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

Abstract

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.

Determination of the Ion Concentrations in VRFB by Non-Invasive Optical Techniques Due to Chemical Reactions, Complexes, and Side Reactions

“…22 Even more recent works addressing fabrication and testing of our battery are available in: Krowne, 165 Nernst equations and overpotential; Krowne, 166 quantum mechanical values for batteries relating theory and experiment; Krowne, 167 fluid physics for the battery; Daniel, Byron and Krowne, 168 discussion of battery stacks and industrial opportunities; Krowne, 169 current/ voltage behavior related to electrodes and ion concentrations; and Krowne, 170 governing equations for the VRFB. Other recent related works are by Khali, Das et al, are capacity related to electrolyte flow 171 ; reducing the number of sensors 172 ; parameter identification via metaheuristic algorithms 173 ; and multi-objective optimization for VRFBs. 174…”

Physics, electrochemistry, chemistry, and electronics of the vanadium redox flow battery by analyzing all the governing equations

“…Discussion of the basic electrochemistry of VRFB, electronic circuits of the VRFB, fluid dynamics, statistical and thermodynamic physics basis which underpins all of the battery work, measures of battery performance used to monitor and control the battery delivery of power, such as the State of Charge (SoC), State of Health (SoH), temperature T (in both the two half-cells, and an overall average), current flowing I , and battery capacity, are available in articles. 1–117 Because the subject of batteries is so interdisciplinary, it is nearly impossible to identify articles as being solely one aspect versus another, so that the journal articles are not listed in any particular order. Most of the articles handle the basic science and engineering, but some address issues of insertion into power grids.…”

Optimal parameters identification strategy of a lead acid battery model for photovoltaic applications

“…The identification techniques of this study are based on meta-heuristic optimization algorithms (MOAs). 11 In this study, we have determined the parameters of two solar LaBs: Banner 12 V-100 Ah and Bergan 12 V-100 Ah batteries. The selected mathematical model is elaborated by Shepherd.…”

“…The objective is to build a workspace to analyze LaB system storage problems, from its design to its starting and operating. The identification techniques of this study are based on meta ‐heuristic optimization algorithms (MOAs) 11 …”