Image processing methods and light optical microscopy for in-situ quantification of chromatic change and anode dilation in Li-ion battery graphite anodes during (de-)lithiation
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A. Jansche
is currently pursuing his Ph.D. in applied machine learning for materials microscopy. He received his bachelor's degree in Computer Science and his research master's degree in Advanced Materials and Manufacturing from Aalen University, Germany. He has been working as a research assistant at the Materials Research Institute Aalen since 2015 and as a software engineer for automated microscopy since 2012. His research includes deep neural networks, computer vision and materials microscopy.
is currently pursuing his master's degree in the field of Engineering Sciences at TH Rosenheim. Since 2021 he has been working as a student assistant at Materials Research Institute Aalen. There he was working on semantic segmentation with deep neural networks for 2D and 3D data as well as image feature extraction for regression models. His research interests include deep neural networks and computer vision.
Abstract
In Lithium-ion batteries, the graphite anode is known to undergo a noticeable chromatic change during lithiation and de-lithiation by forming graphite intercalation compounds. Additionally, the graphite anode primarily contributes to the volume change of the battery. Using a novel in-situ optical microscopy setup for imaging cross-sections of Li-ion full cells, both effects can be studied simultaneously during charging and discharging. In this work, we describe feature extraction methods to quantify these effects in the image data (3730 images in total) captured during the lithiation and de-lithiation process. Automated and manual evaluations are compared. The images show graphite anodes and NMC 622 cathodes. For colorfulness, we evaluate different methods based on classical image processing. The metrics calculated with these approaches are compared to the results of ColorNet, which is a trainable colorfulness estimator based on deep convolutional neural networks. We propose a supervised semantic segmentation approach using U-Net for the layer thickness measurement and the anode dilation derived from it.
Kurzfassung
Die Graphitanode in Lithium-Ionen-Batterien ist dafür bekannt, dass sie sich durch die Ausbildung von Graphiteinlagerungsverbindungen während Lithiierung und Delithiierung merklich farblich verändert. Darüber hinaus wirkt sich die Graphitanode maßgeblich auf die Volumenänderung des Akkus aus. Mithilfe eines neuartigen optischen In-situ-Mikroskopieverfahrens zur Schnittbildgebung von Li-Ionen Vollzellen können beide Effekte während des Lade- und Entladevorgangs gleichzeitig untersucht werden. In diesem Beitrag beschreiben wir Merkmalsextraktionsverfahren zur Quantifzierung dieser Effekte in den während der Lithiierung und Delithiierung erfassten Bilddaten (insgesamt 3730 Bilder). Automatisierte und manuelle Auswertungen werden miteinander verglichen. Die Bilder zeigen Graphitanoden sowie NMC 622-Kathoden. Bezüglich der Farbigkeit werten wir verschiedene, auf klassischer Bildverarbeitung basierende Verfahren aus. Die so berechneten Metriken werden mit den mit ColorNet erzielten Ergebnissen verglichen. ColorNet ist ein auf tiefen neuronalen Faltungsnetzen (Deep Convolutional Neural Networks) basierender trainierbarer Schätzer der Farbigkeit. Wir stellen einen Ansatz zur überwachten semantischen Segmentierung vor, bei dem für die Schichtdickenmessung und die daraus abgeleitete Anodenausdehnung U-Net zum Einsatz kommt.
About the authors
is currently pursuing his Ph.D. in applied machine learning for materials microscopy. He received his bachelor's degree in Computer Science and his research master's degree in Advanced Materials and Manufacturing from Aalen University, Germany. He has been working as a research assistant at the Materials Research Institute Aalen since 2015 and as a software engineer for automated microscopy since 2012. His research includes deep neural networks, computer vision and materials microscopy.
is currently pursuing his master's degree in the field of Engineering Sciences at TH Rosenheim. Since 2021 he has been working as a student assistant at Materials Research Institute Aalen. There he was working on semantic segmentation with deep neural networks for 2D and 3D data as well as image feature extraction for regression models. His research interests include deep neural networks and computer vision.
6 Acknowledgements
The authors acknowledge funding projects CharLiSiKo (03XP0333A) and MiCha (03XP0317A) in the AQua cluster funded by the German Federal Ministry of Education and Research (BMBF).
6 Danksagung
Die Autoren bedanken sich für die Förderung der Projekte CharLiSiKo (03XP0333A) und MiCha (03XP0317A) im AQua-Cluster durch das Bundesministerium für Bildung und Forschung (BMBF).
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Articles in the same Issue
- Contents
- Editorial
- Editorial
- Image processing methods and light optical microscopy for in-situ quantification of chromatic change and anode dilation in Li-ion battery graphite anodes during (de-)lithiation
- A comprehensive study on the metallographic preparation of W-Re samples for high resolution electron-based microscopy
- Failure Analysis
- Hot Tearing in an Investment Cast Burner Swirler
- Picture of the Month
- Picture of the Month
- News
- Researchers develop optical method to verify topological phases in magnetic materials
- Meeting Dairy
- Meeting Diary
Articles in the same Issue
- Contents
- Editorial
- Editorial
- Image processing methods and light optical microscopy for in-situ quantification of chromatic change and anode dilation in Li-ion battery graphite anodes during (de-)lithiation
- A comprehensive study on the metallographic preparation of W-Re samples for high resolution electron-based microscopy
- Failure Analysis
- Hot Tearing in an Investment Cast Burner Swirler
- Picture of the Month
- Picture of the Month
- News
- Researchers develop optical method to verify topological phases in magnetic materials
- Meeting Dairy
- Meeting Diary
