High-Fidelity Magnetic Characterization and Analytical Model Development for Switched Reluctance Machines

Nasirian, Vahidreza; Kaboli, Shahriyar; Davoudi, Ali; Moayedi, Seyedali

doi:10.1109/tmag.2012.2222427

Cited by 28 publications

(16 citation statements)

References 42 publications

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“…[43] has utilized the estimated derivative of the inductance profile to control the force in a linear SRM. In SRMs, since the flux linkage and current go to zero periodically, the phase flux linkage may be obtained from the integral of the phase voltages [34], [44]. More advanced techniques have been utilized to form regression mechanisms for the inductance or the force profiles such as utilizing a support vector machine in [45] and with the addition of a kernel based regression in [46].…”

Section: Inductance Estimationmentioning

confidence: 99%

Inductance Surface Learning for Model Predictive Current Control of Switched Reluctance Motors

Shamsi

2015

IEEE Trans. Transp. Electrific.

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In this paper, an inductance surface estimation and learning for utilization with a stochastic Model Predictive Control (MPC) scheme for the current control of Switched Reluctance Motors (SRM) is introduced. This MPC is equipped with state estimators and is implemented as a recursive linear quadratic regulator for practical deployments in hybrid vehicle applications. Additionally, a learning mechanism is developed to dynamically adapt to the inductance profile of the machine and update the MPC and Kalman filter parameters. The introduced control scheme can cope with noise as well as uncertainties within the machine nonlinear inductance surface. The introduced system will benefit from a fixed switching frequency and will offer low current ripples by calculating the optimal duty cycles using the SRM model. Lastly simulations and experimental results are provided to evaluate the proposed method.

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Section: Inductance Estimationmentioning

confidence: 99%

Inductance Surface Learning for Model Predictive Current Control of Switched Reluctance Motors

Shamsi

2015

IEEE Trans. Transp. Electrific.

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“…In [23], the SRM is developed based on the data extracted from finite element analysis (FEA). Regardless of the wide acceptance of FEA in machine design and characteristics calculation, it needs the detailed design data of machine geometry and material properties which may not be open to the public [23,24]. Moreover, the manufacturing process introduces tolerances that make the exact determination of machine geometry data a difficult task.…”

Section: Introductionmentioning

confidence: 99%

“…It does not require any machine geometry data. Besides, the physical effects and imperfections during the manufacturing processes are contained in the measured data [24,25]. Therefore, the experimental measurement of magnetic characteristics is adopted in this paper.…”

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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“…Once the magnetization curves are obtained, the SRM model is generally built through lookup tables or neural network [4,12]. For both methods, a large amount of data is required.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, a mathematical processing procedure is commonly used to achieve a larger number of magnetization curves. In some cases, analytical equations are developed to obtain intermediary magnetization curves [12][13][14]. However, experimental process errors, such as sensor errors, noise, residual currents, and numerical integration disparities, are encountered in the data obtained experimentally in magnetization tests [12,15].…”

Section: Introductionmentioning

confidence: 99%

Mathematical Modeling of Switched Reluctance Machines: Development and Application

Paula¹,

Lopes²,

Barros³

et al. 2020

Modelling and Control of Switched Reluctance Machines

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The usage of switched reluctance machines (SRMs) grows following the power electronics development. For this reason, a precise mathematical model is crucial for the development of SRM automatic control projects due to the nonlinearities caused by the machine topology and working principle. This chapter focuses on SRM characterization procedure, enlightening the nonlinear characteristics and the importance of the magnetization curves to accomplish precise automatic control of SRM. Different methods found in the literature are commented. The blocked rotor test is detailed, and an automatic acquirement system to obtain the SRM magnetization curves is reasoned. Magnetization curves are processed to create the mathematical model of the SRM. The computational algorithm used to process the acquired data is presented with the purpose of clarifying the production of the lookup tables used in the mathematical model. The developed mathematical model is implemented in Matlab/Simulink® environment. The system simulates the SRM operating both in motoring and generating mode. The mathematical simulation results are compared to experimental results. The developed model is accurate and may be used to study SRM behavior and control systems for SRM applications.

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High-Fidelity Magnetic Characterization and Analytical Model Development for Switched Reluctance Machines

Cited by 28 publications

References 42 publications

Inductance Surface Learning for Model Predictive Current Control of Switched Reluctance Motors

Inductance Surface Learning for Model Predictive Current Control of Switched Reluctance Motors

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

Mathematical Modeling of Switched Reluctance Machines: Development and Application

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