Simultaneous Optimization of Geometry and Firing Angles for In-Wheel Switched Reluctance Motor Drive

Anvari, Bahareh; Toliyat, H.A.; Fahimi, Babak

doi:10.1109/tte.2017.2766452

Cited by 74 publications

(27 citation statements)

References 21 publications

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“…m x (S i ) is the average of the performance index S under the ith level of parameter x. m(S) is the average of the performance index S. The performance of the SRM is closely related to the turn-on angle on and the turn-off angle off . The output power of the SRM can have various combinations of on and off [23], and the size of the constant power region depends on the range of the value of on and off . The arc of the rotor pole and stator pole has great influence on the dynamic performance of the machine.…”

Section: Global Sensitivity Analysismentioning

confidence: 99%

Design and multi‐objective optimisation of switched reluctance machine with iron loss

Yan

Chen

Liu

et al. 2019

IET Electric Power Applications

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In this study, the design optimisation of a switched reluctance machine (SRM) with the layered method has been studied. Firstly, a multi-physical analytical model for the SRM is established. The proposed model consists of an electromagnetic model, an electrical model, a loss model, and a thermal model. Then a layered optimisation design method suitable for the SRM is proposed in combination with the multi-physical field simulation model. Taguchi method is used to analyse the influence degree of the main geometric dimensions on the dynamic performance, and the parameters are divided into high-sensitivity parameters (HSPs) and low-sensitivity parameters. The non-dominated sorting genetic algorithm-II algorithm is used to optimise the HSP by the proposed model. The iron loss, average torque, average torque per unit mass, torque ripple and efficiency are taken as the objective functions. Finally, the prototype is manufactured and a testing platform is built. The flux linkage of several special positions of the machine is verified experimentally, and the accuracy of the electromagnetic model in the multi-physical field model is verified. The temperature experiments are carried out on the machine, and the accuracy of the equivalent heat circuit model in the multi-physical field model is verified.

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Section: Global Sensitivity Analysismentioning

confidence: 99%

Design and multi‐objective optimisation of switched reluctance machine with iron loss

Yan

Chen

Liu

et al. 2019

IET Electric Power Applications

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“…For EVs, obtaining the maximum torque per ampere (MTPA) over the entire range of speed control is desirable in order to increase the driving mileage per charge [19,26]. The main idea for the MTPA is to estimate the proper switching angles (θ on and θ off ) that extract the highest value of torque according to the motor speed and current levels [19,27]. The θ on angle is the dominant parameter for performance improvement in SRM drives [19,28].…”

Section: Introductionmentioning

confidence: 99%

Numerical Estimation of Switched Reluctance Motor Excitation Parameters Based on a Simplified Structure Average Torque Control Strategy for Electric Vehicles

et al. 2020

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Switched reluctance motors (SRMs) have been receiving great attention in electric vehicle (EV) applications. However, the complicated control and inherent torque ripples are the major drawbacks of SRMs. This paper introduces a numerical estimation method for the optimum control parameters of SRM based on a simplified average torque control (ATC) strategy for EVs. The proposed control aims to simplify the control algorithm to cut down complexity and cost. Besides, it aims to achieve all the vehicle requirements. A multi-objective optimization problem is set to determine the most efficient excitation parameters that can fulfill the vehicle requirements. The objective function has two terms: torque ripple and efficiency. Proper constraints for both turn-on and turn-off angles are included in order to achieve high-performance control, maximum torque per Ampere (MTPA) production, and reliable operation. Besides, additional toque constraints are involved to ensure fast dynamics, high-performance torque tracking capability, and parameter insensitivity. The motor model is accurately achieved based on the experimentally measured torque and flux characteristics. Several simulations are executed to prove the feasibility and effectiveness of the proposed control. Moreover, experimental results are obtained to validate the theoretical findings. It is observed that the proposed control has a significant reduction of torque ripples compared to the conventional control methods. The average reduction ratio of torque ripple over the speed range is about 72.43%. Besides, the proposed control succeeds in maintaining a very good efficiency and high torque/current ratio. It also has a fast-dynamic performance.

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“…This algorithm and its unique method of dealing with MO optimization problems have been used repeatedly by different people to create newer MO optimization algorithms. The NSGA‐II algorithm is one of the most widely used and robust algorithms for solving MO optimization problems and has been proven to solve various issues especially in the field of electrical machine optimization 20,21 . The electromagnetic optimization procedure of electrical machines is shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis and multiobjective design optimization of flux switching permanent magnet motor using MLP‐ANN modeling and NSGA‐II algorithm

Mahmouditabar

Vahedi

Mosavi

et al. 2020

Int Trans Electr Energ Syst

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Summary The flux switching permanent magnet (FSPM) motor is relatively a new topology of the permanent magnet (PM) motors, which both PM and armature winding are placed at the stator. This feature leads to more robust design, better heat dissipation, and a proper option for a wide range of industrial applications. The critical part of the development of FSPM motor is the design optimization of the structure to improve the electromagnetic performance of the motor. In this article, first to reduce the computation time and required memory of the optimization procedure, the multiobjective sensitivity analysis based on design of experiment is performed to specify the most effective parameters on the objectives. Then, the initial samples data of the optimization procedure is obtained by 2D finite element method (FEM) model of the FSPM motor, which is validated by the prototype of the motor. Furthermore, based on FEM results the multilayers perceptron artificial neural network for the approximation of relation between design variables and objectives is implemented. Finally, using the nondominated sorting genetic algorithm‐II, the optimization procedure of the FSPM motor is done. The accuracy of the presented optimization procedure is validated by a comparison of the initial prototype and final design of the motor.

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Simultaneous Optimization of Geometry and Firing Angles for In-Wheel Switched Reluctance Motor Drive

Cited by 74 publications

References 21 publications

Design and multi‐objective optimisation of switched reluctance machine with iron loss

Design and multi‐objective optimisation of switched reluctance machine with iron loss

Numerical Estimation of Switched Reluctance Motor Excitation Parameters Based on a Simplified Structure Average Torque Control Strategy for Electric Vehicles

Sensitivity analysis and multiobjective design optimization of flux switching permanent magnet motor using MLP‐ANN modeling and NSGA‐II algorithm

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