05 Fakultät Informatik, Elektrotechnik und Informationstechnik

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

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Start date: 2024End date: 2024Institute: search.filters.UBSInstitut.Institut für PhotovoltaikAuthor: search.filters.author.Fill, AlexanderSubject: search.filters.subject.621.3

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    A high frequency alternating current heater using the advantages of a damped oscillation circuit for low voltage Li-ion batteries
    (2024) Oehl, Joachim; Gleiter, Andreas; Manka, Daniel; Fill, Alexander; Birke, Kai Peter
    In many cases, batteries used in light e-mobility vehicles such as e-bikes and e-scooters do not have an active thermal management system. This poses a challenge when these batteries are stored in sub-zero temperatures and need to be charged. In such cases, it becomes necessary to move the batteries to a warmer location and allow them to acclimatize before charging. However, this is not always feasible, especially for batteries installed permanently in vehicles. In this work, we present an internal high-frequency AC heater for a 48 V battery, which is used for light electric vehicles of EU vehicle classes L1e and L3e-A1 for a power supply of up to 11 kW. We have taken advantage of the features of a damped oscillating circuit to improve the performance of the heater. Additionally, only a small inductor was added to the main current path through a cable with three windings. Furthermore, as the power electronics of the heater is part of the battery main switch, fewer additional parts inside the battery are required and therefore a cost and space reduction compared to other heaters is possible. For the chosen setup we reached a heating rate of up to 2.13 K min -1 and it was possible to raise the battery temperature from -10 °C to 10 °C using only 3.1% of its own usable capacity.
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    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 Peter
    Revolutionary 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.