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Implementing Reversible Swelling into the Numerical Model of a Lithium-Ion Pouch Cell for Short Circuit Prediction

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  • معلومة اضافية
    • بيانات النشر:
      Multidisciplinary Digital Publishing Institute
    • الموضوع:
      2023
    • Collection:
      MDPI Open Access Publishing
    • نبذة مختصرة :
      Mechanical simulation models have become crucial for understanding Li-ion battery failure and degradation mechanisms. However, existing safety assessment models lack the implementation of SOC-dependent thickness variations referred to as reversible swelling. Reversible swelling affects the applied preload force on a constrained pouch cell, potentially impacting its safety. To investigate this, a finite element RVE model was developed in LS-Dyna. Two swelling models, simplified homogenous expansion (HE) and locally resolved expansion (LE), were implemented along with a reference basis model (BM) without expansion. Six different stress- or strain-based short circuit criteria were calibrated with abuse test simulations at different SOCs and preload forces. Short circuit prognosis improved on average by 0.8% and 0.7% for the LE and HE model compared to the BM, with minimum principal stress being the most suitable criterion. The LE model exhibited a softer mechanical response than the HE model or BM, accounting for the pouch cell surface unevenness at small indentations. This study demonstrated the feasibility and usefulness of implementing an expansion model in a commercial FE solver for improved short circuit predictions. An expansion model is crucial for simulating aged battery cells with significant geometry changes strongly affecting the preload force of a constrained battery cell.
    • File Description:
      application/pdf
    • Relation:
      Battery Modelling, Simulation, Management and Application; https://dx.doi.org/10.3390/batteries9080417
    • الرقم المعرف:
      10.3390/batteries9080417
    • الدخول الالكتروني :
      https://doi.org/10.3390/batteries9080417
    • Rights:
      https://creativecommons.org/licenses/by/4.0/
    • الرقم المعرف:
      edsbas.9121A521