<p>This study examines the implementation of advanced multi-criteria group decision-making techniques on real-world applications of battery efficiency optimization in electric vehicles (EVs). It focuses on battery efficiency being a major aspect of the performance and sustaining EVs, which requires optimization strategies to be evaluated for and amongst various options. As a solution, this paper introduces a spherical fuzzy Z-number-based decision framework as a robust approach under uncertainty. Hence, by the introduction of Yager norm-based aggregation operators, a more flexible and adaptable quality evaluation approach will provide more efficiency in the assessment of battery management strategies for sustainability manufacturing in EVs. Z-numbers, which integrate reliability and constraint factors, present a much more credible characterization of uncertain information. Yet, for all their advantages, Z-numbers are still far from fully utilized, particularly when combined with spherical fuzzy sets. This research combines these two under-explored fields and establishes a new kind of spherical fuzzy Z-number aggregation operators under the framework of Yager norms, adding flexibility and adaptability in decision-making. In formalizing this approach, an extensive set of operational rules is laid down, proposing averaging and geometric aggregation operators, based on Yager norms. Unlike Sugeno-Weber operators, Yager-norms tend to relax the prerequisite of symmetry existing in Sugeno-Weber operators, thus constituting more effective compromise aggregation mechanisms. The comparative analysis with Sugeno-Weber-norm-based operators and grey relational analysis proves the consistency of ranking and flexibility in decision-making.</p>