A hybrid unified decoding analytics system for end-of-life strategies in mass-produced EV battery projects

The rapid expansion of electric vehicle markets has intensified the need for effective investment and end-of-life decision frameworks for the first generation of mass-produced electric vehicle (EV) batteries. The core problem in this context lies in the absence of systematic guidance on how to prioritize investment criteria and strategy alternatives under high uncertainty, technological heterogeneity, and sustainability constraints. Existing studies often analyze battery technologies or recycling options in isolation, while neglecting the integrated evaluation of circular, technological, and energy-related dimensions. This fragmentation reveals a critical gap in the literature regarding the identification and prioritization of the most influential investment criteria and end-of-life strategies. Accordingly, the main objective of this study is to determine the most critical investment criteria and the most effective end-of-life strategy alternatives for first-generation mass-produced EV batteries through a novel fuzzy multi-criteria decision-making framework. To address this gap, a cipher fuzzy set (CFS)-based hybrid decision model is proposed, integrating Manhattan distance-based centrality expert selection, cognitive maps for criteria weighting, and orthogonal metric robust aggregation for strategy ranking. The proposed model offers methodological advantages by eliminating the need for defuzzification, reducing information loss, and enhancing robustness against uncertainty and expert subjectivity. Empirical results indicate that circular value add, and energy efficiency emerge as the most critical investment criteria, while component-level reuse and direct cathode-to-cathode recycling are identified as the most effective end-of-life strategies. These findings suggest that early-generation EV battery investments should prioritize value-retentive and energy-efficient pathways that support closed-loop systems and resource conservation. The study contributes to the EV battery literature by providing a comprehensive prioritization framework and introducing cipher fuzzy sets as a novel methodological advancement, offering actionable insights for policymakers, investors, and industry stakeholders.