2021/06/17 by Amanda A. Howard, Howard, Amanda A., Alexandre M. Tartakovsky +1
Chemistry · Energy · Engineering · #Advanced battery technologies research #Chemical Physics (physics.chem-ph) #Electrocatalysts for Energy Conversion #Electrochemical Analysis and Applications #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2106.09188
openalex publication_date 2021/06/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.