tobias
- Venue
- Energies
- Publisher
- MDPI AG
- Published
- 2019-07-25
- Volume
- 12
- Issue
- 15
- Pages
- 2858
- ISSN
- 1996-1073 (ISSN-L)
- OpenAlex
- W2962860441
- Cited by
- 13
- References
- 22
- Citations by year
- 2026: 1 · 2023: 1 · 2022: 3 · 2021: 3 · 2020: 3 · 2019: 2
- FWCI
- 0.99
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Energies (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Building and Construction › Smart Parking Systems Research
- Keywords
- electric vehicle charging, customer behavior modeling, multi-agent simulation, agent-based modeling, charging infrastructure, customer satisfaction, state of charge
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