TY - GEN
T1 - Research on Axial Force Balance and Loss Optimization Control of Electric Aircraft
AU - Fan, Yukun
AU - Liang, Peixin
AU - Liu, Weiguo
AU - Liang, Lihao
AU - Zhang, Xiaoke
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Aircraft electric propulsion system
KW - axial force
KW - conical motor
KW - fuzzy algorithm
KW - multi-objective optimization algorithm
UR - http://www.scopus.com/inward/record.url?scp=85182342381&partnerID=8YFLogxK
U2 - 10.1109/ICEMS59686.2023.10344655
DO - 10.1109/ICEMS59686.2023.10344655
M3 - 会议稿件
AN - SCOPUS:85182342381
T3 - 2023 26th International Conference on Electrical Machines and Systems, ICEMS 2023
SP - 3657
EP - 3661
BT - 2023 26th International Conference on Electrical Machines and Systems, ICEMS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 26th International Conference on Electrical Machines and Systems, ICEMS 2023
Y2 - 5 November 2023 through 8 November 2023
ER -