Multi-objective Optimization Method for Propeller and Motor Size Selection in Electric Propulsion Systems for Unmanned Aerial Vehicles

Abstract

This article presents a multi-objective optimization method for selecting propeller and motor sizes in electric propulsion systems for unmanned aerial vehicles (UAVs). The study aims to achieve optimal performance in terms of efficiency, weight, and dynamic response time. Design variables such as motor armature diameter, stator lamination stack length, propeller size, and rated revolutions are considered. The motor efficiency map is simulated for calculating the system efficiency, and the electromagnetic parameters of the motor is needed by the control system simulation. The aerodynamic performance of the propeller is evaluated using blade element momentum theory. The control system is a double closed-loop DC speed regulation system with PI controller coefficients. The proposed method provides the most efficient motor and propeller combination for each input motor-propeller size parameters. Based on this, genetic algorithm is applied to find the optimal motor-propeller size set, which can be selected by UAV designers based on requirements beyond optimization objectives. Verification shows accurate results for optimal targets, with errors less than 5% respectively. The method efficiently identifies size parameters and helps designers achieve optimal electric propulsion system performance.