A critical review of future aspects of digitalization next generation Li-ion batteries manufacturing process

As the world rushes to expedient the growing demands for energy utilization and storage solutions, Lithium-ion batteries (LIBs) are dominating in almost every sector of the battery systems. Recent research and development in the continuing energy revolution have demonstrated that LIBs are a viable technology for portable gadgets and Electric Vehicles(EV). This is primarily owing to their exceptional performance capabilities and reasonably favorable cell durability. Nevertheless, the optimization of manufacturing electrodes is a crucial factor in guaranteeing the producing top of the line LIBs. The production of LIBs is a complicated system because of the interdependent electrochemical kinetics involved in their chemistry. It has become one of the challenging aspects due to the continuous update of new materials and methodologies. The introduction of digital tools to overcome challenges can optimize these parameters of manufacturing LIBs which are being researched and reaching a phase of implementation in the related industry. Typical state-of-art manufacturing of batteries is a sequence of interdependent steps like slurry preparation, coating and drying, electrode cutting, calendering, stacking, pouch cell formation, electrolyte filling, sealing, and testing, which need precise control. Optimization of the process for each dependent parameter with cautiousness and reorganize them to adopt new innovative battery technologies, which takes a painstaking effort and machine handling to implement. Automation using Machine learning (ML), the Internet of Things (IoT), and Artificial Intelligence (AI) are the ultra-modern approach to the manufacturing process. Digitalization of these techniques provides profitable manufacturing and guides cell prototyping and advanced cell chemistry to the new manufacturing tools in a virtual way. Hence, designing tools and prototyping costs can be reduced. This paper looks at both experimental and computational approaches to make a smooth transition in the process of battery manufacturing.