The burgeoning global electric vehicle (EV) market is increasingly complex, necessitating sophisticated analytical tools to navigate its diverse offerings and rapid technological advancements. Recent academic and industry research highlights the pivotal role of advanced multi-criteria decision-making (MCDM) frameworks, particularly those integrating data-driven T-spherical fuzzy mathematical models, in optimizing EV evaluation. This methodological evolution is critical for stakeholders ranging from individual consumers to large fleet operators and policymakers seeking to make informed decisions amidst a proliferation of models and specifications.Traditional evaluation methods often struggle with the inherent vagueness and uncertainty associated with real-world EV data, such as subjective performance metrics, fluctuating energy costs, and evolving charging infrastructure. T-spherical fuzzy sets, an extension of intuitionistic and Pythagorean fuzzy sets, offer a robust mathematical apparatus to model these imprecise parameters. By allowing for degrees of membership, non-membership, and hesitancy, these models can more accurately capture the nuanced preferences and uncertain data points crucial for comprehensive EV assessment. For instance, evaluating an EV's "long-range capability" or "fast-charging efficiency" involves qualitative judgments and variable real-world conditions that fuzzy logic can effectively quantify.Leading research institutions have begun applying these models to evaluate EVs across a spectrum of criteria, including purchase cost, operational expenses, battery range, charging time, performance metrics (acceleration, top speed), safety features, environmental impact (lifecycle emissions), and technological sophistication. The data-driven aspect ensures that the models are continuously refined by empirical evidence, leading to more objective and reliable evaluations. This approach moves beyond simple weighted averages, providing a more holistic and adaptable framework that can prioritize criteria based on specific user needs or strategic objectives, such as maximizing environmental benefits or minimizing total cost of ownership.The market significance of these advanced evaluation tools is substantial. For consumers, they offer a systematic way to compare seemingly disparate EV models, aligning choices with personal priorities and reducing decision-making paralysis. For manufacturers, these models can identify market gaps, highlight competitive advantages, and inform R&D priorities by revealing which attributes are most valued by different segments. Policymakers can leverage these insights to design more effective incentives, infrastructure development plans, and regulatory standards that genuinely accelerate EV adoption and ensure sustainable growth. As the industry matures, the ability to precisely quantify and compare the multifaceted aspects of EVs will be paramount for sustained innovation and market penetration. This analytical rigor is poised to become a standard in the industry, driving more efficient resource allocation and fostering greater confidence in the EV transition.