The rapid growth of the electric vehicle (EV) market necessitates advanced multi-criteria decision-making (MCDM) frameworks capable of integrating diverse quantitative and qualitative factors under uncertainty. Traditional MCDM approaches often struggle to capture the complexity and imprecision inherent in EV evaluations, particularly in dynamic contexts like India. To address this gap, this study proposes the T-Spherical Fuzzy (T-SF) MARCOS and T-SF MOORA methods, which utilize T-Spherical Fuzzy Numbers (T-SFNs) to enhance decision precision. T-SFNs extend conventional fuzzy models by independently incorporating degrees of membership, non-membership, and hesitation, enabling a more granular and realistic modeling of expert judgments. In the methodological construction, numerical criteria (e.g., battery capacity, charging time) are directly incorporated, while qualitative criteria (e.g., safety, comfort) are initially evaluated by four domain experts through linguistic assessments, subsequently transformed into T-SFNs for integrated evaluation and accurate criteria weighting. The developed models are then employed to rank ten EV alternatives across 21 comprehensive technical and consumer-centric criteria. Comparative analysis shows that T-SF MARCOS and T-SF MOORA achieve superior ranking accuracy, with a high mutual Pearson correlation of 0.71, while traditional SF methods like SF-WSM and SF-WASPAS exhibit negative correlations of −0.43 and −0.42, respectively. Sensitivity analyses—covering variations in criteria weights and additional criteria integration—confirm the robustness and stability of the frameworks, with rank reversal rates remaining below 10 % across all scenarios. This study presents a technically resilient, uncertainty-aware evaluation framework, offering strategic insights for advancing consumer-centric EV development.