Artificial intelligence model-based computing device and analysis method for optimal design of secondary battery electrolyte

Abstract

Proposed is an artificial intelligence (AI) model-based analysis method for an optimal design of a secondary battery electrolyte. The AI model-based analysis method may include obtaining a tomography image of a calendared battery material, and inputting the tomography image to a pre-trained AI model. The method may also include outputting a viscosity and a transmittance of a battery electrolyte through the AI model. The AI model may include a first AI model configured to analyze porosity distribution and tortuosity information about the tomography image input thereto. The AI model may also include a second AI model configured to output a viscosity and a transmittance corresponding to the electrolyte, based on the porosity distribution and tortuosity information.

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