ChemRxiv
These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
ChemRxiv.pdf (8.84 MB)

Data-Driven Electrode Parameter Identification for Vanadium Redox Flow Batteries Through Experimental and Numerical Methods

preprint
revised on 01.07.2020, 21:32 and posted on 03.07.2020, 07:43 by Ziqiang Cheng, Kevin M. Tenny, Alberto Pizzolato, Antoni Forner-Cuenca, Vittorio Verda, Yet- Ming Chiang, Fikile Brushett, Reza Behrou
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 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 $\tau$ 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 (RFB) stacks.

History

Email Address of Submitting Author

rbehrou@eng.ucsd.edu

Institution

University of California San Diego

Country

United States

ORCID For Submitting Author

0000-0003-3136-3886

Declaration of Conflict of Interest

The authors declare that they have no conflict of interest.

Exports