Switched Reluctance Motor Research Articles

Modeling of nonlinear characteristics of switched reluctance motor based on optimized RBF neuron network

The strong nonlinearity of switched reluctance motor (SRM) makes it difficult to establish an accurate mathematical model of its electromagnetic characteristics. In this paper, an artificial neural network modeling method is adopted to estimate the nonlinear flux linkage characteristics of a four-phase SRM. Based on the samples obtained by experimental measurement, a radial basis function neural network is trained and further optimized using the K-Means algorithm. The modeling accuracy of the optimized flux linkage model is analyzed in detail and also verified by dynamic simulation of a direct instantaneous torque control system of the motor.

Switched-Reluctance Motor Drive for More Electric Aircraft With Energy Storage Buffer

Switched-Reluctance Motor Drive for More Electric Aircraft With Energy Storage Buffer

Performance Analysis of Switched Reluctance Motor by Using Closed Loop Current Control Technique

This paper presents the performance analysis of an 6/4 3-phase switched reluctance motor in terms of current, flux, torque and speed. When the speed of the motor is low the current rises quickly. If the speed is large the increase in current is low. In low-speed region, the current can be controlled by using chop/hysteresis current control mode. In this mode a voltage of +Vdc and -Vdc is applied in such way the current is limited to a band. The bands can design around the reference value of current. The upper and lower bands can be designed based on the reference current. After the base speed we have to maintain current, current can’t increase quickly because speed becomes large, so in this region instead of keeping current constant the power is to be considered constant. When the speed is large this mode is not preferable. So still we have to maintain the power current has to build up, so we switch little earlier that angle is called advance angle. Angle by which voltage application is advanced that angle is the advance angle. The advance angle control can be used in such way that the current increase will happen in the lower inductance region only. The advancing of angle can be increased up to certain angles only i.e up to the lower value of inductance minimum point. The simulation has been carried out for 6/4 3-phase switched reluctance motor using MATLAB/ Simulink by applying closed loop current control technique. The detailed control of SRM for different speed modes has been carried out and the analysis of results demonstrated.

Compensating torque ripples in a coarse pointing mechanism for free-space optical communication: A Gaussian process repetitive control approach

Actuators that require commutation algorithms, such as the switched reluctance motor (SRM) considered in this paper and employed in the coarse pointing assembly (CPA) for free-space optical communication, often have torque-ripple disturbances that are periodic in the commutation-angle domain that deteriorate the positioning performance. The aim of this paper is to model the torque ripple as a Gaussian Process (GP) in the commutation-angle domain and consequently compensate for it at arbitrary velocity. The approach employs repetitive control (RC) at a constant velocity. A spatial GP with a periodic kernel is trained using data that is obtained from the RC step resulting in a static non-linear function for compensation at arbitrary velocity. Stability conditions are provided for both steps. The approach is successfully applied to a CPA prototype to improve the tracking performance for laser communication, where the torque ripple is compensated at arbitrary velocity.

Performance Analysis Of DSMST Converter FED BLDC Motor

A positive output voltage SEPIC-type DC-DC converter called DSMST (modified SEPIC topology) without a transformer for use in electric vehicles. The DSMST converter is derived from the traditional SEPIC converter with the addition of a switching cell. Furthermore, the output diode and inductors are interchanged for the continuous output current. In the realm of electric vehicles, the utilization of Brushless DC (BLDC) motor drives coupled with a DC-DC converter has become increasingly important. Specifically in the context of electric vehicles, the use of BLDC motors offers several technical advantages compared to other types of motors such as DC, induction, and switched reluctance motors. The DSMST converter is connected to the BLDC motor through a three-phase voltage source inverter (VSI) to regulate the voltage and current supplied to the BLDC motor, ensuring optimal performance. To regulate the speed of drive, a closed loop control system is developed using hall sensor signals. The proposed system is developed in the MATLAB/Simulink to check the applicability of electric vehicle configuration and the results shown that the proposed converter is well suitable for electric vehicle applications.

Quantized iterative learning control for impulsive differential inclusion systems with data dropouts

This paper studies the quantized iterative learning control with encoding–decoding mechanism of a class of impulsive differential inclusion systems with random data dropouts. First, the set-valued mappings in the differential inclusion systems are transformed into single-valued mappings by using the Steiner-type selector. Then, a learning algorithm based on the intermittent update principle is designed to address the data asynchronism problem caused by two-sided data dropouts. If the data are successfully transmitted at the actuator and measurement sides, then the control input is effectively updated. Furthermore, a suitable scaling sequence is introduced to ensure the system output to achieve zero-error tracking performance for a desired trajectory. An upper bound of the quantization level is determined such that the quantization error is always bounded. The results show that the quantization method reduces the burden of network communication at the cost of increasing the amount of computation, and the learning algorithm does not require the data dropouts to satisfy a certain probability distribution. Finally, the effectiveness of the learning algorithm is verified by numerical simulations of the switched reluctance motor system.

