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@PHDTHESIS{Fabender:1029073,
      author       = {Faßbender, Max},
      othercontributors = {Andert, Jakob Lukas and von der Aßen, Niklas Vincenz},
      title        = {{I}ntelligent management of mobile robot fleets for
                      electric vehicle charging},
      school       = {Rheinisch-Westfälische Technische Hochschule Aachen},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {RWTH Aachen University},
      reportid     = {RWTH-2026-02075},
      pages        = {1 Online-Ressource : Illustrationen},
      year         = {2026},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University; Dissertation, Rheinisch-Westfälische Technische
                      Hochschule Aachen, 2026},
      abstract     = {The increasing spread of electric vehicles requires a
                      flexible and efficient charging infrastructure. Mobile
                      charging robots can be a useful addition to stationary
                      charging stations, as they increase flexibility and improve
                      user convenience. However, controlling and coordinating
                      these charging robots is a complex optimization task, as
                      both the charging power and the assignment of the robots to
                      the vehicles must be taken into account. Nevertheless, there
                      are hardly any strategies for the operation of mobile
                      charging robots in the literature to date. This thesis
                      investigates the use of model predictive control (MPC) and
                      reinforcement learning (RL) to optimize charging management
                      with mobile charging robots in this context. For this
                      purpose, a simulation environment was developed in the
                      Python programming language, which enables a realistic
                      representation of a parking lot with the help of the
                      Gymnasium Toolbox. The modeling includes a network of
                      parking spaces, electric vehicles, stationary charging
                      stations and mobile charging robots. These robots inherit
                      characteristics of electric vehicles and charging stations,
                      respectively. The local electricity grid with variable
                      electricity prices, building loads and a photovoltaic system
                      are also taken into account. To implement the control
                      strategies, the RL agent was trained using the RLlib
                      software framework, while the MPC optimization was modeled
                      using the Pyomo toolbox and solved using the Gurobi solver.
                      The challenge of the RL approach lies in the decomposition
                      of the problem in order to adequately deal with the large
                      observation and action space. In contrast, the MPC approach
                      focuses on the efficient formulation of the optimization
                      problem and the integration of a suitable prediction. A
                      rule-based controller is used as a reference for the two
                      approaches. The evaluation is carried out over four weeks
                      spread over the year as an example in order to reflect
                      seasonal differences in electricity generation and demand.
                      The rule-based reference approach achieves the highest gains
                      but the lowest user satisfaction across all scenarios. The
                      profits of the other two approaches are slightly lower,
                      mainly due to the lower amount of charged energy. In two of
                      the four weeks, the MPC approach achieves a higher economic
                      profit than the RL approach, while the RL approach delivers
                      a better result in the winter simulation study. In the fall
                      scenario, the economic gains of the RL and MPC approaches
                      are similar. Overall, the RL approach shows certain
                      advantages compared to the MPC approach in terms of user
                      satisfaction. In summary, this thesis examines charging
                      management strategies for electric vehicles, taking into
                      account mobile charging robots. The focus is on maximizing
                      profit and user satisfaction.},
      cin          = {422320 ; 422310},
      ddc          = {620},
      cid          = {$I:(DE-82)422320_20210420$},
      pnm          = {BMWK 01MV21019A - Verbundprojekt: GINI - Roboter für
                      flexibles automatisches Laden von Elektrofahrzeugen;
                      Teilvorhaben: Konzeptentwicklung und Aufbau Primo-/Prototyp
                      (01MV21019A) / XL-Connect - Large scale system approach for
                      advanced charging solutions (101056756)},
      pid          = {G:(BMWK)01MV21019A / G:(EU-Grant)101056756},
      typ          = {PUB:(DE-HGF)11},
      doi          = {10.18154/RWTH-2026-02075},
      url          = {https://publications.rwth-aachen.de/record/1029073},
}