The global drone market is expected to grow from $4.9 billion to $14.3 billion within the next decade, indicating a heavy demand for high performance electric aircraft. Modern drones are propelled with brushless DC (BLDC) motors and electronic speed controllers (ESCs). However, a current lack of information concerning the performance and efficiency of BLDC motors and ESCs prevents their use in rigorous aircraft design. Low cost hobby ESCs and BLDCs are typically used in research aircraft, but few technical details are released by their manufacturers.

To better understand these devices, a custom dynamometer was constructed to study the performance of ESCs and BLDC motors. By properly recording the DC, AC, and mechanical power, information on peak efficiency and performance for the ESCs and BLDC motors are determined experimentally. Motors between 920 KV to 2500 KV were tested with 18 A, 30 A, and 40 A ESCs. A combination of these tests were carried out at 7.2 V, 11.1 V, and 14.8 V DC to explore trade offs in the design process. While typically neglected in formal analysis, this work seeks to better understand the power loss mechanisms in ESCs, as it was found that ESCs could have efficiencies as low as 65%, reducing the overall efficiency of the system considerably. This custom dynamometer features a load varying device, power analyzers, and a unique two DAQ setup to properly capture the high frequency electrical signals of BLDC motors.

From the sets of experimentally recorded motor and ESC tests, a novel analytical model is developed to predict the performance of ESCs and BLDC motors. At the heart of this modeling effort is describing the 3 phase AC circuit as a single equivalent circuit, which encapsulating the motor’s performance. This work is critical in the design process, as properly sizing ESCs, motors, and rotors for an electric aircraft can improve aircraft endurance and range. Performance metrics are extracted from experimental results and are fit into the analytical model. Predictions for the system’s mechanical power, AC power, and DC power agree well with experimental results, demonstrating applicability of the robust model.