A novel sequential-stage optimization strategy for an interior permanent magnet synchronous generator design

For an optimal design of an interior permanent magnet synchronous generator (IPMSG) for range the extended electric vehicle (REEV), many design variables and objective functions should be considered. Conventional optimization methods like the Taguchi method andmultiobjective optimization algorithm have completeness or calculation time problems when solving the many design variables and objectives problems. To address these problems, a sequential-stage optimization strategy (SSOS) is proposed. In the first stage of the SSOS, the initial design result considering the various objective functions is derived by using the Taguchi method. In addition, the sensitive design variables are sorted through the calculation of synthetic signal to noise. In the second stage, the optimal solution for the sensitive design variables is derived using the surrogate assisted genetic algorithm (SAGA). The SAGA not only obtains an accurate and well-distributed Pareto front set but considerably reduces the number of function calls as well. In the last stage, the uncertainty consideration based on the worst-case scenario is applied to derive the robust optimal solution. By applying the proposed optimization strategy to the optimal design of IPMSG for REEV, an optimal solution is derived with fewer function calls, and the feasibility of the proposed optimization is verified by experimental results of manufactured generator.