Model Of Switched Reluctance Motor Research Articles

Multi-objective optimization of switched reluctance motor driving electric vehicles based on vehicle operating condition

To improve the comprehensive performance of the switched reluctance motor (SRM) for electric vehicles (EVs) under actual driving conditions, a novel geometrical optimization method based on the time-varying phase current characteristics and the comprehensive indicator with justified weight coefficients is proposed in this paper. First, based on the established dynamic models of EV and SRM, the actual operating phase currents of the motor working under the New European Driving Cycle (NEDC) are extracted and classified to develop the representative optimization conditions. Then, considering the requirements of EV, five indicators are defined to evaluate the comprehensive performance of SRM, and a new multi-objective synchronization optimization function is proposed with five weight coefficients obtained by the statistical analysis method for the sample data of the indicators. Furthermore, the geometrical optimization for a four-phase 8/6 SRM is carried out by the combination of Particle Swarm Optimization (PSO) and BP neural network algorithms, after analyzing the effects of key geometrical parameters on the indicators. Finally, the work performance of the optimized SRM is evaluated and compared with that of the initial prototype, and the results show that the optimized SRM with the proposed multi-objective synchronization optimization method can improve the comprehensive indicator by 59.59%, and can significantly enhance the vehicle actual working economy by 50.74% under NEDC cycle.

Speed Control of Switched Reluctance Motor using Adaptive Fuzzy Backstepping Sliding Mode Control

The Switched Reluctance Motors (SRMs), with many outstanding advantages, are gradually being widely applied in industries, households, and recommended in many works. However, most studies in the field of SRM control only concentrate on the mathematical model of the motor itself, neglecting the nonlinearity introduced by the inverter, which is responsible for switching between phases to drive the motor. This paper proposes an adaptive backstepping sliding mode control algorithm based on the SRM nonlinear model that combines both the motor and the inverter. Firstly, a backstepping sliding mode controller is used to track the desired value and ensure the stability of the system according to the Lyapunov criterion. Secondly, a fuzzy logic system is added to adjust the controller parameters to account for uncertainty and external disturbance, as well as to minimize the chattering phenomenon. Finally, a few simulation scenarios are performed to assess the effectiveness of the proposed controller. The simulation results clearly demonstrate that the proposed controller surpasses the previously published H infinity controller in terms of speed control quality for the combined nonlinear model of SRM. The proposed controller exhibits zero steady-state error, zero overshoot, and a short settling time of approximately 0.5 seconds. Moreover, the system's output quickly stabilizes when affected by disturbance noise.

A simple approach for the computation of magnetic characteristics of 4-phase switched reluctance motor

Switched reluctance motor (SRM) is non-linear in nature due to non-uniform air gap between stator and rotor that is produced from doubly salient poles. To control the operation of non-linear motor, a precise modelling of magnetic characteristic is essential. Traditionally magnetic equivalent circuit, finite element method (FEM) and experimental approaches have been used to derive magnetic characteristics of SRM. These methods involve complex computational steps and numerous assumptions for its calculations. To avoid this complexity, a simple approach is suggested in this paper to compute magnetic characteristics of 4-phase SRM. In this approach, SRM phase inductance is measured directly to identify the location of rotor with respect to stator from unaligned to aligned position. Then, single phase of SRM is excited through asymmetric converter and voltage and currents of corresponding phase is recorded with rotor being blocked using indexing head. Later recorded values are used to compute the magnetic characteristics from unaligned to aligned position. The proposed method is carried out using field programmable gate array (FPGA) controlled 8/6, 4-phase SRM. Further, accuracy of obtained magnetic characteristic is verified using FEM. In addition, fidelity of the magnetic characteristic is also validated by developing a dynamic model of SRM in MATLAB/Simulink

Torque Ripple Minimization of Variable Reluctance Motor Using Reinforcement Dual NNs Learning Architecture

