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
Ennio Comi, David Kempf, Prof. Dr. Hartmut Nussbaumer, Jens Baier, Salome Berger, Dr. Christoph Kirsch
Project partners
Fluxim AG; Solaronix S.A.
Project status
completed, 01/2022 - 03/2025
Institute/Centre
Institute of Computational Physics (ICP); Institute of Energy Systems and Fluid Engineering (IEFE); Institute of Product Development and Production Technologies (IPP)
Funding partner
Innovationsprojekt / Projekt Nr. 58054.1 IP-EE
