Physics-informed CoKriging model of a redox flow battery

Amanda A. Howard, Tong Yu, Wei Wang, Alexandre M. Tartakovsky

Research output: Contribution to journalArticlepeer-review

Abstract

Vanadium redox flow batteries (VRFBs) 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 VRFB to potentially improve the battery capacity and performance. We develop a multifidelity model for predicting the charge–discharge curve of a VRFB. 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.

Original languageEnglish (US)
Article number231668
JournalJournal of Power Sources
Volume542
DOIs
StatePublished - Sep 15 2022
Externally publishedYes

Keywords

  • Hybrid models
  • Multifidelity models
  • Physics-informed models
  • Redox flow battery

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology
  • Physical and Theoretical Chemistry
  • Electrical and Electronic Engineering

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