The torque ripples in a switched reluctance motor (SRM) are minimized via an optimal adaptive dynamic regulator that is presented in this research. A novel reinforcement neural network learning approach based on machine learning is adopted to find the best solution for the tracking problem of the SRM drive in real time. The reference signal model which minimizes the torque pulsations is combined with tracking error to construct the augmented structure of the SRM drive. A discounted cost function for the augmented SRM model is described to assess the tracking performance of the signal. In order to track the optimal trajectory, a neural network (NN)-based RL approach has been developed. This method achieves the optimal tracking response to the Hamilton–Jacobi–Bellman (HJB) equation for a nonlinear tracking system. To do so, two neural networks (NNs) have been trained online individually to acquire the best control policy to allow tracking performance for the motor. Simulation findings have been undertaken for SRM to confirm the viability of the suggested control strategy.

Accounting for magnetic saturation in designing a SRM speed controller for torque ripple minimization

This study established a nonlinear control design for switched reluctance motor (SRM) vehicle applications, using the backstepping approach. The suggested controller is established according to a model that consider magnetic saturation while reducing torque ripple and resulting in less vibrations. To optimize torque ripple, control angles are adjusted based on the machine speed and torque measurements. Indeed, a lookup table is constructed, offering the efficient control angles for various motor operating points. The suggested control technique was validated through simulation, exploiting an accurate MATLAB SRM model considering magnetic saturation effects. To illustrate the superiority of the suggested regulator, a comparison of its performance with a proportional-integral (PI) controller was performed. The acquired findings indicate the suggested regulator’s effectiveness.

SR Motor T-I Characteristics Performance Simulation Validation through Experimental Results

This article presents T-I (Torque–Current) characteristics performance of switched reluctance motor (SRM), simulation validation through experimental results. SRM has a simple construction, high fault tolerance capability, reliability, and low cost considered for the study. It works on the principle of reluctance. The simulation using MATLAB/SIMULINK for 8/6 SRM and experimentation tests in the laboratory is essential to find performance. Advance speed control technique of SRM drive using Digital Signal Processor (DSP-28335) controller and hardware setup for experimentation is utilized, for testing the performance. SRM is applicable in automation, electric vehicle, and industry. The characteristics study is a very important need in all applications. The simulation model for SRM is built in MATLAB/SIMULINK using proportional-integral controller. Experiments are conducted in the laboratory on SRM by load tests with a digital storage oscilloscope. Test data along with waveform are recorded for simulation and experimentation. Torque–Current characteristics performances are plotted. Finally, all results of both experimental and simulation are validated and presented. T-I characteristics of SRM are compared in both simulation and experimentation are very close to comparable.

Design and analysis of switched reluctance motor with FEM based Ansys-Maxwell

In this study, radial forces that create noise, which is a fundamental problem in Switched Reluctance Motor (SRM), are investigated. Since radial force causes stator vibrations and noise caused by magnetic field, a new switched reluctance motor model with increased stator and rotor poles has been designed in order to reduce the radial force acting on the rotor poles. In addition, a structure with high torque and low leakage flux compared to conventional winding structure has been proposed for Switched Reluctance Motors (SRM). The windings of the designed SRM are placed in layers, insulated from each other. Rotor poles are formed to move on both sides of each phase winding. Thus, the flux that will occur in the phase windings axially completes its circuit from the stator poles and the rotor poles. This designed ARM model has been examined by Finite Element Method (FEM. The proposed motor geometry has been analyzed with ANSYS Maxwell-3D, which performs FEM-based solution), and parametric analysis with ANSYS-RMxprt.

Intelligent Optimization of Switched Reluctance Motor Using Genetic Aggregation Response Surface and Multi-Objective Genetic Algorithm for Improved Performance

In this paper, a thorough framework for multiobjective design optimization of switched reluctance motor (SRM) is proposed. Selection of stator and rotor pole embrace coefficients is an essential step in the SRM design process since it influences torque output and torque ripple in SRM. The problem of determining optimal pole embrace is formulated as a multi-objective optimization problem with the objective of optimizing average torque, efficiency and torque ripple, and response surface models were obtained based on the genetic aggregation method. The results obtained by genetic aggregation response surface (GARS) and the non-dominated genetic algorithm (NSGA-II) were validated with the finite element method (FEM) model of the initial SRM. The optimized model displayed better efficiency profile over a wide speed range. The initial and optimized models recorded maximum efficiencies of 85% and 94.05%, respectively, at 2000 rpm. The efficiency values of 93.97–94.05% were achieved for the three pareto optimal candidates. The findings indicate the viability of the suggested strategy and support the use of GARS and NSGA-II as useful methods for addressing SRM key challenges.

