05 Fakultät Informatik, Elektrotechnik und Informationstechnik

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

Browse

Filters

20232

Settings

Institute: search.filters.UBSInstitut.Institut für PhotovoltaikSubject: search.filters.subject.004Subject: search.filters.subject.621.3

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    Investigating the production atmosphere for sulfide-based electrolyte layers regarding occupational health and safety
    (2023) Kreher, Tina; Jäger, Patrick; Heim, Fabian; Birke, Kai Peter
    In all-solid-state battery (ASSB) research, the importance of sulfide electrolytes is steadily increasing. However, several challenges arise concerning the future mass production of this class of electrolytes. Among others, the high reactivity with atmospheric moisture forming toxic and corrosive hydrogen sulfide (H2S) is a major issue. On a production scale, excessive exposure to H2S leads to serious damage of production workers’ health, so additional occupational health and safety measures are required. This paper investigates the environmental conditions for the commercial fabrication of slurry-based sulfide solid electrolyte layers made of Li3PS4 (LPS) and Li10GeP2S12 (LGPS) for ASSBs. First, the identification of sequential production steps and processing stages in electrolyte layer production is carried out. An experimental setup is used to determine the H2S release of intermediates under different atmospheric conditions in the production chain, representative for the production steps. The H2S release rates obtained on a laboratory scale are then scaled up to mass production dimensions and compared to occupational health and safety limits for protection against H2S. It is shown that, under the assumptions made for the production of a slurry-based electrolyte layer with LPS or LGPS, a dry room with a dew point of = - 40 C and an air exchange rate of AER = 30 1h is sufficient to protect production workers from health hazards caused by H2S. However, the synthesis of electrolytes requires an inert gas atmosphere, as the H2S release rates are much higher compared to layer production.
  • Thumbnail Image
    ItemOpen Access
    Temperature estimation in lithium-Ion cells assembled in series-parallel circuits using an artificial neural network based on impedance data
    (2023) Ströbel, Marco; Kumar, Vikneshwara; Birke, Kai Peter
    Lithium-ion cells are widely used in various applications. For optimal performance and safety, it is crucial to have accurate knowledge of the temperature of each cell. However, determining the temperature for individual cells is challenging as the core temperature may significantly differ from the surface temperature, leading to the need for further research in this field. This study presents the first sensorless temperature estimation method for determining the core temperature of each cell within a battery module. The accuracy of temperature estimation is in the range of DT=1 K. The cell temperature is determined using an artificial neural network (ANN) based on electrochemical impedance spectroscopy (EIS) data. Additionally, by optimizing the frequency range, the number of measurement points, input neurons, measurement time, and computational effort are significantly reduced, while maintaining or even improving the accuracy of temperature estimation. The required time for the EIS measurement can be reduced to 0.5 s, and the temperature calculation takes place within a few milliseconds. The setup consists of cylindrical 18,650 lithium-ion cells assembled into modules with a 3s2p configuration. The core temperature of the cells was measured using sensors placed inside each cell. For the EIS measurement, alternating current excitation was applied across the entire module, and voltage was measured individually for each cell. Various State of Charge (SoC), ambient temperatures, and DC loads were investigated. Compared to other methods for temperature determination, the advantages of the presented study lie in the simplicity of the approach. Only one impedance chip per module is required as additional hardware to apply the AC current. The ANN consists of a simple feedforward network with only one layer in the hidden layer, resulting in minimal computational effort, making this approach attractive for real-world applications.