Extended Kalman Filter (EKF)-based modular-stack Vanadium Redox Flow Battery (V-RFB) prediction model development for reducing electrode contact resistance and parallelization current

Mohamed, Mohd Rusllim (2019) Extended Kalman Filter (EKF)-based modular-stack Vanadium Redox Flow Battery (V-RFB) prediction model development for reducing electrode contact resistance and parallelization current. , [Research Report: Research Report] (Unpublished)

Extended Kalman Filter (EKF)-based modular-stack Vanadium Redox Flow Battery (V-RFB).wm.pdf

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Vanadium Redox Flow Battery (V-RFB) is a type of rechargeable flow battery that employs vanadium ions in different oxidation states. It undergoes oxidation and reduction reaction during discharge and charge process at anode and cathode. Presently, there are lack of publication studies on electrical circuit model for V-RFB. Electrochemical model is commonly use to represent battery due to its detailing in electrochemical process, however, the model is not suitable to identify electrical behavior of V-RFB. Parameter estimation on battery model is a process to fit an equivalent circuit into the battery. This thesis presents equivalent electrical circuit consists of actual and approximate circuit for V-RFB as well as hydrodynamics behavior of the Vanadium redox flow battery (V-RFB) by using 3D computational fluid dynamics (CFD) models. The aim of this project is to propose equivalent electrical circuit for V-RFB that represents excellent adaptableness to any circuitry analysis and design and to study the pump power (pump energy consumption) and electrolyte flow distribution required within the cell. For equivalent circuit, the actual circuit consists of an open-circuit cell potential, two Resistor-Capacitor (RC) branch, a series RC, internal resistance, and inductor. From the circuit, some of the parameters are lumped to construct approximate circuit consists of open-circuit cell potential, impedance of RC branches and internal resistance with inductor. Approximate circuit is built in order to present less complex result and save time. Extended Kalman Filter (EKF) is used for parameter estimation for both circuit. Actual and approximate circuit are derived accordingly. The simulation result through recursive EKF algorithm of each parameters of both circuits shows approaching steady with 0.6% and 2.0% errors, respectively. So, it proven that both circuit are adaptable for V-RFB. On the other hands, three different cell geometries of V-RFB cell, namely square-, rhombus- and circular cell designs are evaluated at three different cases i.e. no flow (plain) channel, parallel channel and serpentine channel. Furthermore, the work has been extended in modular stack of 100 cm2 of V-RFB. The stack has been developed and tested to observe the pump power within the stack in the three designs which directly related to performance of the cell with respect to power distribution and power losses. Based on the findings, the cell exhibits different characteristics under different geometries of V-RFB cell at no flow channel application. Conversely, based on the scaling up of the cell geometry, the relationship between pump power and cell geometry for 100 cm2 of V-RFB has been developed. Optimum flow distribution within the cells without fluid flow channels were recorded; highest and lowest pump consumption at 25.6% and 18.4% respectively. Extended reduction of power losses by 53% were recorded as parallel flow channels has been applied to the V-RFB. Proportionate correlations were observed for modular V-RFB as a result of scaling up of the cell and potential for further analysis of extension to the nth cell. Further works are presented for future research in geometry study of V-RFB.

Item Type: Research Report
Additional Information: RESEARCH VOTE NO: RDU150123
Uncontrolled Keywords: Vanadium Redox Flow Battery (V-RFB); Extended Kalman Filter (EKF)
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Depositing User: En. Mohd Ariffin Abdul Aziz
Date Deposited: 16 Feb 2023 03:26
Last Modified: 16 Feb 2023 03:26
URI: http://umpir.ump.edu.my/id/eprint/36326
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