A thin-layer diffusion model-based intelligent cyclic voltammetry analysis method to capture electrochemical parameters in flow batteries

Contrary to the conventional approach that utilizes the planar electrode model to achieve the electrochemical parameters of the electrode in flow batteries, this work proposes an intelligent cyclic voltammetry (CV) analysis method to accurately capture the electrochemical parameters of the carbon-based porous electrode: (i) the transient diffusion process is elaborated by the Fick's second law; (ii) the thin-layer diffusion boundary condition is determined based on the structural characteristics of porous electrodes; (iii) CV response on the porous electrode is obtained by the Bulter-Volmer equation in assocition with the Fick's first law; and (iv) the electrochemical parameters of the porous electrode are automatically extracted from the experimental data via the genetic algorithm. The feasibility of the intelligent CV analysis method is proven by studying the electrochemical parameters including the diffusion coefficient D and the standard rate constant k0 of [Fe(CN)6]4−/[Fe(CN)6]3−. Additionally, this analysis method is applied to investigate the vanadium redox flow battery by providing accurate electrochemical parameters for the 3D multi-physics model to predict the charge/discharge performance at different current densities. The simulation results demonstrate a maximum relative error of 2% in comparison to the experimental data, suggesting the good reliability in developing the advanced model. The proposed intelligent CV analysis method provides a potential application in accurately capturing the electrochemical parameters in other electrochemical energy conversion and storage devices.