Optimization Design of Permanent Magnet Synchronous Motor Based on Multi-Objective Artificial Hummingbird Algorithm

The interior permanent magnet synchronous motor (IPMSM) is known for its high output torque, strong overload capacity, and high power density, making it a popular choice in the electric vehicle industry. This paper proposes an improved multi-objective artificial hummingbird algorithm that combines c...

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Vydáno v:Actuators Ročník 13; číslo 7; s. 243
Hlavní autoři: Zhang, Shaoru, Yan, Hui, Yang, Likun, Zhao, Hua, Du, Xiuju, Zhang, Jielu
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.07.2024
Témata:
Adaptive algorithms
Back electromotive force
Deep learning
Design optimization
Design techniques
Efficiency
Electric vehicles
Electromotive forces
Finite element analysis
Finite element method
Foraging behavior
Genetic algorithms
IPMSM
MOAHA
multi-objective optimization
Multiple objective analysis
Neural networks
Objectives
Optimization algorithms
Parameter identification
Permanent magnets
Response surface methodology
Ripples
Sensitivity analysis
Synchronous motors
Taguchi methods
Torque
ISSN:2076-0825, 2076-0825
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Shrnutí:The interior permanent magnet synchronous motor (IPMSM) is known for its high output torque, strong overload capacity, and high power density, making it a popular choice in the electric vehicle industry. This paper proposes an improved multi-objective artificial hummingbird algorithm that combines chaotic mapping, adaptive weights, and dynamic crowding entropy. An optimization strategy that combines the Taguchi method with the Improved Multi-Objective Artificial Hummingbird Algorithm (IMOAHA), is proposed to minimize torque ripple and back electromotive force in the interior permanent magnet synchronous motor while simultaneously increasing the average torque of the motor. Taking the 8-pole 48-slot interior permanent magnet synchronous motor as an example, the optimization objectives include back electromotive force, average torque, and torque ripple. The rotor-related structural parameters are used as optimization variables. First, the Taguchi method is employed to identify parameters that significantly influence the optimization objectives. Subsequently, response surface fitting is used to establish the relationship between the optimization objectives and parameters. Finally, the multi-objective artificial hummingbird algorithm is utilized for optimization. By comparing the finite element analysis of the motor models before and after optimization, it is evident that the improved multi-objective artificial hummingbird algorithm can effectively enhance the performance of the interior permanent magnet synchronous motor.
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ISSN:2076-0825
2076-0825
DOI:10.3390/act13070243