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@PHDTHESIS{Hoppe:696127,
      author       = {Hoppe, Matthias},
      othercontributors = {Abel, Dirk and Brabetz, Ludwig},
      title        = {{M}odellbasierte {E}ntwicklung und {A}pplikation von
                      {D}iagnosefunktionen im {K}ühlkreislauf des
                      {K}raftfahrzeugs},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Aachen},
      reportid     = {RWTH-2017-06644},
      pages        = {1 Online-Ressource (xvii, 139 Seiten) : Illustrationen,
                      Diagramme},
      year         = {2017},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University; Dissertation, RWTH Aachen University, 2017},
      abstract     = {The focus of this work lies on model-based methods for the
                      development and the calibration of diagnosis functions.
                      Using model-based calibration methods in the development of
                      diagnosis functions reduces the required time significantly.
                      A model-based diagnosis function allows an isolation of
                      errors, which is not possible with diagnosis functions
                      currently in use. The temperature diagnosis in the cooling
                      system of a vehicle is used as an example application. In a
                      first step, a simulation model of the cooling system is
                      developed. For this model, a balance between the accuracy of
                      the model and the calculation time has to be found. For the
                      engine, a static combustion model is combined with a dynamic
                      model of the heat transfer. For the elements in the outer
                      cooling circuit, a static model using maps is compared with
                      a physical, dynamic model. For the calibration of the maps
                      used in the diagnosis, two methods are described. On the one
                      hand, parametric methods like a least squares algorithm can
                      be used. On the other hand, a direct optimization with the
                      help of evolutionary algorithms is presented. Both
                      algorithms are further extended, in order to satisfy
                      additional requirements, like a smooth surface of the maps.
                      For the development of a model-based diagnosis function,
                      several sensor configurations are investigated in regard to
                      the isolation of errors with the help of a structural
                      analysis. A diagnosis function is implemented for a suitable
                      sensor configuration. The functionability is tested in
                      simulation.},
      cin          = {416610},
      ddc          = {620},
      cid          = {$I:(DE-82)416610_20140620$},
      typ          = {PUB:(DE-HGF)11},
      doi          = {10.18154/RWTH-2017-06644},
      url          = {https://publications.rwth-aachen.de/record/696127},
}