Electrode processing plays an important role in advancing lithium-ion battery technologies and has a significant impact on cell energy density, manufacturing cost, and throughput. Compared to the extensive research on materials development, however, there has been much less effort in this area. In this Review, we outline each step in the electrode processing of lithium-ion batteries from materials to cell assembly, summarize the recent progress in individual steps, deconvolute the interplays between those steps, discuss the underlying constraints, and share some prospective technologies. This Review aims to provide an overview of the whole process in lithium-ion battery fabrication from powder to cell formation and bridge the gap between academic development and industrial manufacturing.

From Materials to Cell: State-of-the-Art and Prospective Technologies for Lithium-Ion Battery Electrode Processing.

Machine vision significantly improves the efficiency, quality, and reliability of defect detection. In visual inspection, excellent optical illumination platforms and suitable image acquisition hardware are the prerequisites for obtaining high-quality images. Image processing and analysis are key technologies in obtaining defect information, while deep learning is significantly impacting the field of image analysis. In this study, a brief history and the state of the art in optical illumination, image acquisition, image processing, and image analysis in the field of visual inspection are systematically discussed. The latest developments in industrial defect detection based on machine vision are introduced. In the further development of the field of visual inspection, the application of deep learning will play an increasingly important role. Thus, a detailed description of the application of deep learning in defect classification, localization and segmentation follows the discussion of traditional defect detection algorithms. Finally, future prospects for the development of visual inspection technology are explored.

/pdf/state-of-the-art-in-defect-detection-based-on-machine-vision-2rtzvgsnqy.pdf

State of the Art in Defect Detection Based on Machine Vision

This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily understandable, review of general interest to the battery community. It addresses the concepts, approaches, tools, outcomes, and challenges of using AI/ML as an accelerator for the design and optimization of the next generation of batteries-a current hot topic. It intends to create both accessibility of these tools to the chemistry and electrochemical energy sciences communities and completeness in terms of the different battery R&D aspects covered.

Artificial Intelligence Applied to Battery Research: Hype or Reality?

Automated surface defect detection framework using machine vision and convolutional neural networks

Machine learning toward advanced energy storage devices and systems.

During the manufacturing of lithium-ion battery electrodes, it is difficult to prevent certain types of defects, which affect the overall battery performance and lifespan. Deep learning computer vision methods were used to evaluate the quality of lithium-ion battery electrode for automated detection of microstructural defects from light microscopy images of the sectioned cells. The results demonstrate that deep learning models are able to learn accurate representations of the microstructure images well enough to distinguish instances with defects from those without defect. Furthermore, the benefits of using pretrained networks for microstructure classification were also demonstrated, achieving the highest classification accuracies. This method provides an approach to analyse thousands of Li-ion battery micrographs for quality assessment in a very short time and it can also be combined with other common battery characterization methods for further technical analysis.

Image-based defect detection in lithium-ion battery electrode using convolutional neural networks

Neue Werkstoffe sind oft der entscheidende Treiber bei der Entwicklung innovativer Produkte, so auch im Automobilbau. Gerade bei den Schlusselkomponenten Energielieferant, Energiewandler und Leichtbaukonstruktionen lasst die Pulvermetallurgie signifikante Innovationspotenziale fur die Elektromobilitat erwarten. Im vorliegenden Artikel soll eine Ubersicht gegeben werden uber die fur die Elektromobilitat wichtigen Komponenten, bei denen die Pulvertechnologie eine Bedeutung spielen kann. An einigen Beispielen werden der Forschungsbedarf und die Zukunftspotenziale der Pulvertechnologie diskutiert.

Pulvertechnisch hergestellte Werkstoffe für die Elektromobilität — Teil 1: Batterien

Neue Werkstoffe sind oft der entscheidende Treiber bei der Entwicklung innovativer Produkte, so auch im Automobilbau. Gerade bei den Schlusselkomponenten Energielieferant, Energiewandler und Leichtbaukonstruktionen lasst die Pulvermetallurgie signifikante Innovationspotenziale fur die Elektromobilitat erwarten. Im vorliegenden Artikel soll eine Ubersicht gegeben werden uber die fur die Elektromobilitat wichtigen Komponenten, bei denen die Pulvertechnologie eine Bedeutung spielen kann. Nachdem im vorausgehenden Teil 1 Batterien [1] und in Teil 2 Magnete [2] behandelt wurden, geht es in diesem Teil 3 um die Additive Fertigung.

Pulvertechnisch hergestellte Werkstoffe für die Elektromobilität - Teil 3: Additive Fertigung

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Image-based defect detection in lithium-ion battery electrode using convolutional neural networks

Pulvertechnisch hergestellte Werkstoffe für die Elektromobilität — Teil 1: Batterien

Pulvertechnisch hergestellte Werkstoffe für die Elektromobilität - Teil 3: Additive Fertigung