  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.
