Digital twin design and analytics for scaling up electric vehicle battery production using robots

Abstract

As electric vehicle adoption accelerates and demand increases, the inability to produce batteries in sufficient quantities has emerged as a critical bottleneck in the electric vehicle supply chain. Given the impending climate change crisis, resolving this bottleneck is imperative to accelerate the transition to a zero-emission electric mobility future. One potential solution is the use of robotics for fast and cost-effective assembly of batteries at scale. This study proposes a three-stage digital twin design and analysis method to develop robotic workcells for fast and cost-effective assembly of electric vehicle battery modules. Using digital twin design and simulation, robotic assembly line configurations have been developed for battery module production at different scales. Digital twin analytics was used to evaluate and optimise the proposed robotic battery assembly system for speed and cost. Industrial automation experts were consulted to further improve robotic work cell layouts to minimise investment in robots. Because digital twins of robotic workcells have been used, the configurations of the battery assembly line, as designed and validated, are ready for immediate implementation. For practitioners, this study offers heuristic methods to determine the appropriate assembly line configuration, the required number of robots and humans, for a desired production volume. For researchers, this study outlines promising areas for future investigation.

Keywords:

• Digital twin
• digital twin design and analytics
• electric vehicles
• batteries
• robots
• sustainability

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article [and/or]its supplementary materials.

Additional information

Notes on contributors

Ajit Sharma

Dr. Ajit Sharma received his Ph.D. degree in Technology and Operations from the University of Michigan, Ann Arbor, MI, USA. He also holds a B. Tech. in manufacturing engineering, a masters in industrial management, and an MBA from the Ross school of business. Before entering academia, he worked in industry for 12 years, primarily in the robotics industry. He has worked for firms, such as FANUC Robotics America, Xerox, and General Motors. He has also done startups in the areas of robotics and technology consulting. He started his academic career at Carnegie Mellon University as a professor of business technologies, teaching courses in business technology consulting and information systems, for which he received the Dean's Teaching Award. He is currently an Assistant Professor of technology, innovation, and entrepreneurship at Wayne State University, Detroit, MI, USA. His research interests are in the applications of AI and robotics for amplifying the potential of individuals and organisations. Dr. Sharma is passionate about the societal implications of technology. Since 2015, he has helped found and run LIME Lab L3C, a low profit organisation that offers pro-bono robotics and technology training to K12 kids in Detroit.

Manoj Kumar Tiwari

Prof. Manoj Kumar Tiwari (FNAE, FNASc, FIISE) is the Director of National Institute of Industrial Engineering (NITIE) Mumbai and Professor with Higher Academic Grade (HAG) in the Department of Industrial and Systems Engineering at the Indian Institute of Technology, Kharagpur. He is a fellow of The National Academy of Sciences India (NASI), and Indian National Academy of Engineering (INAE), and the Institute of Industrial and Systems Engineers (IISE), USA. He is actively involved in research relevant to the application of optimisation, modelling, decision support systems, and data mining in logistics, supply chain management, and manufacturing research domains. He is associate/senior editor of several renowned journals like IJPR, POMS, JIM, IEEE-SMCA etc. Prof. Tiwari is an author of more than 348 articles in leading international journals with an H index-73.