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@PHDTHESIS{Nguyen:1026581,
      author       = {Nguyen, Thuc Anh},
      othercontributors = {Abel, Dirk and Alrifaee, Bassam},
      title        = {{D}esign and tuning of a real-time capable {MPC} for
                      automotive fuel cell systems},
      school       = {Rheinisch-Westfälische Technische Hochschule Aachen},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {RWTH Aachen University},
      reportid     = {RWTH-2026-00978},
      pages        = {1 Online-Ressource : Illustrationen},
      year         = {2025},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University 2026; Dissertation, Rheinisch-Westfälische
                      Technische Hochschule Aachen, 2025},
      abstract     = {Polymer electrolyte membrane fuel cells are emerging as a
                      promising technology for achieving carbon-free mobility,
                      particularly in the automotive sector. In fuel cell-dominant
                      vehicles, highly dynamic operation of the fuel cell system
                      (FCS) is required, while simultaneously maintaining high
                      efficiency and safe operation. This entails avoiding oxygen
                      starvation, compressor surge and choke, as well as ensuring
                      proper membrane hydration, all of which are also critical
                      for system longevity. This thesis presents a model
                      predictive control (MPC) framework designed, implemented,
                      and evaluated for an automotive FCS in a fuel cell-dominant
                      hybrid electric vehicle (FCHEV). The control structure is
                      arranged as a hierarchical MPC scheme, composed of a
                      high-level and a low-level control layer. The high-level
                      control layer includes a power-split nonlinear MPC (NMPC)
                      and a target selector. The former optimizes the power
                      distribution between the FCS and the hybrid battery to
                      maximize FCS efficiency and improve battery charge
                      sustainability. Given this power distribution, the target
                      selector ensures a statically optimal allocation of the
                      compressor operating point, preventing overactuation of the
                      FCS. The high-level control layer, which considers a dynamic
                      model of the battery and a static model of the FCS, operates
                      on a one-second sampling time in line with the power demand
                      dynamics. The low-level control layer comprises a tracking
                      MPC and a state and disturbance estimator. Considering the
                      transient behavior of the FCS, the tracking MPC computes the
                      optimal inputs for FCHEV actuation, with the objective to
                      dynamically track the references provided by the high-level
                      control layer while respecting the system’s operational
                      bounds. The extended Kalman filter estimates internal states
                      of the fuel cell stack and provides a static model error for
                      zero steady-state offset tracking. To capture the transient
                      system behavior, the tracking MPC operates at a faster rate,
                      which poses challenges for real-time feasibility on embedded
                      hardware. In this context, a comparison between linear
                      time-varying MPC and NMPC shows that nonlinear prediction is
                      advantageous under highly dynamic operation, particularly
                      when rapid power changes interact with safety-critical
                      constraints. This motivates a careful design of the NMPC,
                      including the selection of sampling time, numerical
                      integration scheme, and prediction horizon to ensure
                      real-time feasibility. The real-time feasibility of the
                      resulting controller is demonstrated on embedded hardware.
                      The overall MPC framework, including the real-time capable
                      tracking MPC, is tuned using multi-objective Bayesian
                      optimization to balance dynamic capability, system
                      efficiency, and constraint satisfaction. A further
                      contribution of this work is the incorporation of humidity
                      into the tracking MPC, which is essential for accurately
                      capturing the dynamic behavior of the FCS and enabling
                      proper water management.},
      cin          = {416610},
      ddc          = {620},
      cid          = {$I:(DE-82)416610_20140620$},
      pnm          = {GRK 1856 - GRK 1856: Integrierte Energieversorgungsmodule
                      für straßengebundene Elektromobilität (210731724)},
      pid          = {G:(GEPRIS)210731724},
      typ          = {PUB:(DE-HGF)11},
      doi          = {10.18154/RWTH-2026-00978},
      url          = {https://publications.rwth-aachen.de/record/1026581},
}