Multi-Objective Optimization Design for a New Consequent-Pole Hybrid Excited Machine with Segmented Stator

This paper proposes a hybrid design methodology combining sensitivity analysis and multi-objective optimization. The method solves multi-parameter optimization challenges in a new consequent-pole hybrid excited machine with segmented stator (CHE-SS) machine for electric vehicles (EVs). The initial parameters are determined through electromagnetic field analysis and engineering experience, then the parameter response surface models are constructed via finite element (FE) method. Taguchi method, leave-one-out cross-validation (LOOCV) method and analysis of variance (ANOVA) method are adopted to quantify the sensitivity coefficients of the design parameters. Therefore, the critical influencing factors in the sensitivity analysis can be determined. A multi-objective optimization model including output torque, torque ripple, cogging torque, and torque regulating factor is subsequently developed. The optimization employs genetic algorithms (GA) for global exploration. The sequential quadratic programming (SQP) is adopted for the local refinement. Then, Pareto-optimal solutions can be obtained. The FE results validate the effectiveness of the proposed optimization method in this paper, which can offer a new method for the design of the multi-phase machine.