Research on multi-objective optimization of switched flux motor based on improved NSGA-II algorithm

Abstract

In order to optimize the local search efficiency of multi-objective parameters of flux switching permanent motor based on traditional NSGA-II algorithm, an improved NSGA-II (iNSGA-II) algorithm is proposed, with an anti-redundant mutation operator and forward comparison operation designed for quick identification of non-dominated individuals. In the initial stage of the iNSGA-II algorithm, half of the individual populations were randomly generated, while the other half was generated according to feature distribution information. Taking the flux switching permanent motor stator/rotor gap, permanent magnets width, stator tooth width, rotor tooth width and other parameters as optimization variables, the flux switching permanent motor maximum output shaft torque and minimum torque ripple are taken as optimization objectives, thus a multi-objective optimization model is established. Real number coding was adopted for obtaining the Pareto optimal solution of flux switching permanent motor structure parameters. The results showed that the iNSGA-II algorithm is better than the traditional NSGA-II on convergence. A 1.8L TOYOTA PRIUS model was selected as the prototype vehicle. By using the optimized parameters, a joint optimization simulation model was established by calling ADVISOR’s back-office function. The simulation results showed that the entire vehicle’s 100-km acceleration time is under 8 s and the battery’s SOC value maintains at 0.5–0.7 in the entire cycle, implying that the iNSGA-II algorithm optimizes the flux switching permanent motor design and is suitable for the initial design and optimizing calculation of the flux switching permanent motor.

Parameter optimization of rule-based control strategy for multi-mode hybrid electric vehicle

“…So we proposed an improved NSGA_II algorithm, where an anti-redundant mutation operator and forward comparison operation are designed in the improved NSGA_II. The effectiveness of this algorithm has been proved in Jin et al 37…”

“…So we proposed an improved NSGA_II algorithm, where an anti-redundant mutation operator and forward comparison operation are designed in the improved NSGA_II. The effectiveness of this algorithm has been proved in Jin et al 37 We use the improved NSGA_II algorithm to optimize the logic threshold by taking the total fuel consumption as a measure of fuel economy in a driving cycle. Simply put, the objective of optimization is to find a set of optimal logical threshold values…”

FEM-Based Benchmark Problem for Cogging Torque Minimization of Axial Flux Permanent-Magnet Motors in Artap Framework

“…The non-dominated sorting genetic algorithm II (NSGAII) has advantages compared to classical non-dominated sorting EAs with O(MN 3 ). NSGAII has a faster sorting approach with a computational complexity of O(MN 2 ) [32,33].…”

Application Research on the Maintainability Allocation Method of a Certain Shooter Seat

“…In reference [18], the superiority of the iNSGA-II algorithm has been verified, and the parameters of the switchedflux motors are optimized using this algorithm. In this paper, the iNSGA-II algorithm is used to solve the maintainability allocation problem of a certain shooter seat and obtain the optimal Pareto solution, which effectively solves the limitations of the traditional maintainability allocation method and then provides a basis for a certain shooter seat to obtain a reasonable maintainability allocation scheme.…”