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@ARTICLE{Zou:878072,
      author       = {Zou, Wei and Froning, Dieter and Shi, Yan and Lehnert,
                      Werner},
      title        = {{A}n online adaptive model for the nonlinear dynamics of
                      fuel cell voltage},
      journal      = {Applied energy},
      volume       = {288},
      issn         = {0306-2619},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2020-02614},
      pages        = {116561 -},
      year         = {2021},
      abstract     = {Polymer electrolyte fuel cells have been widely used in
                      automotive applications, in which fast-response and highly
                      accurate fuel cell systems are required to achieve good
                      performance. To fulfill this requirement, an adaptive fuel
                      cell model is developed herein for a polymer electrolyte
                      fuel cell system. The model is established on the basis of a
                      least squares support vector machine. A genetic algorithm is
                      employed to set the initial values of the internal
                      parameters of the model by incorporating existing data from
                      previous experiments. Then, an adaptive process is further
                      conducted to provide an online update of the model’s
                      internal parameters. The genetic algorithm can effectively
                      avoid the initial parameters by falling to a local minimum.
                      Moreover, the online updating of the parameters makes the
                      model more adaptive to load changes in the real-time
                      application of the fuel cell system. The proposed model is
                      experimentally-tested on a fuel cell test rig. The results
                      indicate that the proposed model can accurately and
                      effectively predict fuel cell voltage. In addition, two
                      reference models are employed to compare with the online
                      adaptive model, by which the advantages of the genetic
                      algorithm and parameter updating are verified. The model
                      accuracy is improved significantly with the genetic
                      algorithm, indicating the importance of initial parameters
                      setting. The gradient method also benefits the model’s
                      accuracy in online modeling and predicting, but its
                      efficiency still depends on the initial parameters. This
                      online adaptive model can easily address frequent load
                      change and the long term operation of fuel cells.},
      cin          = {IEK-14},
      ddc          = {620},
      cid          = {I:(DE-Juel1)IEK-14-20191129},
      pnm          = {123 - Chemische Energieträger (POF4-123)},
      pid          = {G:(DE-HGF)POF4-123},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000634778500008},
      doi          = {10.1016/j.apenergy.2021.116561},
      url          = {https://juser.fz-juelich.de/record/878072},
}