Industrial manufacturing processes have evolved and improved since the disruption of the Industry 4.0 paradigm, while energy has progressively become a strategic resource required to maintain industrial competitiveness while maximizing quality and minimizing environmental impacts. In this context of global changes leading to social and economic impact in the short term and an unprecedented climate crisis, Digital Twins for Energy Efficiency in manufacturing processes provide companies with a tool to address this complex situation. Nevertheless, already existing Digital Twins applied for energy efficiency in a manufacturing process lack a flexible structure that easily replicates the real behavior of consuming machines while integrating it in complex upper-level environments. This paper presents a combined multi-paradigm approach to industrial process modeling developed and applied during the GENERTWIN project. The tool allows users to predict energy consumption and costs and, at the same time, evaluates the behavior of the process under certain productive changes to maximize consumption optimization, production efficiency and process flexibility.

The growing demand for lithium-ion batteries, driven by the electrification of transportation and the expansion of renewable energy systems, is accelerating the deployment of large-scale manufacturing facilities worldwide. This rapid industrial growth increases the need to ensure high product quality and process reliability, given the complexity and strong interdependence of battery manufacturing stages. In this context, traceability becomes a key requirement for monitoring production processes, detecting deviations, and ensuring consistent performance. Digital twins, defined as virtual representations of physical systems integrating data, models, and simulation tools, are emerging as a promising approach to address these challenges. By enabling enhanced process visibility, predictive capabilities, and decision support, digital twins can contribute to improved control and optimization of battery manufacturing processes. This paper presents a review of current developments in digital twin applications for lithium-ion battery cell production and highlights the potential benefits that these tools can offer to the battery industry, particularly in supporting traceability, process optimization, and quality assurance in next-generation gigafactories. The outcomes of this review provide actionable insights for both academia and industry by identifying research gaps, technological limitations, and opportunities to advance the development and industrial adoption of digital twins in battery cell manufacturing.