This paper proposes a comprehensive framework for multiobjective design optimization of switched reluctance motors (SRMs) based on a combination of the design of experiments and particle swarm optimization (PSO) approaches. First, the definitive screening design was employed to perform sensitivity analyses to identify significant design variables without bias of interaction effects between design variables. Next, optimal third-order response surface (RS) models were constructed based on the Audze-Eglais Latin hypercube design using the selected significant design variables. The constructed optimal RS models consist of only significant regression terms, which were selected by using PSO. Then, a PSO-based multiobjective optimization coupled with the constructed RS models, instead of the finite-element analysis, was performed to generate the Pareto front with a significantly reduced computational cost. A sample SRM design with multiple optimization objectives, i.e., maximizing torque per active mass, maximizing efficiency, and minimizing torque ripple, was conducted to verify the effectiveness of the proposed optimal design framework.

/pdf/multiobjective-optimization-of-switched-reluctancemotors-5b1vnj7w3a.pdf

Multiobjective Optimization of Switched ReluctanceMotors Based on Design of Experiments and ParticleSwarm Optimization

Switched reluctance machines (SRMs) have recently attracted more interest in many applications due to the volatile prices of rare-earth permanent magnets (PMs) used in permanent magnet synchronous machines (PMSMs). They also have rugged construction and can operate at high speeds and high temperatures. However, acoustic noise and high torque ripples, in addition to the relatively low torque density, present significant challenges. Geometry and topology optimization are applied to overcome these challenges and enable SRMs to compete with PMSMs. Key geometric design parameters are optimized to minimize various objective functions within geometry optimization. On the other hand, the material distribution in a particular design space within the machine domain may be optimized using topology optimization. We discuss how these techniques are applied to optimize the geometries and topologies of SRMs to enhance machine performance. As optimizing the machine geometry and material distribution at the design phase is of substantial significance, this work offers a comprehensive literature review on the current state of the art and the possible trends in the optimization techniques of SRMs. The paper also reviews different configurations of SRMs and stochastic and deterministic optimization techniques utilized in optimizing different configurations of the machine. 

https://ieeexplore.ieee.org/ielx7/6287639/9668973/09669949.pdf

Geometry and Topology Optimization of Switched Reluctance Machines: A Review

In this article, a novel composite predictive control using PWM control signals is proposed for the torque ripple suppression of the switched reluctance machine (SRM). A PWM modulation method for the asymmetric half-bridge (AHB) converter is introduced to achieve the conversion of the duty cycle to control signals. In the single-phase region, the deadbeat predictive control (DPC) is employed to predict the optimal duty cycle for the conduction phase. The model predictive control (MPC) using PWM control signals predicts the optimal duty cycles for the incoming phase and the outgoing phase in the commutation region. Latin-hypercube sampling (LHS) design is applied to obtain the appropriate duty cycle prediction scheme according to the machine operation state. The response surface model (RSM) is proposed to replace the cost function for achieving online optimization. The performance of the proposed composite predictive control method is evaluated by detailed simulations and experiments under different working conditions. The results show that the proposed control method exhibits better performance in the torque ripple suppression of SRM compared to the conventional MPC.

A Composite Model Predictive Control Method of SRMs with PWM-based Signal for Torque Ripple Suppression

Abstract To reduce the length of the superconducting wire of the superconducting motor, the no-load back EMF E and the magnetic field component perpendicular to the wire B⊥ were selected as the optimization goals. Therefore, a multi-objective optimization method based on the Response Surface Method (RSM) and Genetic Algorithm (GA) was proposed by taking a high-temperature superconducting motor (HTSM) with a rated power of 40 MW as the research object. Firstly, the overall structure, mathematical model, and output characteristics of HTSM were introduced. Secondly, the sample points required for constructing the RSM equation were obtained by Finite Element Analysis (FEA), and the multi-objective optimization of the RSM equations was performed by applying GA to obtain the Pareto optimal frontier. Finally, the calculated optimal design was simulated and verified. Compared with the initial design, the E of the optimized HTSM was increased by 4.47%, and the B⊥ was reduced by 4.83%. Based on the motor parameters obtained by multi-objective optimization, the final optimized design of HTSM was obtained. The length of the superconducting wire used in the motor has been reduced by 10.2%, which greatly reduces the cost. 

Multi-Objective Optimization Design of 40 MW High-Temperature Superconducting Motor

A Flux-switching Linear Motor (FSLM) is suitable for urban rail transit due to its high efficiency and low cost. This paper studies a new FSLM with segmented secondary and the key of this structure is that the secondary stator is composed of separate iron blocks. The topology is introduced, and the operating principle is explained. To ensure that the new FSLM is suitable for the linear traction system, the LIM used in Guangzhou Metro Line 4 is taken as the reference. The electromagnetic characteristics of the new FSLM are studied and compared by means of finite element analysis (FEA). The results reveal that the new FSLM can meet requirements of the linear traction system after multi-objective optimization.

Optimization of Secondary Segmented Flux-Switching Linear Motor Designed for Urban Rail Transit

Switched reluctance motor (SRM) has been applied in more and more fields. However, the larger torque ripple and lower power density limit its filed. The response surface model (RSM) of optimizing parameters of motor structure is built based on the analysis of the causes of motor torque ripple in order to reduce the vibration and noise of the motor. The multi-group sample points are constructed by using the central composite design (CCD) method, where the torque ripple and efficiency are taken as the optimization objective, and the stator pole, rotor pole arc and turn-off angle are the optimization variables. The sample data are obtained by finite element analysis (FEA), and the RSM equation is built. The effect of all factors on the response face value is analyzed. The relationship between the variables and the target is determined by studying the interaction between the stator/rotor pole arcs and the outer diameter of the rotor. According to the verification formula of response surface, the complex correlation coefficient of torque ripple and efficiency are 0.929 and 0.962; and the lack of fit are 0.059 and 0.056 respectively, which show that the linear relationship is significant between response surface and independent variables. The simulation results show that the torque ripple, average torque and efficiency are 0.608，4.558 and 0.827 when the stator pole arc, rotor pole arc and turn-off angle is 19 deg., 24.4 deg., 22 deg., respectively.

Optimization Design of Switched Reluctance Motor Based on Response Surface Method

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Optimization Design of Switched Reluctance Motor Based on Response Surface Method