Fuzzy quality factor for environmental impact assessment of lithium-ion battery recyclability - Chua, Howayne O. (Animo Repository, 2021)
The onset of global warming and climate change have driven scientists to develop strategies which will simultaneously produce energy and yet combat its harmful effects. One of the emerging technologies is renewable energy. Since renewable energy is not always available, the battery serves as a critical excess energy storage component to facilitate the wide scale adoption of the system. Typically, lead-acid batteries are being used for renewable energy systems; however, they are slowly being replaced by more energy dense and longer lasting lithium-ion batteries. With the increasing demand for renewable energy, it is also expected that there would be an increase in volume of waste generated from spent batteries. Various strategies for the recovery and reuse of lithium-ion batteries are being considered to minimize their environmental impact. Such mechanisms are essential to facilitate the transition towards a more circular economy where material recycling and minimization of environmental impacts are encouraged. However, previously developed circularity indicators such as recycling rate do not possess factoring in waste quality uncertainty which is an important factor in gauging circularity. This paper addresses the gap by modeling waste quality uncertainty through its real-time performance ratio using fuzzy set theory. The waste quality uncertainty is integrated as a factor to existing circularity assessment method to form a range of modified recycling rate circularity index values. Furthermore, it is integrated to the impact analysis to illustrate the approximate effect range of factoring quality uncertainty in circularity assessments. The result of the improvised framework is further analyzed with uncertainty analysis. It shows that the correlation between recycling rates and environmental impacts at different quality ratio factors is still in agreement with current circularity trends from previous studies made.