Multi-objective optimization design of long stator linear synchronous motor for maglev train based on surrogate model

Abstract

In order to improve the multi-objective optimization (MOO) efficiency of Long-Stator Linear Synchronous Motor (LSLSM), an efficient optimization design method based on the adaptive Kriging (AKriging) surrogate model and the improved Non-dominated Sorting Genetic Algorithm II (INSGA-II) is put forward. The optimization objectives are average thrust, thrust ripple, and suspension force ripple, with key electromagnetic structural parameters selected as design variables. Optimal Latin hypercube design and Spearman correlation analysis are used to identify the main design variables affecting performance. Then, a high-accuracy AKriging surrogate model for thrust and suspension performance is built, and optimization is carried out using the INSGA-II algorithm. The optimal solution is selected from the generated three-dimensional Pareto front and re-evaluated through finite element analysis (FEA). The simulation results show that the optimized motor's average thrust increases by 13.44%, and the thrust ripple and suspension force ripple decrease by 26.62% and 16.32%, respectively. While maintaining suspension stability, the average thrust is significantly increased, and thrust ripple is effectively suppressed, validating the effectiveness of the optimization method.