Parthasarathy, Thirumalai NallasivanSalahshour, SoheılNarayanamoorthy, SamayanThilagasree, Chakkarapani SumathiMarimuthu, Palanivel RubavathiSalahshour, SoheilFerrara, MassimilianoAhmadian, Ali2024-09-112024-09-11202402590-123010.1016/j.rineng.2024.1022722-s2.0-85193518338https://doi.org/10.1016/j.rineng.2024.102272https://hdl.handle.net/20.500.14517/6159Ahmadian, Ali/0000-0002-0106-7050An adaptation to electric mobility quickens waste management tasks for recyclers to end-to-end processing of marketed electric vehicle batteries. Especially lithium-ion batteries play a prominent role in electrifying the world for e-transport technology innovation. This research offers a multi-attribute decision-making (MADM) structure for finding the best performance e-vehicle recycling techniques. The structured algorithm combines an advanced stratified MADM strategy with e-transportation recycling techniques. The optimal algorithm evaluates the results of qualitative attributes and alternatives using a weighted-ranking MADM approach. The importance of attributes is calculated using a blending of dual objective-weighted approaches: entropy and CILOS methods, viz., the aggregated IDOCRIW approach. The ranking of alternatives is determined through the COCOSO method in a hesitation environment. The q-rung orthopair picture fuzzy set (q-ROPFS) is used to cope with uncertainty and vagueness in decision analysis. The feasibility and robustness of the suggested algorithm were validated through different MADM methods and by altering crucial ranking-dependent parameters in the problem.eninfo:eu-repo/semantics/openAccessElectric vehicleEntropyCILOSCoCoSoq-rung picture fuzzyArticleQ122WOS:001301239900001