Optimisation design of a flux memory motor based on a new non‐linear MC‐DRN model

Abstract

In this study, based on a magnetisation-controllable dynamic reluctance network (MC-DRN) model, an optimisation design method with an efficient modern evolution algorithm for the flux memory motor is proposed. To clarify the method conveniently, a doubly salient flux-control memory (DS-FM) motor is involved and taken as a design example. First, according to the motor topology, the detailed MC-DRN model of the DS-FM motor is built, in which the magnetisation state adjustment of the low coercivity force material is considered. During the optimisation design process, to meet the diverse design requirements of multiple operation modes, non-dominated sorting genetic II is employed to obtain the trade-off optimal design of the DS-FM motor under different magnetisation states. Moreover, to verify the feasibility of the optimisation method, the electromagnetic performances of the motor in different operation modes are investigated in detail, and the results are compared with that obtained by the finite-element method. Finally, a prototype is manufactured, and the experiments are carried out. The corresponding results not only validate the reasonability of the investigated DS-FM motor but also prove the accuracy and the effectiveness of the MC-DRN model and optimisation design method.

Electric Field Evaluation Using the Finite Element Method and Proxy Models for the Design of Stator Slots in a Permanent Magnet Synchronous Motor

“…After defining and calculating each element, the algorithm performs a refinement process from an initial mesh, evaluating finer meshes [23]. After evaluating the meshes, an asymptotic behavior is found, and in this way, the variations become smaller until the algorithm converges [37]. The elements are connected through nodal points, and these form a mesh [38], as shown in Figure 3 for analysis with triangular elements.…”

“…Figure 10. Proxy model with logarithmic transformation (blue line) applied to the data presented in Table 2 (red dots).The derived regression with the logarithmic transformation was given by V(r) = 249.58 + 20.25r − 1.97r 2 + 0.07r 3 − 15.26 ln(r),(37) …”

Optimal design of the drive motor based on the road condition of the electric vehicle

“…Wu et al proposed a new method for parameter modelling and optimisation of brushless DC motors. Based on the neural network, the geometrical feasibility was optimised to achieve the purposes of efficiency maximising, material cost minimising, and higher power density [4]. Some scholars also evaluated the performance of motors in combination with cycle test conditions of EV.…”