Volkswagen Ag

Leland Stanford Junior University

Vedran Glavas

Jonathan Edward Mueller

Julian Wegener

Friedrich B Prinz

Thomas Schladt

Shicheng Xu

Melanie Senn

Sebastian Kirsch

Nasim Souly

Gianina Alina Negoita

Gerold Huebner

Tanja Graf

Prateek Agrawal

Julian Koopmann

Stéphane Brice Olouou Guifo

Marco Wiethop

The invention relates to a secondary particle for a cathode of a secondary lithium battery cell that comprises a plurality of primary particles, each having a layered crystalline structure in which transition metal oxide layers and lithium layers alternate. Each of the primary particles has a spatial extension in the direction of its crystallographic c-axis that is greater than in a direction perpendicular to this axis. The invention also relates to a method for producing such a particle.

Secondary particle for a cathode of a secondary lithium battery cell and method for producing such a particle

A manufacturing process includes: depositing a catalyst support on a gas diffusion layer to form a catalyst support-coated gas diffusion layer; depositing a catalyst on the catalyst support-coated gas diffusion layer to form a catalyst-coated gas diffusion layer; and depositing an ionomer on the catalyst-coated gas diffusion layer to form an ionomer-coated gas diffusion layer. A membrane electrode assembly for a fuel cell includes: a gas diffusion layer; a polymer electrolyte membrane; and a catalyst layer disposed between the gas diffusion layer and the polymer electrolyte membrane, wherein the catalyst layer includes an ionomer, and a concentration of the ionomer varies within the catalyst layer according to a concentration profile.

Catalyst layer with extended surface area and method of forming such catalyst layer for polymer electrolyte fuel cells

A method, apparatus, system for batter material screening is disclosed. First, microstructure generation parameters for a plurality of microstructures are received, where the microstructure generation parameters include microstructure characteristics. Microstructure statistics are generated using a first artificial intelligence ('AI') model, where the received microstructure generation parameters are inputs for the first AI model. Microstructure properties are predicted using a second AI model for the microstructures based on the generated microstructure statistics, the received microstructure generation parameters, and battery cell characteristics. It is determined whether at least one of the microstructures is within a predefined energy profile range based on the predicted microstructure properties.

Battery materials screening

Growing community of inventors

Wolfsburg, Germany

Vedran Glavas