05 Fakultät Informatik, Elektrotechnik und Informationstechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6
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Item Open Access Cycling of double-layered graphite anodes in pouch-cells(2022) Müller, Daniel; Fill, Alexander; Birke, Kai PeterIncremental improvement to the current state-of-the-art lithium-ion technology, for example regarding the physical or electrochemical design, can bridge the gap until the next generation of cells are ready to take Li-ions place. Previously designed two-layered porosity-graded graphite anodes, together with LixNi0.6Mn0.2Co0.2O2 cathodes, were analysed in small pouch-cells with a capacity of around 1 Ah. For comparison, custom-made reference cells with the average properties of two-layered anodes were tested. Ten cells of each type were examined in total. Each cell pair, consisting of one double-layer and one single-layer (reference) cell, underwent the same test procedure. Besides regular charge and discharge cycles, electrochemical impedance spectroscopy, incremental capacity analysis, differential voltage analysis and current-pulse measurement are used to identify the differences in ageing behaviour between the two cell types. The results show similar behaviour and properties at beginning-of-life, but an astonishing improvement in capacity retention for the double-layer cells regardless of the cycling conditions. Additionally, the lifetime of the single-layer cells was strongly influenced by the cycling conditions, and the double-layer cells showed less difference in ageing behaviour.Item Open Access 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 PeterThe 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.Item Open Access Novel approach to ensure safe power supply for safety-relevant consumers(2022) Braun, Lars; Le, Minh; Motz, Jürgen; Birke, Kai PeterThe 12 V powernet in vehicles must fulfill certain safety requirements due to the safety demand of consumers. A potential risk is undervoltage for a safety-relevant consumer, which leads to its fault. Therefore, a novel approach is presented in this study, which can predict the minimum terminal voltage for consumers. This consists of diagnostics of the wiring harness and of the lead-acid battery as well as predefined consumer currents. Using simulation, first the beginning of a drive cycle is simulated to determine the state of the powernet, and afterwards a critical driving maneuver is simulated to validate the predicted minimum terminal voltage. It demonstrates that the novel approach is able to predict a fault due to undervoltage. In addition to fulfilling safety requirements, the novel approach could be used to achieve additional availability and miniaturization of powernet components compared to the state of the art.