Integrated Taguchi method‐assisted polynomial Metamodelling & Genetic Algorithm based optimisation of a surface inset permanent synchronous motor for performance improvement. Issue 1 (8th September 2021)

• Record Type: Journal Article
• Authors: Verma, Monika; Singh, Madhusudan; Sreejeth, Mini
• Is Part Of: IET electrical systems in transportation. Volume 12:Issue 1(2022)
• Page Start: 26
• Page End: 35
• Publication Date: 2021-09-08
• Subjects: electric machines -- finite element analysis -- genetic algorithms -- optimisation -- permanent magnet motors -- torque; Electric vehicles -- Periodicals; Electricity in transportation -- Periodicals; 621.3105
• DOI: 10.1049/els2.12035
• Languages: English
• ISSNs: 2042-9738

Abstract

Abstract: In this study, an Integrated Taguchi method‐assisted polynomial Metamodelling & Genetic Algorithm (ITM&GA)‐based optimisation technique is implemented for design optimisation of a surface inset permanent magnet synchronous motor (SIPMSM). The motor geometry is analysed by implementing the finite element method for application of the motor in electric compressors of the cooling system of an electric vehicle (EV). The polynomial surrogate model is computed with the help of Taguchi experiments to eliminate the redesigning process of models to reach the optimum values of design parameters and reduce the ambiguity to select the best optimum solution in Traditional Taguchi Method. The root‐mean‐square error test is performed to validate the accuracy of metamodels. The optimum solutions are then converged using the GA technique. The optimum results are compared and presented. Using the ITM&GA technique, the reduction in unwanted ripples in torque and cogging torque along with the improved torque performance of the motor is achieved successfully. The proposed mechanism is effective in obtaining quick and accurate solutions for preliminary designs of the SIPMSM for the electric compressor application in EVs.