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Data-driven electrode parameter identification for vanadium redox flow batteries through experimental and numerical methods

Author

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  • Cheng, Ziqiang
  • Tenny, Kevin M.
  • Pizzolato, Alberto
  • Forner-Cuenca, Antoni
  • Verda, Vittorio
  • Chiang, Yet-Ming
  • Brushett, Fikile R.
  • Behrou, Reza

Abstract

The vanadium redox flow battery (VRFB) is a promising energy storage technology for stationary applications (e.g., renewables integration) that offers a pathway to cost-effectiveness through independent scaling of power and energy as well as longevity. Many current research efforts are focused on improving battery performance through electrode modifications, but high-throughput, laboratory-scale testing can be time- and material-intensive. Advances in multiphysics-based numerical modeling and data-driven parameter identification afford a computational platform to expand the design space by rapidly screening a diverse array of electrode configurations. Herein, a 3D VRFB model is first developed and validated against experimental results. Subsequently, a new 2D model is composed, yielding a computationally-light simulation framework, which is used to span bounded values of the electrode thickness, porosity, volumetric area, fiber diameter, and kinetic rate constant across six cell polarization voltages. This 2D model generates a dataset of 7350 electrode property combinations for each cell voltage, which is used to evaluate the effect of these structural properties on the pressure drop and current density. These structure-performance relationships are further quantified using Kendall τ rank correlation coefficients to highlight the dependence of cell performance on bulk electrode morphology and to identify improved property sets. This statistical framework may serve as a general guideline for parameter identification for more advanced electrode designs and redox flow battery stacks.

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  • Cheng, Ziqiang & Tenny, Kevin M. & Pizzolato, Alberto & Forner-Cuenca, Antoni & Verda, Vittorio & Chiang, Yet-Ming & Brushett, Fikile R. & Behrou, Reza, 2020. "Data-driven electrode parameter identification for vanadium redox flow batteries through experimental and numerical methods," Applied Energy, Elsevier, vol. 279(C).
    • Handle: RePEc:eee:appene:v:279:y:2020:i:c:s0306261920310424
    DOI: 10.1016/j.apenergy.2020.115530
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    Cited by:

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    2. Wan, Shuaibin & Liang, Xiongwei & Jiang, Haoran & Sun, Jing & Djilali, Ned & Zhao, Tianshou, 2021. "A coupled machine learning and genetic algorithm approach to the design of porous electrodes for redox flow batteries," Applied Energy, Elsevier, vol. 298(C).
    3. Zhang, Xiaoxi & Pan, Yongjun & Xiong, Yue & Zhang, Yongzhi & Tang, Mao & Dai, Wei & Liu, Binghe & Hou, Liang, 2024. "Deep learning-based vibration stress and fatigue-life prediction of a battery-pack system," Applied Energy, Elsevier, vol. 357(C).
    4. Pan, Yongjun & Zhang, Xiaoxi & Liu, Yue & Wang, Huacui & Cao, Yangzheng & Liu, Xin & Liu, Binghe, 2022. "Dynamic behavior prediction of modules in crushing via FEA-DNN technique for durable battery-pack system design," Applied Energy, Elsevier, vol. 322(C).

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