Motivated by the success in terms of learning efficiency and engagement of our previous VR and MR educational tools, in this Concept, we present our Battery Manufacturing Metaverse (BMM), an innovative educational tool designed to promote accessibility, inclusion and collaborative learning of the manufacturing process of battery electrodes and cells in an interactive and very flexible VR representation of a battery pilot line. The BMM has been designed for integration and collaborative learning between the universities (Figure ) under the i-MESC (Interdisciplinarity in Materials for Energy Storage and Conversion) MSc. program . The BMM enables interactive knowledge sharing and collaborative practices across different geographical locations in order to engage, include and learn effectively. i-MESC is a 2-year Erasmus Mundus Joint Master program (2023-2029) designed to prepare professionals to address energy challenges. It focuses on a variety of energy technologies such as rechargeable batteries, supercapacitors and fuel cells, offering interdisciplinary training in materials, device manufacturing, battery engineering, Artificial Intelligence (AI), and digital twins, alongside soft skills, with the support of innovative learning methods based on VR, MR and the BMM in particular. BMM pushes very significantly the boundaries of Simubat 4.0. Building on its foundation, the BMM introduces three significant advancements. Firstly, users can now select the chemistry of their cell, providing greater flexibility and customisation. Secondly, the platform enables multiple users to join the same instance of the pilot line environment, allowing real-time collaboration in VR through virtual avatars and voice chat. Thirdly, users have the freedom to adjust the process parameter values of any machine. Then, it uses this information to predict the resulting electrode properties and cell performance, such as the electrode mass loading and thickness and the corresponding gravimetric cell energy density. It also generates a Galvanostatic Cycling with Potential Limitation (GCPL) curve for the chemistry selected, which helps the users understand how the chemistry affects the charge-discharge characteristics of the cell. To ensure maximum flexibility in educational and research scenarios, the BMM also enables the creation of virtual half cells and unconventional full cells containing identical electrode materials on both sides. This openness allows students and researchers to intentionally try "incorrect" configurations for maximal flexibility in possible pedagogic scenarios, fostering a deeper insight into battery manufacturing principles. Additionally, the application offers detailed feedback based on the chosen parameter values, enriching the learning and experimentation process. These very significant advancements empower users to experiment with new recipes and observe the effects of different parameter combinations, marking a significant step forward in virtual battery manufacturing education, training, and research.