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.Birke, Kai PeterStart date: 2024End date: 2024Subject: search.filters.subject.620

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Now showing 1 - 3 of 3
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    A dual‐layered anode buffer layer structure for all solid‐state batteries
    (2024) Lu, Yushi; Chang, Hansen Michael; Birke, Kai Peter
    Over the past few decades, lithium‐ion batteries have garnered considerable attention, especially for their use in electric vehicles (EVs). In recent years, solid‐state batteries have become increasingly popular due to their excellent safety features and potential for high energy density. However, solid‐state batteries with lithium metal anodes present challenges in terms of electrochemical reactivity and cost. To address these challenges, alternative anode systems such as the “anode‐free” approach are being explored. In this study, we introduced a dual‐layered anode comprising a primary layer of physically vapor‐deposited zinc and a secondary layer of carbon black, focusing on investigating the influence of varying thicknesses of the lithiophilic zinc layer on cell cycling performance. Among the three different zinc thicknesses chosen for this purpose - categorized as thin (286 nm), medium (1.802 μm), and thick (6.519 μm) - the dual‐layered anode buffer layer was analyzed in a single‐layer full pouch cell. An in‐depth investigation into the lithium‐zinc alloying behavior was conducted through post‐mortem analysis. From the results, we found that the combination of the zinc layer with the carbon black layer improved cell cycling performance in terms of discharge capacity retention compared to a single layer of either zinc or carbon black. The cycling performance of this dual‐layered anode could be further enhanced by optimizing the zinc layer thickness, likely due to the irreversible alloying step of zinc and lithium. Among the various thicknesses evaluated, the thin zinc layer (286 nm) combined with the carbon black layer demonstrated the most promising cycling performance in all solid‐state batteries.
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    Influence of temperature and pressure on the wetting progress in 21700 lithium‐ion battery cells : experiment, model, and lattice Boltzmann simulation
    (2024) Wanner, Johannes; Burgard, Matthias; Othman, Nabih; Singh, Soumya; Birke, Kai Peter
    The electrolyte filling and subsequent wetting of the active material is a time‐critical process in the manufacturing of lithium‐ion batteries. Due to the metallic cell housing, the process phenomena are insufficiently accessible, preventing the replication of the wetting processes by mathematical or simulative methods and hindering efforts to accelerate the wetting process. Therefore, this publication employs a glass cell housing for electrolyte filling of a 21700 cylindrical cell to investigate the wetting at different temperatures and process pressures. In parallel, a mathematical replication of the wetting, as well as a lattice Boltzmann pore‐scale simulation, is used to evaluate the influence of these varying process boundary conditions. The results show a strong temperature dependence on electrolyte wetting and the positive effect of pressure changes in the wetting process. These findings are particularly relevant to the process design of large‐scale cylindrical cell manufacturing.
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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.