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Accurately sizing vehicle components is an impactful step in the aircraft design process. However, existing methods of sizing brushless DC (BLDC) motors for small unmanned aerial systems (SUAS) ignore how cooling affects motor size. Moreover, the literature methods do not predict a notional motor's electrical constants, namely winding resistance, torque constant, and figure of merit. We developed a sizing algorithm that predicts the optimal mass and electrical constants using a combination of sizing, efficiency, and thermal models. The algorithm works for radial-flux BLDC motors with masses up to 800 g. An experimental teardown of seven motors informed the algorithm's sizing models. The teardown motors varied in mass (24–600 g) and geometry (stator aspect ratio of 1.4–9.0). Validated against an independent catalog of 30 motors, the sizing models predicted mass and resistance within 10% and 20% of catalog specifications, respectively. Validated against experimental data, the full algorithm predicted mass, efficiency, and temperature within 20%, 5%, and 10% accuracy, respectively. The algorithm also captured how lowering mass would increase losses and temperature, which the literature models ignore. The algorithm can help users develop more viable concepts that save costs in the long run. [ABSTRACT FROM AUTHOR]

Accurately sizing vehicle components is an impactful step in the aircraft design process. However, existing methods of sizing brushless DC (BLDC) motors for small unmanned aerial systems (SUAS) ignore how cooling affects motor size. Moreover, the literature methods do not predict a notional motor’s electrical constants, namely winding resistance, torque constant, and figure of merit. We developed a sizing algorithm that predicts the optimal mass and electrical constants using a combination of sizing, efficiency, and thermal models. The algorithm works for radial-flux BLDC motors with masses up to 800 g. An experimental teardown of seven motors informed the algorithm’s sizing models. The teardown motors varied in mass (24–600 g) and geometry (stator aspect ratio of 1.4–9.0). Validated against an independent catalog of 30 motors, the sizing models predicted mass and resistance within 10% and 20% of catalog specifications, respectively. Validated against experimental data, the full algorithm predicted mass, efficiency, and temperature within 20%, 5%, and 10% accuracy, respectively. The algorithm also captured how lowering mass would increase losses and temperature, which the literature models ignore. The algorithm can help users develop more viable concepts that save costs in the long run.