An electrical model for characterising electric double layer capacitors using open-circuit discharge evaluation /
Ria
- xv, 212 pages : illustration ; 30 cm. + 1 CD-ROM
Faculty of Industrial Sciences and Technology
Thesis (Doctor of Philosophy) -- Universiti Malaysia Pahang – 2024
Includes bibliographical references
Batteries and electrochemical capacitors (ECs, synonymously supercapacitors, or SCs) are the two widely investigated electrical energy storage devices in use today. The ECs gained attention over the last couple of decades owing to their superior specific power (~10 - 15 kW‧kg-1 compared to ~250 - 400 W‧kg-1 for lithium-ion batteries, LIBs) and extremely high cycle life (~105 cycles compared to ~102 – 103 for LIBs), which make them an appealing alternative or complementary device for the batteries. The ever-increasing EC materials research demands assessing their performance and capabilities for large-scale use and requires understanding their behaviour through cutting-edge experimentation and model simulations. Because a model is a substitute of a real system, the accuracy and assumptions made in the model describes its applicability for specific purposes. Since most of the electrical EC models do not consider the effect of open circuit discharge (OCD) phenomenon, they can only predict the short and medium-term behaviour. The long-term behaviour can only be predicted when the leakage current is taken into account. The aim of this doctoral thesis is to develop an electrical model of an electric double layer capacitor (EDLC) such that experimental OCD data, which is synonymously known as self-discharge, can be used to derive the charge storage and charge kinetic parameters. Such a model can be immensely beneficial for academia and industry to characterize an EDLC electrode with a high degree of accuracy. This objective is achieved by simulating an electrical model of ECs using MATLAB Simulink platform employing a three-branch RC circuit logic. The model variables (time constants, maximum voltage, resistances, and capacitances) in the device are obtained by dividing the OCD data into fast, medium, and slow branches. Based on these time scales, mathematical equations are derived to calculate the parametric values of the various circuit elements present in the model. The assumptions of charge transfer behaviour from one branch to another are corroborated using a simple experiment. To validate the working of the model, two types of electrode materials: the first is porous carbon, which is a widely used electrode material for commercial EDLC, and the second is a randomly chosen metal oxide, CuMn2O4 nanofibers. The electrodes are characterized using OCD, cyclic voltammograms, electrochemical impedance spectroscopy and galvanostatic charge–discharge measurements (GCD) in three choices of electrolytes, viz. 1 M KOH (alkaline), 1 M Na2SO4 (neutral) and 1 M Li2SO4 (neutral). The simulated and experimental data are in close agreement, with a relative error of less than 5%. Once validated, the model is used to simulate the GCD curve without performing the experiments in the laboratory. This is again verified to be correct by comparing with experimental GCD curve, thereby saving time and resources. The measurements further showed that while carbon showed EDLC behaviour, the storage mode in the CuMn2O4 electrode depends on the choice of the electrolyte, i.e., battery type storage mode in 1 M KOH and EDLC type in 1 M Na2SO4 and 1 M Li2SO4. The simulated and experimental OCD and GCD data vary significantly in case of battery type storage, thereby clearly suggesting that the model can differentiate between EDLC and nonEDLC devices. The thesis, thus, presents a model to characterize EDLC devices using a simple OCD analysis. This model could also be used in industries to characterize the EDLCs using minimum number of experiments, thereby adding much easiness to the quality control across the batch of production. Therefore, the method detailed in this thesis provides an excellent tool to characterize EDLCs for both academia and industry
THE0009868 (Local)
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