Multi-dimensional evaluation framework for electric vehicle battery recycling

Developing sustainable battery lifecycle management strategies for the rapidly expanding electric vehicle sector is essential to improve the sustainability and resilience of battery supply chains. To support strategic decision-making under uncertainty, this study proposes a q-rung orthopair fuzzy set (q-ROFS)-based Combinative Distance-Based Assessment (CODAS) framework for the comprehensive evaluation of battery recycling methods. A multi-criteria decision-making model was developed by identifying ten sub-criteria grouped under the three main dimensions of technological requirements, sustainability, and productivity through an extensive literature review and expert evaluations. Using this framework, seven battery recycling methods were assessed and ranked according to their overall performance. To validate the robustness and reliability of the proposed approach, comparative analyses with other fuzzy MCDM methods, q-level analyses, and sensitivity analyses of the criteria weights were conducted. The results indicate that hydrometallurgy is the most suitable recycling method, followed by direct regeneration, whereas mechanochemistry ranks as the least preferred alternative. The proposed q-ROFS-based CODAS framework effectively captures uncertainty, accommodates variations in expert judgments, and considers the interactions among evaluation criteria, providing a robust and reliable decision-support tool for sustainable battery recycling policy selection.