Browsing by Subject "Electrochemical Impedance Spectroscopy"

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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.
Open Access
Impedance based modeling of battery parameters and behavior
(2017-07) Aydın, Elif
Modeling battery performance under arbitrary load has gained importance in recent years with the increasing demand on batteries in various fields from automotive industry to consumer electronic devices. Due to numerous application areas of electrochemical energy storage (EES) systems, researchers have tried to predict the battery performance and the voltage using extensive calculations. Unfortunately, in order to achieve high levels of accuracy, the model has to be algebraically and computationally complex. Models with decreased computational and algebraic complexity suffer from loss of accuracy. In this thesis, we offer a new modeling approach to predict the voltage responses of batteries and supercapacitors which is both algebraically straightforward and yielding more accurate results. Our approach is valid using any discharge profile including published by regulatory bodies such as Environmental Protection Agency (EPA). Our method is based on Electrochemical Impedance Spectroscopy (EIS) measurements done on the system to be predicted and slow DC discharge. EIS data is used directly to predict the fast moving portion of the voltage response to the profiles. The EIS data is used as is, namely, in frequency domain without any modeling. The slow DC discharge data provides DC response and is added in through a straightforward lookup table. This widely applicable approach can predict the voltage with less than 1% error, without any adjustable parameters to a large variety of discharge profiles.
Open Access
Performance modeling of unmanaged hybrid battery/supercapacitor energy storage systems
(Elsevier, 2021-09-13) Zabara, Mohammed Ahmed; Uzundal, Can Berk; Ülgüt, Burak
Unmanaged hybrid battery/supercapacitor energy storage systems possess higher performance with lower cost and complexity compared to not only individual cells, but also electronically managed hybrid systems. Achieving full performance requires the understanding of the power distribution and predicting their best combinations. In this work, a semi-empirical modeling methodology is presented that can predict the current distribution and the voltage response of battery/supercapacitor hybrid systems under arbitrary charge/discharge profiles. Results are presented for the assessment of hybrid systems under real life scenarios. The key strength of the presented method is that it is free of any parametrization, fits or subjective inputs. The modeling methodology is validated with experimental measurements for two different Li-ion battery chemistries, namely Lithium Iron Phosphate and Lithium Vanadium Pentoxide, connected in parallel to wide range of supercapacitors. Finally, we outline several design rules for hybrid storage systems for different use cases.