Switched Reluctance Motor Speed and Torque Control using ACO and PSO Algorithms

A Switched Reluctance Machine (SRM) is an electric motor that operates based on the principle of magnetic reluctance. In addition, SRMs have several advantages over other types of electric motors. However, SRMs also have some disadvantages. They can produce high noise and vibration levels, especially at high speeds. This paper describes the torque and speed controller for SRM using two techniques, Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) combined with a PI corrector. The conventional PI controller is nowadays used in most engineering, being acknowledged for their ability to give up superior control in power electronic systems. Moreover, finding appropriate values for the PI controller is not easy. A solution based on ACO and PSO is used to overcome this problem and simplify tuning the PI controller parameters. Simulation in MATLAB-Simulink was used to demonstrate the effectiveness of the suggested controllers.

Optimum design and research of switched reluctance motor for electric vehicle based on Motor-Cad

With the development of the economy and the deepening of the energy crisis, electric vehicle has now become the hotspot of the global automotive industry. Therefore, as the key part of electric vehicle, the design and research of drive motor has become the focus of attention of engineers and technicians. Because of its simple structure, easy maintenance, high output power, high output torque, wide speed range, and high efficiency, the switched reluctance motor (SRM) has been one of the first choice of electric vehicle driving motors. According to the performance requirements of electric vehicle driving motors, a 3-phase 24/16 pole with rated power of 30 kW switched reluctance motor (SRM) for electric vehicle has been designed in this paper by using Motor-Cad which can be used to optimize the parameters of motor to make the overall structure of the motor more reasonable. In order to reduce the torque ripple of switched reluctance motor, appropriate stator polar arc and rotor polar arc design are used in the motor. To realize the speed control of switched reluctance motor in a wide range, PWM control mode is adopted in this paper, which has fast motor response speed, high power factor and excellent dynamic and static performance. The simulation results show that the performance indexes of the switched reluctance motor meet the design requirements completely.

A Novel Magnetic-Geared Motor Combining Magnetic Gear and SR Motor

A direct-drive permanent magnet (PM) motor and a magnetic-geared PM motor have the advantage of high torque density. Therefore, these motors are expected to be applied as in-wheel motors for electric vehicles (EVs). However, they have a problem that it is difficult to achieve the wide speed-torque characteristics required for EVs. This paper presents a novel magnetic-geared motor that consists of a flux-modulated type magnetic gear and a switched reluctance (SR) motor. In this paper, the SR motor part and the magnetic gear part are designed, and both parts are combined into a magnetic-geared motor. The proposed magnetic-geared motor has the wide speed-torque characteristic, which direct-drive PM motors or SR motors cannot achieve.

Saliency Ratio-Based Torque Enhancement of Switched Reluctance Motors for Electric Bikes

Switched reluctance motors (SRMs) face challenges in achieving high performances in terms of the power factor, torque per ampere, and torque ripples. Although several parameters impact their performance, magnetic saliency has a significant impact. This paper presents a study on the influence of the saliency ratio on the performance of switched reluctance motors. A detailed design procedure for a mid-drive electric motor for a high-performance electric bike is presented. A systematic methodology is adopted to estimate the power rating for desired vehicle performance while considering the vehicle’s mechanical and aerodynamic parameters. Additionally, the paper discusses and analyzes the design of two SRM motor configurations with different rotor poles (6/4 and 6/10) while performing magnetostatic analysis for generated torque validation. A parametric study is performed on the 6/10 SRM design for further improvement in the torque profile while keeping the same fill factor and electrical loading. Furthermore, the transient analysis in Ansys Maxwell 2D for both SRMs confirms the designed performance. The results shows that the SRM with higher rotor poles (6/10) is a better choice.

