Optimization Design Of Switched Reluctance Motor Based On Response Surface Method And Genetic Algorithm

Switched Reluctance Motor(SRM)has the advantages of simple and robust structure,wide speed range and high system reliability.It has been widely used in electric vehicle drive,general industry,household appliances,wind power generation and other fields.However,the inherent torque ripple of SRM has great limitations on the application of the motor.Therefore,reducing the torque ripple of the SRM and improving the efficiency of the motor are the current research hotspots.Based on the initial electromagnetic design of SRM,this paper analyzes the influence of important parameters of motor on output performance,constructs sample points through central composite design(CCD),establishes response surface model by finite element analysis,and finally obtains optimal solution by multi-objective genetic algorithm.Simulation experiments show that the optimized structural parameters given by this method can reduce torque ripple and improve motor efficiency.Firstly,the research status of SRM is introduced,including the advantages and disadvantages of the motor and the method of reducing the torque ripple.The design,simulation and parameter design of the four-phase 8/6 pole SRM are studied.The Ansoft software transient field is used to analyze the main design parameters of the motor: stator and rotor pole arc and cut-off angle,and calculate the motor efficiency and torque ripple at rated speed.Secondly,the efficiency and torque ripple of the motor are the optimization targets,and the stator,rotor pole arc and turn-off angle are used as optimization variables.The experimental sample points were constructed by the central composite experimental design(CCD)method.The sample points were calculated by finite element analysis,the sample data was obtained and the response surface model was established.The model equations with motor torque ripple and efficiency were obtained.The variance analysis is used to analyze the relationship between each factor and the response surface value in the response surface model,and the significant relationship between the response surface model and the independent variable is determined.Finally,the genetic algorithm is used to solve the multi-objective value of the response surface model,and the optimal solution of the design parameters of the motor is obtained.The Ansoft software was used to analyze and compare the initial motor and the optimized motor,and the correctness of the method for motor optimization design was verified.The research in this paper has certain guiding significance for the optimization design of SRM.