Closing gaps in LCA of lithium-ion batteries: LCA of lab-scale cell production with new primary data. (15th January 2023) - Erakca, Merve; Pinto Bautista, Sebastián; Moghaddas, Samineh; Baumann, Manuel; Bauer, Werner; Leuthner, Lea; Weil, Marcel
Abstract: Battery storage systems have become an important pillar in the transformation of the energy and transportation sector over the last decades. Lithium-ion batteries (LIBs) are the dominating technology in this process making them a constant subject of analysis regarding their sustainability. To assess their environmental performance, several Life Cycle Assessments (LCA) of LIBs have been performed over the last years. Yet, the amount of available primary data on their production remains low, leading to recurrent reliance on a few disclosed datasets, mostly at industrial scale. Thus, there is a need for new LCA studies at different scales (lab, pilot, industrial) using transparent datasets to facilitate more reliable and robust assessments. This work presents a screening of recent environmental assessments for LIBs at different production scales aiming at identifying remaining gaps and challenges, and deriving a detailed LCA of a lab-scale battery cell production. For the first time the environmental impact of a lab-scale battery production based on process-oriented primary data is investigated. The results are flanked by sensitivity analyses and scenarios and compared with literature values. The hotspots identified in this study, cathode slurry, anode current collector, as well as the energy demand of the dry room and coating process, are consistent with the literature, although the absolute values are an order of magnitude larger. The main reason for this are the inefficiencies inherent in lab-scale production. In order to analyze the effects of production scale, an upscaling to the pilot scale is performed. Highlights: Extensive review of relevant environmental assessment studies of LIB manufacturing. First LCA for LIB cell manufacturing at laboratory scale. Compiling fully transparent process-based primary data. Theoretical scale-up and consequent analysis of lab-scale data to pilot scale.