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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Abstract	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.
AbstractList	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.
Author	Zhang, Shaoru Zhang, Jielu Yang, Likun Yan, Hui Du, Xiuju Zhao, Hua
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SubjectTerms	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
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Title	Optimization Design of Permanent Magnet Synchronous Motor Based on Multi-Objective Artificial Hummingbird Algorithm
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