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 A novel long short-term memory approach for online state-of-health identification in lithium-ion battery cells(2024) Kopp, Mike; Fill, Alexander; Ströbel, Marco; Birke, Kai PeterRevolutionary and cost-effective state estimation techniques are crucial for advancing lithium-ion battery technology, especially in mobile applications. Accurate prediction of battery state-of-health (SoH) enhances state-of-charge estimation while providing valuable insights into performance, second-life utility, and safety. While recent machine learning developments show promise in SoH estimation, this paper addresses two challenges. First, many existing approaches depend on predefined charge/discharge cycles with constant current/constant voltage profiles, which limits their suitability for real-world scenarios. Second, pure time series forecasting methods require prior knowledge of the battery’s lifespan in order to formulate predictions within the time series. Our novel hybrid approach overcomes these limitations by classifying the current aging state of the cell rather than tracking the SoH. This is accomplished by analyzing current pulses filtered from authentic drive cycles. Our innovative solution employs a Long Short-Term Memory-based neural network for SoH prediction based on residual capacity, making it well suited for online electric vehicle applications. By overcoming these challenges, our hybrid approach emerges as a reliable alternative for precise SoH estimation in electric vehicle batteries, marking a significant advancement in machine learning-based SoH estimation.