Browsing by Author "Zabara, Mohammed Ahmed Mohammed"

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Open Access
Electrochemical impedance spectroscopy based characterization and modeling of electrochemical energy storage systems
(2021-10) Zabara, Mohammed Ahmed Mohammed
In this thesis, first we demonstrate the characterization of the electrochemical processes in primary Lithium batteries utilizing Electrochemical Impedance Spectroscopy (EIS). We develop Galvanostatic-EIS at discharge technique which provide linear and stable impedance data of primary Li batteries in wide frequency range. The obtained data is further investigated by variation of the electrolyte composition. The results reveal the electrochemical processes associated with impedance response at different frequency regions. The impedance response is then assigned to the corresponding anodic and cathodic charge transfer plus the interfacial processes. Further, we investigate the temperature dependence of the impedance of the batteries which reveals the activated processes and allow for the calculation of their activation energies. Along with the linear impedance response, we also investigate the non-linear response obtained from the primary Li batteries. We show that non-linearity can be used to detect the degree of the passivation of the Li anode. We also show the non-linear response at different States-of-Charge and with temperature change. Second, we utilize linear impedance data in modeling the voltage response of the primary Li batteries. We apply previously developed EIS based Zero-free-parameter modeling approach to predict the voltage response of the primary Li batteries for the desired applications. We improve the method to account for the voltage delay phenomena which is an outcome of using metallic Li in the anode. We further utilize the same modeling method to predict the voltage response of hybrid unmanaged secondary Li-ion batteries supercapacitor systems under real-life setarious. We develop the method to first predict the current distribution among the parallel connected hybrid systems utilizing differential evolution optimization algorithm. Then with the accurate impedance of each system the voltage response is predicted. We validate the modeled results with experimental measurements which shows high accuracy for various hybrid Li-ion supercapacitor systems. Finally, utilizing the modeled results we present design rules for hybridization based on the gains obtained in different parameters such as, peak current, power and total energy. Moreover, the improvements in size and cost of hybridization with different supercapacitor capacities are studied which also contribute in determining the best combination of the Li-ion battery supercapacitor hybrid system.