% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Xu:810849,
      author       = {Xu, Liangfei and Fang, Chuan and Hu, Junming and Cheng,
                      Siliang and Li, Jianqiu and Quyang, Minggao and Lehnert,
                      Werner},
      title        = {{P}arameter extraction and uncertainty analysis of a proton
                      exchange membrane fuel cell system based on {M}onte {C}arlo
                      simulation},
      journal      = {International journal of hydrogen energy},
      volume       = {42},
      number       = {4},
      issn         = {0360-3199},
      address      = {New York, NY [u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2016-03432},
      pages        = {2309 - 2326},
      year         = {2017},
      abstract     = {Recently, there has been rapid development in the field of
                      proton exchange membrane fuel cell (PEMFC) systems for
                      transportation applications. The performance of a PEMFC
                      system is very sensitive to the operating conditions, and
                      uncontrolled working conditions may cause malfunctions and
                      degradation. A robust control strategy is urgently needed,
                      in order to improve the reliability of PEMFCs and prolong
                      their working lifetime. To develop such a control strategy,
                      one needs to not only model the system with identified
                      parameters, but also know their uncertainties. In most
                      studies related to system uncertainties, however, the
                      parameter uncertainty is usually regarded as a pre-known
                      condition. This paper proposes a method to identify key
                      parameters and their boundaries of PEMFCs, and analyze the
                      uncertainties of internal states based on Monte Carlo
                      simulation. A nonlinear isothermal dynamic model, which
                      takes into account the filling-and-emptying dynamic
                      sub-models and a sub-model of mass transport through the
                      membrane, is firstly introduced. Key parameters are then
                      extracted stepwise using a nonlinear least squares (NLS)
                      algorithm, and the parameter boundaries are identified based
                      on Monte Carlo simulations. The uncertainties of internal
                      states in time and frequency domains are investigated
                      afterwards. The results demonstrate the effectiveness of
                      this method. Among the three sub-systems (cathode, anode,
                      and membrane), the cathode sub-system was found to have the
                      smallest uncertainties, while the membrane has the largest.
                      Transfer functions for small disturbances of cell current to
                      the internal states also have uncertainties, which can be
                      low-frequency pass or bandpass functions depending on the
                      parameter values. Further study will focus on the design of
                      robust control strategies based on system models with
                      uncertainties.},
      cin          = {IEK-3},
      ddc          = {660},
      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:000395842000042},
      doi          = {10.1016/j.ijhydene.2016.11.151},
      url          = {https://juser.fz-juelich.de/record/810849},
}