Physics-informed CoKriging model of a redox flow battery

TL;DR

This paper introduces a physics-informed multifidelity machine learning model for accurately predicting redox flow battery charge-discharge behavior, combining experimental data with physics constraints for improved performance and robustness.

Contribution

The paper presents a novel physics-informed CoKriging model that effectively integrates experimental data with physics-based models for RFBs, enhancing prediction accuracy and robustness.

Findings

Model shows good agreement with experimental data.

Significant improvement over existing zero-dimensional models.

Requires only small datasets for accurate predictions.

Abstract

Redox flow batteries (RFBs) offer the capability to store large amounts of energy cheaply and efficiently, however, there is a need for fast and accurate models of the charge-discharge curve of a RFB to potentially improve the battery capacity and performance. We develop a multifidelity model for predicting the charge-discharge curve of a RFB. In the multifidelity model, we use the Physics-informed CoKriging (CoPhIK) machine learning method that is trained on experimental data and constrained by the so-called "zero-dimensional" physics-based model. Here we demonstrate that the model shows good agreement with experimental results and significant improvements over existing zero-dimensional models. We show that the proposed model is robust as it is not sensitive to the input parameters in the zero-dimensional model. We also show that only a small amount of high-fidelity experimental datasets are needed for accurate predictions for the range of considered input parameters, which include current density, flow rate, and initial concentrations.