Research on Axial Force Balance and Loss Optimization Control of Electric Aircraft

This paper proposes an optimal matching strategy for aircraft electric propulsion conical motors to balance the bearing wear, loss and working condition requirements of the electric propulsion system. This paper first analyzes the simulation data of the propeller and the motor, and establishes the propeller output model and the motor axial magnetic pull calculation model by means of fitting and BP neural network respectively. Aiming at the characteristics that the axial force balance and copper loss reduction of the electric propulsion system cannot be satisfied at the same time, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) multi-objective optimization algorithm is used to obtain the Pareto optimal solution. Combine with the fuzzy algorithm, the solution set that has the same trend as Pareto and can change the ratio of axial magnetic pull and copper loss by adjusting the input coefficients is used to meet the needs of changing working conditions. Finally, the effectiveness of the method is verified by simulation.