Model-Based Assessment of Energy and Material Efficiency in Battery Cell Production

Battery cell production is composed of numerous heterogeneous processes and interdependencies across different production levels, such as processes and products. The growing demand for batteries further increases this complexity, as it requires scaling existing processes and integrating new chemistries and technologies. As the material and energy flows within battery cell production affect both the technosphere and the ecosphere, achieving environmental sustainability requires a comprehensive understanding of these flows and their environmental implications. To meet this need, this work proposes a model-based methodology for digitally representing and investigating battery cell production systems, assessing energy and material efficiency while considering interdependencies between different elements of production systems. The modularity of the proposed modeling approach allows diverse stakeholders (e.g., production designers and sustainability experts) to identify unknown interdependencies and support knowledge-based decision-making. A flexible modeling of different elements within battery cell production systems accounts for differences in production, including process technologies and production capacities. The detailed, dynamic material flow modeling enables tracking of materials used in production and the assessment of how product quality and scrap rates influence material efficiency. Similarly, the dynamic energy flow modeling allows the reproduction of weather-related variations in energy demand and the impact of dynamic events (e.g., machine breakdowns and bottlenecks). The methodology also considers product embodied energy, accounting for indirect contributions from technical building services and non-productive machine states, thereby supporting the assessment of production performance and the identification of potential energy efficiency improvements. Furthermore, it includes the analysis of material-energy-carbon interdependencies, delivering a detailed breakdown of results at both product and process levels to support stakeholders with varying expertise and focus areas. The proposed methodology is prototypically implemented in Python, combining a process chain model for simulating material and energy flows with discrete event and agent-based modeling approaches. Complementary process- and product-specific models offer deeper insights into how process parameters influence electrode structure and battery cell performance. Three case studies demonstrate the application of the methodology to (i) evaluate different production system designs under consideration of location, weather conditions, and electricity mix, (ii) compare different process technologies, and (iii) analyze the impact of product design at both process and production chain levels. The high temporal resolution of the results enables the identification of the impact of simulated scenarios on material and energy demand, as well as the carbon emissions associated with each battery cell produced. These findings contribute to a greater understanding of efficiency measures and environmental impacts, highlighting the importance of a comprehensive modeling approach for sustainable battery cell production.