Parameter identification of electrochemical model of vanadium redox battery by metaheuristic algorithms

An accurate pre-setting of the constant coefficients and parameters of the electrochemical model of the battery is essential for the accuracy of the model. The experimental methods are not precisely determined these parameters. In vanadium redox flow batteries (VRFB), like other types of batteries, the electrochemical model's coefficients vary by each battery cell design, different kinds of membranes, etc. Moreover, a VRFB cell's electrochemical model is highly nonlinear; thus, excellent optimization approach is needed to figure out these coefficients' optimal value. Some metaheuristic optimizers, such as particle swarm optimizer (PSO), grey wolf optimizer (GWO), genetic algorithm (GA), and the hybrid PSOGWO algorithms, are used in this study to identify these coefficients. An optimization framework is characterized to recognize the coefficients of the model by minimizing the mean square error between the measured values and the model-based terminal voltage. The low RMS error of the modeled terminal voltage by the metaheuristic-based electrochemical model demonstrates the accuracy of the proposed parameter identification approach for VRFBs. Further, the improved electrochemical model using the optimal coefficients is employed to estimate VRFB internal parameters, for example, the available battery capacity, the state of health, and the state of charge more accurately.