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@ARTICLE{Zou:863998,
      author       = {Zou, Wei and Froning, Dieter and Lu, X. J. and Lehnert,
                      Werner},
      title        = {{A}n {O}nline {S}patiotemporal {T}emperature {M}odel for
                      {H}igh {T}emperature {P}olymer {E}lectrolyte {F}uel {C}ells},
      journal      = {Energy conversion and management},
      volume       = {199},
      issn         = {0196-8904},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2019-03914},
      pages        = {111974 -},
      year         = {2019},
      abstract     = {This paper focuses on the thermal behavior estimation for
                      high temperature polymer electrolyte fuel cells (HT-PEFCs),
                      which can be used for predicting stack temperature and
                      developing a reliable thermal controller. To overcome the
                      complex nonlinear, time-space-distributed properties in fuel
                      cell stacks, an online spatiotemporal temperature model is
                      developed here. This model is based on the least squares
                      support vector machine (LS-SVM), which has the ability to
                      approximate any nonlinear system by a simple model
                      structure. In the proposed method, the spatial correlations
                      across different locations are fully represented by the
                      kernel function, and then the temporal features are further
                      modeled as a function of the inlet flow rate, partial
                      pressure on each side and current density. After that,
                      integrating the spatial kernel function and temporal
                      dynamics function, the spatiotemporal temperature model is
                      then constructed. The proposed method is tested by
                      simulation in Matlab, with a comparison of the experimental
                      data collected from an HT-PEFC test rig. The modeling result
                      demonstrates that the developed model can effectively and
                      precisely predict HT-PEFC thermal behavior. The present
                      study is of key importance and may help as a black box for
                      future development of new optimization and control
                      strategies.},
      cin          = {IEK-3},
      ddc          = {620},
      cid          = {I:(DE-Juel1)IEK-3-20101013},
      pnm          = {135 - Fuel Cells (POF3-135)},
      pid          = {G:(DE-HGF)POF3-135},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000494884000102},
      doi          = {10.1016/j.enconman.2019.111974},
      url          = {https://juser.fz-juelich.de/record/863998},
}