Advanced Imaging and Machine Learning for PV Quality Assurance
Description
A multi-imaging setup is developed for investigating lab-scale solar cells and for obtaining spatially resolved information about the cell quality. Machine learning is used to estimate parameters for a physical FEM model that serves as a digital twin for further optimization.
Key Data
Projectlead
Deputy Projectlead
Co-Projectlead
Dr. Sandra Jenatsch
Project team
Jens Baier, Salome Berger, Ennio Comi, David Kempf, Dr. Christoph Kirsch, Prof. Dr. Hartmut Nussbaumer
Project partners
Fluxim AG; Solaronix S.A.
Project status
ongoing, started 01/2022
Funding partner
Innovationsprojekt / Projekt Nr. 58054.1 IP-EE
Publications
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Investigation of time and location dependent variations in electroluminescence images of perovskite solar cells
2023 Comi, Ennio; Jenatsch, Sandra; Blülle, Balthasar; Battaglia, Mattia; Torre Cachafeiro, Miguel Angel; Kirsch, Christoph; Hiestand, Roman; Aeberhard, Urs; Ruhstaller, Beat; Knapp, Evelyne
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Electro-thermal model for lock-in infrared imaging of defects in perovskite solar cells
2022 Comi, Ennio; Knapp, Evelyne; Battaglia, Mattia; Kirsch, Christoph; Weidmann, Stefano; Jenatsch, Sandra; Hiestand, Roman; Bonmarin, Mathias; Ruhstaller, Beat
