Integrated Performance Prediction Method for Drone Propulsion BLDC Motor Considering Square Wave Current and Mechanical Losses

This paper proposes a performance prediction process for a Brushless Direct Current (BLDC) motor used in drone propulsion by considering actual drive current characteristics and mechanical losses. Drone propulsion BLDC motors are required to achieve high power density and high efficiency under limited power supply conditions, and therefore their performance should be predicted under practical operating conditions. However, in an Electronic Speed Controller (ESC)-based drive environment, the current waveform varies depending on the operating condition, and mechanical losses such as windage and bearing losses can affect the estimation of input power and efficiency, particularly in high-speed operation. The proposed process integrates inverter simulation, finite element analysis, and a test-based mechanical loss model. In the proposed process, the current response under BLDC drive conditions is reflected in the electromagnetic analysis, and the input power, output power, and efficiency of the motor are estimated by considering both electromagnetic and mechanical losses. In addition, mechanical losses are separated from load test results and formulated as a speed-dependent loss model for use in the performance prediction process. The validity of the proposed method is examined by comparison with test results obtained under different throttle conditions. The proposed method reduced the input power prediction error from 26.61% to 2.62% and the efficiency prediction error from 36.27%p to 2.55%p, demonstrating its effectiveness for predicting the performance of drone propulsion BLDC motors under practical operating conditions.