Physics-informed CoKriging model of a redox flow battery
- URL: http://arxiv.org/abs/2106.09188v1
- Date: Thu, 17 Jun 2021 00:49:55 GMT
- Title: Physics-informed CoKriging model of a redox flow battery
- Authors: Amanda A. Howard, Alexandre M. Tartakovsky
- Abstract summary: Redox flow batteries (RFBs) offer the capability to store large amounts of energy cheaply and efficiently.
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.
- Score: 68.8204255655161
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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