Enhanced Cascaded Converters for Switched Reluctance Motor-Fed Electric Vehicles

This manuscript proposes an optimization technique for electric vehicles (EVs) operated through solar photovoltaic (PV) panels by enabling the drive of a switched reluctance motor (SRM) and an enhanced cascaded converter (ECC). The proposed method is an improved Tunicate Swarm Algorithm (ITSA). The Lévy flight distributions are emerging at the conventional Tunicate Swarm Algorithm (TSA) in the modified Tunicate Swarm Algorithm, which enhances the capabilities of diversification searching and avoids the potential for stagnation. The major objective of the proposed method is to minimize the ripple present in the supply current and give optimal photovoltaic power extraction to the solar module. The proposed enhanced cascaded converter method consists of a single main converter and one auxiliary converter combined as a series structure for the distribution of minimal voltage across the switch to restrict the switch stress of dv/dt. These quantities of error are lessened by the controller of Proportional–integral; the PI gain controller is tuned to the error and provides a reference current signal by using the proposed method. An optimum voltage gain and continuous input current minimize the impact of less solar irradiation in EV applications. Finally, the performance of the proposed method is implemented in the MATLAB platform and compared with existing methods.

Torque Ripple Minimization in EV Application

Switched Reluctance Motor (SRM) drives acts an alternative for the PM motor because of its suitable speed-torque characteristics for traction application. However, it exhibits higher torque ripple and larger acoustic noise. Hence, to overcome these problems, controllers are introduced. But the commercial controllers designed for SRM slows down its efficiency while implementing in traction application. Thus, this work modeled a specific controller, for SRM which makes it more suitable for Electric Vehicle (EV). A firefly tuned PI controller is implemented here to minimize torque ripple besides with speed control. It also optimizes the selection of both turn ON/OFF angle. Thus, to examine the efficiency of the proposed controller, the both simulation and hardware results were carried out. From the result analysis, it is observed that the proposed methodology reduces the ripple present in the torque.

Optimal Torque Sharing Function Control for Switched Reluctance Motors Based on Active Disturbance Rejection Controller

In this article, an optimal torque sharing function (TSF) control method incorporated with improved linear active disturbance rejection control (LADRC) and modified coyote optimization algorithm (MCOA) is introduced and applied in switched reluctance motors (SRMs). First, the piecewise TSF is employed to reduce the torque ripple, which exhibits little effects on the rate of current change and the current peak value. The improved linear extended state observer with the variable gain in speed controller using LADRC is designed to guarantee better antidisturbance ability and torque ripple reduction simultaneously. Then, the MCOA is introduced to automatically tune the parameters including the turn- <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> angle, conduction angle, and coefficients for better comprehensive performances. Finally, comparisons among the conventional TSF with LADRC, the piecewise TSF with LADRC, and the proposed TSF with improved LADRC are conducted both in the simulations and experiments. The results indicate that the proposed method behaves best in aspects of torque ripple suppression and anti-interference ability among the three methods.

An innovative and non-intrusive Hall effect sensor-based rotor position detection system for external rotor switched reluctance motor

An innovative and non-intrusive Hall effect sensor-based rotor position detection system for external rotor switched reluctance motor

Simple Strategy for Torque Ripple Minimization in Switched Reluctance Motor Drives

This article proposes a new simulation strategy to support the calculation of the angular interval of the current supply to minimize the torque ripple in switched reluctance machines, focusing on the motor working condition. Supposing the best angular interval is strongly linked to the working condition of the machine, a formula is needed to calculate the boundary angles of the intervals of the current supply for each phase, starting from real-time speed and electromagnetic torque. Starting from the dataset of simulations made with this new strategy, linear regression was used to train a model that computes useful formulas. The aim of this research is to show how the application of simple calculations allows torque ripple and power losses to be reduced, i.e., RMS phase currents, without altering the geometry of the machine. Simulations on a virtual four-phase 8/6 SRM are carried out to verify the model’s feasibility and effectiveness, even though this strategy can be easily applied to all other configurations of SRMs.

Equivalent circuit modeling and optimization design for a novel double‐stator linear‐rotary switched reluctance motor

SummaryThe energy coupling behavior is propagated through the magnetic field and would seriously affect the operation performance of two‐degree‐of‐freedom motion system based on linear‐rotary motor. This paper proposes a novel double‐stator linear‐rotary switched reluctance motor (DSLR‐SRM) to suppress the energy coupling behavior. First, the structure and operation principles of DSLR‐SRM are presented. The two stators of DSLR‐SRM are radially arranged to perform the independent magnetic circuit and suppress the energy coupling behavior through the magnetic field. Then, an equivalent circuit model is established to shorten the modeling and calculation time. It could be found that the energy coupling behavior directly affects the torque and force performance. Next, the sensitivity of the DSLR‐SRM dimensions is developed. Meanwhile, the dimensions of DSLR‐SRM are optimized to enhance the torque and force output via TOPSIS decision‐making. Finally, the validity of equivalent circuit modeling analysis and optimization results are verified through three‐dimensional finite element model.

