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An Electric Vehicle Charging Simulation to Investigate the Potential of Intelligent Charging Strategies

Author

Listed:
  • Max Faßbender

    (Teaching and Research Area Mechatronics in Mobile Propulsion, Faculty of Mechanical Engineering, RWTH Aachen University, 52062 Aachen, Germany)

  • Nicolas Rößler

    (Teaching and Research Area Mechatronics in Mobile Propulsion, Faculty of Mechanical Engineering, RWTH Aachen University, 52062 Aachen, Germany)

  • Markus Eisenbarth

    (Teaching and Research Area Mechatronics in Mobile Propulsion, Faculty of Mechanical Engineering, RWTH Aachen University, 52062 Aachen, Germany)

  • Jakob Andert

    (Teaching and Research Area Mechatronics in Mobile Propulsion, Faculty of Mechanical Engineering, RWTH Aachen University, 52062 Aachen, Germany)

Abstract

As electric vehicle (EV) adoption grows, efficient and accessible charging infrastructure is essential. This paper introduces a modular simulation environment to evaluate charging point configurations and operational strategies. The simulation incorporates detailed models of electrical consumers and user behaviour, leveraging real-world data to simulate charging scenarios. A rule-based control strategy is applied to assess six configurations for a supermarket parking lot charging point. Key findings include the highest profit being achieved with two fast chargers. In scenarios with a 50 kW grid connection limit, combining fast chargers with stationary battery storage proves effective. Conversely, mobile charging robots generate lower revenue, though grid peak limitations have minimal impact. The study highlights the potential of the simulation environment to optimise charging layouts, refine operational strategies, and develop energy management algorithms. This work demonstrates the utility of the simulation framework for analyzing diverse charging solutions, offering insights into cost efficiency and user satisfaction. The results emphasise the importance of tailored strategies to balance grid constraints, profitability, and user needs, paving the way for intelligent EV charging infrastructure development.

Suggested Citation

  • Max Faßbender & Nicolas Rößler & Markus Eisenbarth & Jakob Andert, 2025. "An Electric Vehicle Charging Simulation to Investigate the Potential of Intelligent Charging Strategies," Energies, MDPI, vol. 18(11), pages 1-31, May.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:11:p:2778-:d:1665249
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    References listed on IDEAS

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    1. Morstyn, Thomas & Collett, Katherine A. & Vijay, Avinash & Deakin, Matthew & Wheeler, Scot & Bhagavathy, Sivapriya M. & Fele, Filiberto & McCulloch, Malcolm D., 2020. "OPEN: An open-source platform for developing smart local energy system applications," Applied Energy, Elsevier, vol. 275(C).
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    3. Dorokhova, Marina & Martinson, Yann & Ballif, Christophe & Wyrsch, Nicolas, 2021. "Deep reinforcement learning control of electric vehicle charging in the presence of photovoltaic generation," Applied Energy, Elsevier, vol. 301(C).
    4. Maria-Simona Răboacă & Irina Băncescu & Vasile Preda & Nicu Bizon, 2020. "An Optimization Model for the Temporary Locations of Mobile Charging Stations," Mathematics, MDPI, vol. 8(3), pages 1-20, March.
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    Cited by:

    1. Max Faßbender & Nicolas Rößler & Christoph Wellmann & Markus Eisenbarth & Jakob Andert, 2025. "Model Predictive Control for Charging Management Considering Mobile Charging Robots," Energies, MDPI, vol. 18(15), pages 1-22, July.

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