This paper introduces a novel mathematical framework that combines Neutrosophic Finsler Geometry with Neutrosophic Cohomology for evaluating the performance of Brushless Direct Current (BLDC) motors under uncertain and indeterminate operating conditions. Classical motor performance models typically assume precise measurements of torque, current, and efficiency; however, in real-world settings, these parameters are often affected by noise, incomplete information, and conflicting observations. By embedding motor operating states into a neutrosophic Finsler space, the proposed approach captures variations not only in magnitude but also in direction, uncertainty, and conflict of performance metrics. In addition, neutrosophic Cohomology is employed to characterize global invariants of the system, enabling the detection of latent structural patterns in efficiency and stability under indeterminacy. Numerical examples and illustrative case studies demonstrates that the proposed framework yields richer and more robust insights than classical and fuzzy-based models. These results highlight the potential of neutrosophic mathematics as a powerful tool for modeling complex engineering systems where truth, falsity, and indeterminacy coexist.
Gharib et al. (Sat,) studied this question.