Nonlinear Dynamic Model-Based Position Control Parameter Optimization Method of Planar Switched Reluctance Motors

Currently, there are few systematic position control parameter optimization methods for planar switched reluctance motors (PSRMs); how to effectively optimize the control parameters of PSRMs is one of the critical issues that needs to be urgently solved. Therefore, a nonlinear dynamic model-based position control parameter optimization method of PSRMs is proposed in this paper. First, to improve the accuracy of the motor dynamics model, a Hammerstein–Wiener model based on the BP neural network input–output nonlinear module is established by combining the linear model and nonlinear model structures so that the nonlinear and linear characteristics of the system are characterized simultaneously. Then, a position control parameter optimization system of PSRMs is developed using the established Hammerstein–Wiener model. In addition, with a self-designed simulated annealing adaptive particle swarm optimization algorithm (SAAPSO), the position control parameter optimization system is performed offline iteratively to obtain the optimal position control parameters. Simulations and experiments are carried out and the corresponding results show that the optimal position control parameters obtained by the proposed method can be directly applied in the actual control system of PSRMs and the control performance is improved effectively using the obtained optimal control parameters.

Online Current Spikes Suppression Strategy Research of Switched Reluctance Motors Based on Hybrid Torque Sharing Function

In this paper, an online compensation predictive control method based on hybrid torque sharing function (HTSF) is proposed to reduce current spikes in the torque sharing control of switched reluctance motor (SRM). First, predictive control is introduced to the torque sharing function (TSF) and a new cost function is designed with Laplace multiplier method by predicting the current and torque of the next cycle of SRM. Then the optimal current control amount is carried out. Second, the ratio of the torque error to the desired torque and its derivative are compensated online to the current phase at the beginning of the commutation overlap period to accurately track the torque, and the torque distribution is optimized by a HTSF at the end of the phase change to further reduce the current spikes and suppress the torque ripple. Finally, the experiment on 8/6 four‐phase SRM is achieved to verify the feasibility and effectiveness of the proposed control strategy, and the results show that the current spikes and the torque ripple are reduced by 25% and 14.5% respectively. Therefore, compared with traditional TSF control, this method can effectively reduce the SRM torque ripple and excessive current spikes. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Peter John Lawrenson. 12 March 1933—27 October 2017

Born in Prescot, Lancashire, Peter Lawrenson rose from relatively humble origins to become a leading figure in the world of electrical motors and generators—first as an academic and latterly as a businessman. Noteworthy research throughout the 1960s quickly earned him a personal chair at the University of Leeds, and for a number of years he was head of department at the university's School of Electrical &amp; Electronic Engineering. He is particularly remembered for pioneering the application of numerical methods to electromagnetic field problems, and for promoting and commercializing the switched reluctance motor, which—via the business, Switched Reluctance Drives Ltd, that he co-founded with research colleagues—was transformed from a laboratory curiosity to a commercially viable and highly efficient alternative to conventional electrical machines. A leading figure in the Institution of Electrical Engineers (IEE, latterly the IET), he also served with distinction as IEE president in 1992.

Optimizing stator/rotor pole ARC for torque and economy in electric vehicle switched reluctance motors

&lt;span lang="EN-US"&gt;Numerous researchers as well as the vehicle industry are paying increasing attention to the switching reluctance motor (SRM) drive. Due to its specialized qualities, SRMs are commonly used in electric vehicle applications. The stator/rotor pole arc of an 8/6 SRM is included in this study's investigation of design principles and performance improvement for poles arcs variation. Several limitations for the stator/rotor pole arc angle are derived for the design principles. These concepts are addressed in the finite element (FE) analysis of SRM topological parameters used in conventional and optimization analysis. The stator/rotor polar arc is tuned for performance using a genetic algorithm (GA) in order to maximize torque and efficiency. An ideal solution is chosen when the optimization results are represented. The proposed method can increase the maximum and average torque with (27.24%, 35.98%, 49.42%, 60.14%), (12.59%, 45.19%, 47.87%, 48.92%) for speeds [900, 1500, 2250, 3000 (rpm)] respectively, As shown by the comparison between the conventional and optimal designs. As well as the efficiency is improved (0.35%, 0.61%, 1.08%, 1.28%) for speeds [900, 1500, 2250, 3000 (rpm)] respectively.&lt;/span&gt;