• Dermenci, Kamil Burak
• Berecibar, Maitane
• Yadav, Poonam
• Dammala, Pradeep Kumar
• Van Mierlo, Joeri
• Kathribail, Anish Raj

Abstract

Lithium ion batteries (LiBs) continue to be the most advanced technology in the battery systems as the world rushes to meet the diverse and expanding demands of the energy storage solutions. Research institutions, academia and industries requires a safer, high-performance and cheaper LiBs to accelerate the transition from oil-based to an electrical-based economy. Because of some interdependent electrochemical kinetics involved in the LiB chemistry, and time it takes for the fabrication process it became one of the challenging aspects in this modern day life as it is time consuming and needs to be updated with upcoming materials and methodologies[1]. To overcome these challenges quickly, introduction of digital tools [2] which can optimize the parameters of making LiBs are being researched and are trying to implement them in the battery manufacturing industry. Typically, the state of art manufacturing of batteries is a sequence of intermittent steps like slurry preparation, coating and drying, electrode cutting, calendaring, stacking pouch cell formation, electrolyte filling, sealing and mechanical and electrochemical testing which have to be precisely controlled and optimize each dependent parameters carefully and reorganize them for the fabrication to adopt to new systems which takes a lot of effort and machine handling for new innovative battery technologies. Automation of this manufacturing process with Artificial Intelligence (AI), Machine learning(ML) or Internet of Things (IoT) is the new way of approach [3]. These approaches can help the research and battery manufacturing plants to meet the demands of cost effectiveness, sustainability, time needs and scalability. Digitalization of these techniques on one hand can reduce the time to market and provide a profitable manufacturing and on the other hand it can guide the cell prototyping and advanced cell chemistry to the new manufacturing tools in the virtual way. Thus the designing tools cost, prototyping cost also can be reduced. The abstract reviews both experimental and computational approach to undergo smooth transition in the battery manufacturing process. ReferencesWitt, D. et al. Myth and Reality of a Universal Lithium-Ion Battery Electrode Design Optimum: A Perspective and Case Study. Energy Technol. 9 , (2021).Ramakrishna, S., Khong, T. C. & Leong, T. K. Smart Manufacturing. Procedia Manuf. 12 , 128–131 (2017).dos Reis, G., Strange, C., Yadav, M. & Li, S. Lithium-ion battery data and where to find it. Energy AI 5 , (2021).

Topics

• impedance spectroscopy
• Lithium
• drying
• machine learning