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HANNOVER MESSE 2019, 01 - 05 April
Homepage>Conference program >Deep learning methods in microscopy for assessing the quality of lithium ion batteries
tech transfer Forum

Deep learning methods in microscopy for assessing the quality of lithium ion batteries

Location & Language

Hall 2, Stand C02

Language

English

Event Details

Type of event

Lecture

Topic

Artificial Intelligence, Integrated Energy, Research & Technology

Event series

tech transfer Forum

Session

Innovations from Baden-Württemberg

Event Host

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Description

Major advances have been made recently in the field of machine learning (ML) and specifically deep learning following decades of intensive research. The consequence of this sudden progress extends to almost every industry and the field of material science is no exception.

In this presentation we use computer vision techniques for the quality assessment of large prismatic Li-ion batteries. The performance of a Li-ion battery is intrinsically linked to the electrode microstructure. Quantitative and qualitative measurements of key structural parameters will enable optimization as well as motivate systematic numerical studies for the improvement of the battery performance. The aim is to automatically evaluate various cell components (cathode, anode, and separator) geometrically in high-resolution and to also identify various types of defects in the microstructure such as metal particle contamination, layer deformation, cracks etc..

Speaker

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