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@INPROCEEDINGS{Raijmakers:1035298,
      author       = {Raijmakers, Luc and Ali, Haider Adel and Tempel, Hermann
                      and Riebesel, Lea and Xhonneux, André and Kasselmann,
                      Stefan and Müller, Dirk and Eichel, Rüdiger-A.},
      title        = {{B}attery simulations of real-world electric vehicle data},
      reportid     = {FZJ-2025-00356},
      year         = {2024},
      abstract     = {Electric vehicles (EVs) have gained significant attention
                      as a sustainable mode of transportation, emphasizing the
                      critical role of Li-ion batteries in their performance.
                      Accurate battery state estimation is essential for improving
                      the reliability and efficiency of EVs. To develop state
                      estimation algorithms, existing testing methods, often
                      primarily performed in laboratory settings under controlled
                      conditions, are utilized. However, these methods differ
                      substantially from real-world scenarios, posing challenges
                      in translating laboratory findings to practical
                      applications. Introducing the complexities of real-world
                      conditions, so-called field data acquisition is essential
                      for providing insights into EV operation beyond laboratory
                      tests. However, field data acquisition introduces
                      complexities such as uncontrolled operating conditions, less
                      accurate sensors, and concerns regarding data collection and
                      storage, including data gaps [1]. Moreover, the unknown
                      battery cell parameters within EV battery packs, if not
                      being a battery or EV manufacturer, pose additional hurdles,
                      particularly in simulating and predicting battery cell
                      states, such as battery cell voltage.In this study, we
                      employ a physics-based battery modelling technique,
                      specifically the extended single particle model (ESPM), to
                      simulate the battery cell voltage under various test drive
                      conditions, with battery pack current as input. The ESPM is
                      chosen for its ability to achieve a favourable balance
                      between accuracy, given the relatively low average C-rates
                      for EVs, and simulation speed [2]. A research EV (Nissan
                      Leaf) is used to perform measurements of EV states,
                      including battery pack current, (cell) voltage, and
                      temperature. The EV states are obtained via the OBD-II
                      interface using an AutoPi telematics device [3], which logs
                      and sends the data via mobile network to a server (in real
                      time) during driving.Before performing battery simulations,
                      a parameter sensitivity analysis was performed to identify
                      the most sensitive parameters of the ESPM model.
                      Subsequently, highly and medium-sensitive parameters were
                      prioritized for parameter optimization, while setting
                      low-sensitive parameters to typical average values obtained
                      from literature. The sensitivity analysis results are shown
                      in Fig. 1a and illustrate the impact of various parameters
                      on the model's performance. Following the sensitivity
                      analysis, battery parameter optimization was performed with
                      the highly and medium-sensitive parameters. For parameter
                      optimization, the difference between measured and simulated
                      battery cell voltage was minimized, while the battery
                      current served as input for the ESPM. The same was repeated
                      for various test drives under different conditions. Fig. 1b
                      shows an example of a comparison between the measured (blue
                      line) and simulated (red line) voltage of a battery cell
                      during a test drive, which highlights the model's ability to
                      capture real-world behaviour.These findings serve as a first
                      step towards achieving state estimation and forecasting
                      through model-based approaches under real-world conditions.
                      In the future, we plan to expand our research by performing
                      parameter optimization under a wider range of operating
                      conditions, aiming to optimize the robustness and accuracy
                      of our simulations. Additionally, we anticipate achieving
                      better simulation accuracy by extracting physical parameters
                      directly from batteries, thereby capturing the intricacies
                      of real-world behaviour more effectively.[1] V. Sulzer, P.
                      Mohtat, A. Aitio, S. Lee, Y.T. Yeh, F. Steinbacher, M.U.
                      Khan, J.W. Lee, J.B. Siegel, A.G. Stefanopoulou, D.A. Howey,
                      The challenge and opportunity of battery lifetime prediction
                      from field data, Joule. 5 (2021) 1934–1955.
                      doi:10.1016/j.joule.2021.06.005.[2] H.A.A. Ali, L.H.J.
                      Raijmakers, K. Chayambuka, D.L. Danilov, P.H.L. Notten,
                      R.-A. Eichel, A Comparison between Physics-Based Li-Ion
                      Battery Models, Preprint. (2023).
                      doi:http://dx.doi.org/10.2139/ssrn.4664038.[3] AutoPi.io,
                      (n.d.). https://www.autopi.io/ (accessed November 27,
                      2023).},
      month         = {Apr},
      date          = {2024-04-09},
      organization  = {Advanced Battery Power Conference,
                       Münster (Germany), 9 Apr 2024 - 11 Apr
                       2024},
      subtyp        = {After Call},
      cin          = {IET-1},
      cid          = {I:(DE-Juel1)IET-1-20110218},
      pnm          = {1223 - Batteries in Application (POF4-122) / LLEC::VxG -
                      Integration von "Vehicle-to-grid" (BMBF-03SF0628)},
      pid          = {G:(DE-HGF)POF4-1223 / G:(DE-Juel1)BMBF-03SF0628},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/1035298},
}