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

Using COMSOL Multiphysics®, a three-dimensional (3D) macrohomogenous redox flow battery (RFB) model is validated against experimental data. Subsequently, the 3D model is accurately reduced to two-dimensions (2D) to enable high-throughput screening of electrode configurations and to quantify statistical correlations throughout a polarization curve. This work affords a framework for generating low-dimensional RFB models and forms a basis for additional data science efforts for regressing RFB performance models.

Scientific Achievement

This study provides extensive validation for 3D-to-2D model reduction for redox flow batteries (RFBs). This computationally light, 2D model is used to generate a data set of >6,000 unique RFB simulations for statistical quantification.

Significance and Impact

This work spans multiple electrode property values to generate a structure/function data set for a model vanadium redox flow cell. This procedure can be applied to new redox chemistries for in operando statistical quantification as well as be further implemented advanced data science analysis.

Research Details

  • A 3D, macrohomogenous RFB model is validated against experimental data with three fitting parameters
  • The 3D model is reduced to a 2D model and validated against experimental data, reducing the computational time
  • This 2D model is used to >6,000 electrode configurations to build a robust structure/function data set
  • Statistical correlations are computed for each simulation, serving as a parameter sensitivity across polarization conditions

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DOI: 10.1016/j.apenergy.2020.115530

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