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@ARTICLE{Li:910829,
      author       = {Li, Weihan and Fan, Yue and Ringbeck, Florian and Jöst,
                      Dominik and Sauer, Dirk Uwe},
      title        = {{U}nlocking electrochemical model-based online power
                      prediction for lithium-ion batteries via {G}aussian process
                      regression},
      journal      = {Applied energy},
      volume       = {306},
      issn         = {0306-2619},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2022-04179},
      pages        = {118114 -},
      year         = {2022},
      abstract     = {The knowledge of the dynamic available charging and
                      discharging power of the battery is a piece of essential
                      information for the safety and longevity of the battery
                      energy storage systems. An accurate real-time prediction of
                      these quantities is very challenging due to the high
                      nonlinearities of battery dynamics. In this paper, an
                      electrochemical model-based online state-of-power prediction
                      algorithm under different time horizons is developed for a
                      safer and more reliable operation of lithium-ion batteries.
                      The safety constraints, which define the safety operation
                      area for the power prediction, are designed based on not
                      only the terminal voltage but also battery internal
                      electrochemical states, i.e., the electrode surface
                      concentration, the electrolyte concentration, and the side
                      reaction overpotential. The algorithm is validated by
                      simulations and experiments under a dynamic load profile,
                      and the dominating constraints in charging and discharging
                      as well as the influence of predictive time horizons on the
                      available battery power are analyzed, providing important
                      information for further researches. Furthermore, the
                      computational speed of the proposed iterative algorithm is
                      improved with the integration of Gaussian process regression
                      by up to $50\%.$ A comparative study with a state-of-the-art
                      equivalent circuit model-based approach highlights the
                      significant benefits of the proposed electrochemical
                      model-based algorithm in operation safety enhancement and
                      battery performance improvement.},
      cin          = {IEK-12 / JARA-ENERGY},
      ddc          = {620},
      cid          = {I:(DE-Juel1)IEK-12-20141217 / $I:(DE-82)080011_20140620$},
      pnm          = {1223 - Batteries in Application (POF4-122) / EVERLASTING -
                      Electric Vehicle Enhanced Range, Lifetime And Safety Through
                      INGenious battery management (713771) / BMBF 03XP0334 -
                      Model2Life- Modellbasierte Systemauslegung für
                      2nd-Life-Nutzungsszenarien von mobilen Batteriesystemen
                      (03XP0334)},
      pid          = {G:(DE-HGF)POF4-1223 / G:(EU-Grant)713771 /
                      G:(BMBF)03XP0334},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000720481200003},
      doi          = {10.1016/j.apenergy.2021.118114},
      url          = {https://juser.fz-juelich.de/record/910829},
}