Position Sensorless Control of Switched Reluctance Motor Drives Based on a New Sliding Mode Observer Using Fourier Flux Linkage Model

This paper proposes a new position sensorless method for switched reluctance motor (SRM) drive control based on a sliding mode observer using nonlinear Fourier flux linkage model. First, to obtain an accurate mathematical model of SRM for establishment of sliding mode position observer (SMPO), an analytical method based on Fourier polynomial function is introduced to regress the nonlinear relationship between flux linkage, position and current. Then, a new SMPO is designed in detail to estimate the rotor position, including the establishment of SRM model of sliding mode observer, and analysis of estimation error and convergence conditions of SMPO. Finally, position sensorless control of SRM drive based on SMPO is carried out in terms of accuracy and robustness in simulation. Experiments are also given on a dynamic testing platform to verify the effectiveness of the proposed position sensorless scheme.

An Improved Vector Control Strategy for Switched Reluctance Motor Drive Based on the Two-Degree-of-Freedom Internal Model Control

The unipolar sinusoidal current excited control strategy of the switched reluctance motor (SRM) has been widely attended for its wide driving area and acceptable torque ripple. However, due to the SRM’s time-varying, non-linear, and high coupling features, the traditional vector control strategies are unable to provide excellent current control performance. In this paper, a novel vector control method based on a two-degree-of-freedom internal model control (2DOF IMC) is proposed. The main objectives of this article are to improve the control precision and to enhance the system’s robustness. Firstly, considering the non-linear characteristics of the system in the rotating reference frame, a 2DOF IMC controller based on a simplified SRM model is designed and the stability analysis of the controller is implemented. Since the time-varying disturbance cannot be effectively eliminated using the 2DOF IMC with constant filter parameters, a simple adaptive disturbance observer (ADO) is incorporated into the inner-loop system to online estimate and eliminate these disturbances. Moreover, a stability analysis based on Lyapunov theory is also presented, and the ADO’s stability and convergence are guaranteed by tuning the adaption gain law. Finally, the effectiveness of the proposed control strategy is demonstrated by experiments, and the results illustrate that the proposed method can effectively improve the control and disturbance rejection performance of the SRM drives.

Dual Three-Phase Flux-Modulated Switched Reluctance Motor Drive With Maximum Torque per Ampere Strategy

This paper proposes a novel dual three-phase flux-modulated switched reluctance motor (SRM) drive with maximum torque per ampere (MTPA) capability. Different from the existing open-winding structure, the stator winding is in dual three-phase connection, which effectively eliminates the side effects of the zero-sequence harmonic voltage component. Hence, the modulating region of the space vector pulse width modulation (SVPWM) can be enhanced together with the increase of the dc-link voltage utilization ratio. The mathematical models of the dual three-phase flux-modulated SRM in the stationary reference frame and synchronous rotating reference frame are analyzed in detail. By considering the inductance nonlinearity, the MTPA control strategy is proposed based on the polynomial fitting method, which not only improves the torque capability but also simplifies the calculation of the current control system. The contributions of fundamental and harmonic currents to the electromagnetic torque are considered. By online adjusting the ratio of the dc-biased current component to the ac current component, the torque capacity can be improved. Besides, the effectiveness of the proposed MTPA control strategy can be enhanced by suppressing the harmonic current components caused by distorted back-EMF. Experimental validations are provided to verify the effectiveness of the proposed SRM drive.

