Optimization design of slotless permanent magnet direct-current motor based on adaptive improved particle swarm algorithm
FAN Jing
XU Shu
Abstract：[Objective]Traditional motor optimization design methods involve establishing analytical models for motor volume,loss,and cost,selecting optimization algorithms to refine them,and deriving optimal design variables.However,since motor models are complex,analytical models fail to precisely describe partial variables.Stator magnetic density is an important variable of slotless permanent magnet direct-current motors,whereas the accuracy of its analytical formula is low.The particle swarm algorithm is widely used in optimization design,but its optimization ability is poor.[Methods]To solve the above problems,an optimization design method of the slotless permanent magnet direct-current motors based on adaptive improved particle swarm algorithm was proposed.An analytical model of the slotless permanent magnet direct-current motors was established,and an objective function was constructed with motor volume,loss,and cost as optimization goals.The Sobol method was employed to identify high-sensitivity variables of the motors,thereby reducing the number of design variables.Subsequently,a magnetic circuit model was developed using finite element simulations,and magnetic density data were extracted after design variable parameters were adjusted.The response surface method was then applied to re-fit the magnetic density data,and a stator magnetic density response surface model was established to replace the original analytical formula.The particle swarm algorithm was improved.The updating modes of inertial weight and learning factor were selected through comparisons between fitness values of individual particles and the average fitness value of global particles during iteration,which enhanced algorithmic precision.Finally,both the original and improved algorithms were utilized to optimize the objective function.The optimal motor design parameters were achieved by comparison.[Results]Comparative analysis of stator magnetic density calculations between the analytical formula and the response surface model reveals that the latter exhibits significantly reduced computational errors.When the adaptive particle swarm algorithm,original particle swarm algorithm,and other classical algorithms were applied to optimize the objective function,the improved particle swarm algorithm achieves the most optimal results.[Conclusion]The experimental results demonstrate that replacing the analytical formula for stator magnetic density with a response surface model effectively mitigates the significant calculation errors associated with the analytical approach.Meanwhile,the particle swarm algorithm incorporating adaptive updates of inertia weight and learning factor exhibits an enhanced optimization capability.Comparative analysis with classical algorithms confirms its superior optimization capability.
Keywords：slotless permanent magnet direct-current motorstator magnetic densitySobol methodresponse surface methodadaptive particle swarminertia weightlearning factor
Publication Date：2025-07-25
Online Publishing Date：2025-09-18(First online date of this platform, not the publication date of the document)
Pages：8( 455-462 )