Analysis of nickel sulphate datasets used in lithium-ion batteries

Electric vehicles are increasingly seen as a key strategy in reducing the dependency on fossil fuels and subsequently the environmental impacts of the transportation sector. However, the production of the traction batteries has been identified as an important environmental hotspot. The active materials used in the production of batteries has a significant contribution toward the environmental impacts, especially the production of cobalt and nickel sulphates. Recent trends indicate a shift toward high nickel content-based batteries. Therefore, there is a need to understand the existing nickel sulphate datasets used in battery studies. It is essential to identify the representativeness and source of difference in existing datasets. A thorough analysis of the existing datasets can aid in selecting the appropriate dataset and indicate the hotspots in nickel sulphate production. In this study, we analyze the production of nickel sulphate based on different datasets used in life cycle assessment studies. The datasets considered in the analysis are provided by the ecoinvent 3.8 database, GREET model 2021, Sphera Solutions, and other recent literature sources. The environmental impacts considered for the analysis are global warming, terrestrial acidification, particulate matter formation, and energy demand. The results indicate there is a high variability in due to the feedstock composition of primary nickel, the energy mix used in nickel sulphate production and the usage of recycled nickel. The global warming potential, for example, varies from 4.6 kg CO2-eq to 19.6 kg CO2-eq. Considering the growing global demand for lithium-ion battery production until 2030, this would indicate a variability of about 23 million metric tons of CO2 eq. The study reiterates the need for technology specific and ore grade specific primary data representative of future nickel sulphate production. The existing datasets are aggregated and do not offer the specificity needed to model representative supply chain configurations.