Multiple parameter identification using genetic algorithm in vanadium redox flow batteries

We propose a multiple parameter identification method for vanadium redox flow batteries (VRFBs) to estimate the model parameter in a VRFB model. The proposed method consists of an evaluation of identifiability based on the Fisher Information Matrix (FIM) to determine the best subset of model parameters to be identified, a numerical modeling of semi-two-dimensional steady-state VRFB model, and a genetic algorithm to estimate optimal model parameters. In the optimization, we introduce a fitness function involving the mean square errors of the voltage between available experimental data and results of the VRFB model. We validate the proposed method by calculating confidence intervals of identifying parameters in the subset based on the FIM from the state of charge-voltage data obtained from a small VRFB cell experiment; we compare the curves of the identified-parameter model with those obtained experimentally. Further, we demonstrate the robustness of the proposed method through its application to a kW-scale VRFB stack utilizing advanced mixed electrolytes. The capacity-voltage curves predicted by the identified-parameter model show good agreement with those obtained experimentally under various operating conditions, with mean relative errors of less than 1.9%.