Multi-Objective Optimization Framework of a Radial-Axial Hybrid Excitation Machine for Electric Vehicles

This paper proposes a multi-objective optimization framework for a radial-axial hybrid excitation machine (RAHEM) to provide higher average torque, better flux regulation ability and smaller torque ripple, which are applied to electric vehicles (EVs). The design variables related to multiple-objective are analyzed by sensitivity stratification. Non-dominated sorting genetic algorithm II (NSGA-II) based on response surface model (RSM) is adopted for the high sensitivity layer variable. The advantages are selected with the pareto optimal solutions (POS), while the low sensitivity layer variables are optimized by sensitivity ranking for single parameter scanning. The optimization function compares the two sensitive layers results to obtain the optimal design. Three-dimensional (3-D) finite element analysis (FEA) is used to compare the electromagnetic performance of initial and optimal designs. Finally, a prototype is manufactured to verify the effectiveness of the proposed framework.

Automated summary

This paper proposes a multi-objective optimization framework for a radial-axial hybrid excitation machine (RAHEM) in electric vehicles (EVs), aiming to improve average torque, flux regulation ability, and torque ripple. The design variables related to multiple objectives are analyzed using sensitivity stratification. Non-dominated sorting genetic algorithm II (NSGA-II) based on response surface model (RSM) is adopted for optimizing the high sensitivity layer variables, while the low sensitivity layer variables are optimized through sensitivity ranking. The proposed framework is verified through three-dimensional finite element analysis (3-D FEA) and prototype manufacturing.