Inline evaluation of Li-ion battery electrode porosity using machine learning algorithms (BattPor)

Project

One component for increasing the performance of Li-ion batteries is the porosity of the battery electrodes. This is adjusted during calendering by redensifying the coated and dried electrodes.

An optical inspection system based on Laser Speckle Photometry (LSP) will be developed for this purpose. For the application, the LSP sensor must be adapted to the roll-to-roll system and the industrial working speed. LSP reference measurement values are recorded and reference values for AI use are derived and collected in a database. This will later be used to develop algorithms for testing and adjusting the desired calendering parameters.

The BattPor project starts with a Technology Readiness Level (TRL) of 2. During the project, the application of the novel LSP method will be validated and the proof-of-concept will be demonstrated analytically and experimentally. The project will deliver a laboratory demonstrator including a data evaluation algorithm with a TRL of 4. This will allow the technology to be experimentally validated as an innovation module in research centers and industry and used to qualify in-house processes. The aim is to transfer the technology to electrode production factories.

 

Funding: M-ERA.NET

Project period: 01.06.2022-31.05.2025

Project partner:

  • Fraunhofer IKTS (coordination)
  • University of Innsbruck (Department of Structural Engineering and Material Sciences, Unit of Material Technology Innsbruck)
  • Phystech Coating Technology GmbH
  • IISAS (Institute of Informatics, Slovak Academy of Science)