% 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{Ali:1033598,
      author       = {Ali, Haider Adel Ali and Raijmakers, Luc and Tempel,
                      Hermann and Danilov, Dmitri and Notten, Peter H. L. and
                      Eichel, Rüdiger-A.},
      title        = {{A} {H}ybrid {E}lectrochemical {M}ulti-{P}article {M}odel
                      for {L}i-ion {B}atteries},
      journal      = {Journal of the Electrochemical Society},
      volume       = {171},
      issn         = {0013-4651},
      address      = {Bristol},
      publisher    = {IOP Publishing},
      reportid     = {FZJ-2024-06478},
      pages        = {110523},
      year         = {2024},
      abstract     = {Physics-based models have proven to be effective tools for
                      predicting the electrochemical behavior of Li-ion batteries.
                      Among the various physics-based models, the
                      Doyle-Fuller-Newman (DFN) model has emerged as the most
                      widely employed. In response to certain limitations of the
                      DFN model, the multiple particle-Doyle-Fuller-Newman
                      (MP-DFN) model was introduced. The MP-DFN model utilizes
                      multiple electrode particle sizes, addressing internal
                      concentration heterogeneities and more realistically
                      simulate diffusion processes in the electrodes. However, the
                      model requires relatively high computational cost. This work
                      introduces the Padé approximation for the MP-DFN model,
                      resulting in the simplified MP-DFN model, leading to a
                      faster simulation time. However, depending on battery design
                      and operation conditions, this solution shows to have lower
                      accuracy compared to the MP-DFN. To overcome these
                      challenges, this study also introduces a hybrid MP-DFN
                      model. This model uses a novel approach aimed at striking a
                      balance between accuracy and computational speed. The hybrid
                      MP-DFN model integrates both the finite difference method
                      (FDM) and Padé approximation to effectively address the
                      challenges posed by multiple particle sizes within the
                      electrodes. The choice between FDM or the approximations for
                      a specific particle in the electrode is determined by the
                      scaled diffusion length.},
      cin          = {IET-1},
      ddc          = {660},
      cid          = {I:(DE-Juel1)IET-1-20110218},
      pnm          = {1223 - Batteries in Application (POF4-122) / LLEC::VxG -
                      Integration von "Vehicle-to-grid" (BMBF-03SF0628) / BMBF
                      13XP0530B - ALIBES: Aluminium-Ionen Batterie für
                      Stationäre Energiespeicher (13XP0530B)},
      pid          = {G:(DE-HGF)POF4-1223 / G:(DE-Juel1)BMBF-03SF0628 /
                      G:(BMBF)13XP0530B},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001364651400001},
      doi          = {10.1149/1945-7111/ad92dd},
      url          = {https://juser.fz-juelich.de/record/1033598},
}