05 Fakultät Informatik, Elektrotechnik und Informationstechnik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6

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Institute: search.filters.UBSInstitut.Institut für PhotovoltaikAuthor: search.filters.author.Pross-Brakhage, Julia

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Now showing 1 - 5 of 5
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    Impedance based temperature estimation of lithium ion cells using artificial neural networks
    (2021) Ströbel, Marco; Pross-Brakhage, Julia; Kopp, Mike; Birke, Kai Peter
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    Introducing a concept for designing an aqueous electrolyte with pH buffer properties for Zn-MnO2 batteries with Mn2+/MnO2 deposition/dissolution
    (2023) Fitz, Oliver; Wagner, Florian; Pross-Brakhage, Julia; Bauer, Manuel; Gentischer, Harald; Birke, Kai Peter; Biro, Daniel
    For large-scale energy-storage systems, the aqueous rechargeable zinc–manganese dioxide battery (ARZMB) attracts increasing attention due to its excellent advantages such as high energy density, high safety, low material cost, and environmental friendliness. Still, the reaction mechanism and its influence on the electrolyte's pH are under debate. Herein, a pH buffer concept for ARZMB electrolytes is introduced. Selection criteria for pH buffer substances are defined. Different buffered electrolytes based on a zinc salt (ZnSO4, Zn(CH3COO)2, Zn(CHOO)2), and pH buffer substances (acetic acid, propionic acid, formic acid, citric acid, 4-hydrobenzoic acid, potassium bisulfate, potassium dihydrogen citrate, and potassium hydrogen phthalate) are selected and compared to an unbuffered 2 m ZnSO4 reference electrolyte using titration, galvanostatic cycling with pH tracking, and cyclic voltammetry. By adding buffer substances, the pH changes can be reduced and controlled within the defined operating window, supporting the Mn2+/MnO2 deposition/dissolution mechanism. Furthermore, the potential plateau during discharge can be increased from ≈1.3 V (ZnSO4) to ≈1.7 V (ZnSO4 + AA) versus Zn/Zn2+ and the energy retention from ≈30% after 268 cycles (ZnSO4) to ≈86% after 494 cycles (ZnSO4 + AA). Herein, this work can serve as a basis for the targeted design of long-term stable ARZMB electrolytes.
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    Comparison of different current collector materials for in situ lithium deposition with slurry-based solid electrolyte layers
    (2023) Kreher, Tina; Heim, Fabian; Pross-Brakhage, Julia; Hemmerling, Jessica; Birke, Kai Peter
    In this paper, we investigate different current collector materials for in situ deposition of lithium using a slurry-based β-Li3PS4 electrolyte layer with a focus on transferability to industrial production. Therefore, half-cells with different current collector materials (carbon-coated aluminum, stainless steel, aluminum, nickel) are prepared and plating/stripping tests are performed. The results are compared in terms of Coulombic efficiency (CE) and overvoltages. The stainless steel current collector shows the best performance, with a mean efficiency of ηmean,SST=98%; the carbon-coated aluminum reaches ηmean,Al+C=97%. The results for pure aluminum and nickel indicate strong side reactions. In addition, an approach is tested in which a solvate ionic liquid (SIL) is added to the solid electrolyte layer. Compared to the cell setup without SIL, this cannot further increase the CE; however, a significant reduction in overvoltages is achieved.
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    Post-lithium batteries with zinc for the energy transition
    (2023) Pross-Brakhage, Julia; Fitz, Oliver; Bischoff, Christian; Biro, Daniel; Birke, Kai Peter
    The energy transition is only feasible by using household or large photovoltaic powerplants. However, efficient use of photovoltaic power independently of other energy sources can only be accomplished employing batteries. The ever-growing demand for the stationary storage of volatile renewable energy poses new challenges in terms of cost, resource availability and safety. The development of Lithium-Ion Batteries (LIB) has been tremendously pushed by the mobile phone industry and the current need for high-voltage traction batteries. This path of global success is primarily based on its high energy density. Due to changing requirements, other aspects come to the fore that require a rebalancing of different technologies in the “Battery Ecosystem”. In this paper we discuss the evolution of zinc and manganese dioxide-based aqueous battery technologies and identify why recent findings in the field of the reaction mechanism and the electrolyte make rechargeable Zn-MnO2 batteries (ZMB), commonly known as so-called Zinc-Ion batteries (ZIB), competitive for stationary applications. Finally, a perspective on current challenges for practical application and concepts for future research is provided. This work is intended to classify the current state of research on ZMB and to highlight the further potential on its way to the market within the “Battery Ecosystem”, discussing key parameters such as safety, cost, cycle life, energy and power density, material abundancy, sustainability, modelling and cell/module development.
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    Artificial feature extraction for estimating state-of-temperature in lithium-ion-cells using various long short-term memory architectures
    (2022) Kopp, Mike; Ströbel, Marco; Fill, Alexander; Pross-Brakhage, Julia; Birke, Kai Peter
    The temperature in each cell of a battery system should be monitored to correctly track aging behavior and ensure safety requirements. To eliminate the need for additional hardware components, a software based prediction model is needed to track the temperature behavior. This study looks at machine learning algorithms that learn physical behavior of non-linear systems based on sample data. Here, it is shown how to improve the prediction accuracy using a new method called “artificial feature extraction” compared to classical time series approaches. We show its effectiveness on tracking the temperature behavior of a Li-ion cell with limited training data at one defined ambient temperature. A custom measuring system was created capable of tracking the cell temperature, by installing a temperature sensor into the cell wrap instead of attaching it to the cell housing. Additionally, a custom early stopping algorithm was developed to eliminate the need for further hyperparameters. This study manifests that artificially training sub models that extract features with high accuracy aids models in predicting more complex physical behavior. On average, the prediction accuracy has been improved by ΔTcell=0.01 °C for the training data and by ΔTcell=0.007 °C for the validation data compared to the base model. In the field of electrical energy storage systems, this could reduce costs, increase safety and improve knowledge about the aging progress in an individual cell to sort out for second life applications.