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@ARTICLE{Steininger:1024912,
      author       = {Steininger, Valentin and Hüsson, Peter and Rumpf,
                      Katharina and Sauer, Dirk Uwe},
      title        = {{C}ustomer-centric aging simulation for 48 {V} lithium-ion
                      batteries in vehicle applications},
      journal      = {eTransportation},
      volume       = {16},
      issn         = {2590-1168},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2024-02562},
      pages        = {100240 -},
      year         = {2023},
      abstract     = {In the automotive industry, a battery management system’s
                      state prediction algorithms can change during the
                      development cycle of a car. Monitored aggregated customer
                      data thus includes inconsistent state of health values which
                      have been generated by different algorithms. To facilitate
                      comparability over the collected data, databased models can
                      be trained on selected input variables to provide reference
                      state of health values. This study aims to generate virtual
                      customer driving data of mild-hybrid electric vehicles using
                      automotive simulation models and stochastic customer driving
                      profiles in order to establish a simulation database for
                      model training purposes on one hand and to conduct lifetime
                      simulations for new vehicles in the market on the other.
                      Mapping algorithms between load profile libraries and
                      derived statistical features from a field customer database
                      ensure a realistic representation of individual customer
                      driving behavior. We validated our toolchain using collected
                      trip data from a testing fleet and checked for statistical
                      plausibility of the simulation data. Moreover, lifetime
                      simulation results of selected customers show significant
                      differences in aging implications due to individual driving
                      behavior and environmental conditions. Therefore, during a
                      10-year simulation, the average aging rate per driven
                      kilometer of an Asian customer is about $33\%$ higher
                      compared to a European customer.},
      cin          = {IEK-12},
      ddc          = {400},
      cid          = {I:(DE-Juel1)IEK-12-20141217},
      pnm          = {1223 - Batteries in Application (POF4-122)},
      pid          = {G:(DE-HGF)POF4-1223},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000959469300001},
      doi          = {10.1016/j.etran.2023.100240},
      url          = {https://juser.fz-juelich.de/record/1024912},
}