Integrating Automation and Big Data in Lithium-Ion Battery Manufacturing: A Case Study of the Ultium Cells Joint Venture

Aim: To examine the integration of automation and big data in lithium-ion battery manufacturing using Ultium Cells joint venture as case study. Problem Statement: The havocs attached to the exhausts emission from fossil-fuel based automobiles are major concerns to the whole world. Records indicating lowering of air quality and depletion of the ozone layer have been reported. Furthermore, the quest to save money spent on non-renewable energy has necessitate the call for research studies on advancement of lithium-ion battery manufacturing. Also, the traditional battery manufacturing techniques production capacities cannot meet the demand for electric vehicles. Nonetheless, the consistency and quality control of the battery cell is challenging. Significance of Study: The use of electric vehicles is prevailing and thus has greatly influenced the need to technological improvement in the production of lithium-ion batteries (LIBs) which are being utilized in electric vehicles. The analysis of big data can provide real-time decision-making and long-term process improvements. This technical review is an eye-opener for researchers on the need to integrate automation and big data in lithium-ion battery manufacturing. Methodology: Recent literature materials in form of books, journals and relevant published articles in the area of automation and big data in lithium-ion battery manufacturing were consulted. Discussion: In this technical review, consideration was given to the integration of automation and big data in lithium-ion battery manufacturing using Ultium Cells joint venture as a case study. The types of lithium-ion batteries and their assemblies were discussed. The battery cells contain the cathode, anode and electrolyte and come in three varieties of designs which are pouches, prismatic cans and cylindrical designs. One of the main requirements that enhance the automation of assembly line in Li-ion battery manufacturing is the use of the collected data from the survey as stated by the industry. The concept is made up of 6 modular sectors such that each sector has the capacity of being scaled up and down based on customer requirements. The three major steps involved in data-driven application to lithium-ion battery cell manufacturing are data acquisition, data warehouse and data mining. Conclusion: The integration of automation and big data in lithium-ion battery manufacturing has positively influenced the quality and quantity of the products.