Modelling of Energy Storage Materials Manufacturing

Dear Colleagues,

The demand for the electrification of the transport industry and economical energy storage devices is driving the development of polymeric batteries. Understanding the physics of “materials and manufacturing processes” will help tuning the production of energy storage devices and optimise the performance by controlling the texturing of materials, improving quality, increasing safety, and reducing wastes. In this Special Issue, we will focus on the modelling and manufacturing of energy storage materials, ranging from material characteristics (chemistry, rheology, topology, and micro-/meso-structure), processing methods, and post-processing stages (drying and calendaring).

The aim of the current Special Issue is to unravel the potentials of using physics-based modelling and emerging areas like machine learning (ML) to remove the current hurdles of the high-value manufacturing of energy storage materials. Contributors from both academia and industry are welcomed to present their work. Areas covered in this research topic include, but are not limited to, the following:

• Physics-based modelling and computational approaches (multiscale multiphysics modelling; continuum and discrete modelling);
• Role of materials characteristics in the manufacturing of energy storage materials;
• Manufacturing of energy storage materials;
• Post-processing of energy storage materials;
• Image analysis and applications of new techniques (X-ray and nano-computed tomography—XCT) in designing energy storage materials;
• Machine learning (ML);
• data mining;
• artificial intelligence (AI);
• statistical learning;
• data-driven modelling.

Dr. Masoud Jabbari
Dr. Jie Lin
Dr. Weihan Li
Guest Editors

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