Non‐linear model of the switched reluctance motor with included core losses’ effects

To accurately predict motor performance, considering the effects of core losses is of great importance, especially at speeds much higher than the base where the core losses represent a considerable portion of the total drive losses. Neglecting the effects of core losses leads to an error in the switched reluctance motor (SRM) performance prediction. In this study, a previously developed non-linear SRM model with multiphase coupling is extended by considering the instantaneous effects of core losses on the SRM performance. Using the new model, the nature of these effects is determined. A comparison of the simulation results of the proposed, basic, and finite element method (FEM) model confirms the importance of considering the effects of core losses on motor performance. Besides the FEM model, the accuracy of the proposed model is verified by experiments.

Minimization of torque ripple in switched reluctance motor based on MPC and TSF

In this paper, a novel control method based on model predictive control (MPC) and torque sharing function (TSF) is addressed to minimize the torque ripple of switched reluctance motor (SRM). Firstly, an accurate SRM model is established based on flux‐linkage characteristic curves obtained from the locked rotor test, which can predict the future operation state of the SRM drive system. Secondly, an improved TSF curve is proposed, and genetic algorithm is used to optimize TSF parameters to reduce torque ripple in commutation region. Besides, the MPC is integrated into the TSF control framework to replace the traditional hysteresis controller, and establishes a new TSF‐based model predicted torque control (MPTC) method, which avoids the frequency conversion problem caused by torque or current hysteresis controller. Further, a sector division scheme is addressed to decrease the number of candidate voltage states, whereby the computation of the controller in each sampling period is effectively reduced. Finally, in order to verify the correctness of the MPTC method, it is compared with the traditional direct instantaneous torque control (DITC). Both simulations and experiments on a three‐phase 12/8 pole SRM have confirmed that MPTC scheme can not only decreases torque ripple and stator copper losses, but also has higher efficiency than DITC. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Study of Longitudinal–Vertical Dynamics for In-Wheel Motor-Driven Electric Vehicles

The in-wheel motor (IWM)-driven electric vehicles (EVs) attract increasing attention due to their advantages in dimensions and controllability. The majority of the current studies on IWM are carried out with the assumption of an ideal actuator, in which the coupling effects between the non-ideal IWM and vehicle are ignored. This paper uses the braking process as an example to investigate the longitudinal–vertical dynamics of IWM-driven EVs while considering the mechanical–electrical coupling effect. First, a nonlinear switched reluctance motor model is developed, and the unbalanced electric magnetic force (UEMF) induced by static and dynamic mixed eccentricity is analyzed. Then, the UEMF is decomposed into longitudinal and vertical directions and included in the longitudinal–vertical vehicle dynamics. The coupling dynamics are demonstrated under different vehicle braking scenarios; numerical simulations are carried out for various road grades, road friction, and vehicle velocities. A novel dynamics vibration absorbing system is adopted to improve the vehicle dynamics. Finally, the simulation results show that vehicle vertical dynamic performance is enhanced.

WITHDRAWN: Investigation of electromagnetic characteristics of Switched Reluctance motor for photovoltaic applications

The magnetic characteristics play an important role in machine design and evaluating the performance of Switched Reluctance Motor (SRM). In this paper, a four phase SRM is modelled and the electromagnetic characteristics are obtained by finite element method (FEM). In addition, the paper presents a new converter designed for a SRM model that uses FEM to enable torque ripple minimization. With the aim of obtaining a high demagnetization voltage, the proposed converter is designed in the form of a simple structure consisting of one diode and one capacitor in addition to the front end of a conventional asymmetric converter. Based on this boost capacitor, the demagnetization voltage is found to be higher. Due to its high demagnetization voltage, the negative torque generation can be suppressed and hence improve the power rating. The proposed system is simulated using MATRIX LAB tools and their results are discussed elaborately.

Customized shape optimization of switched reluctance motor using B-Splines

PurposeThis study aims to design the rotor geometry of switched reluctance motor (SRM) in a completely flexible way. In the proposed method, there is no default geometry for the rotor. The initial geometry of the rotor can start from a circle or any other shape and depending on the required performance takes the final shape during the optimal design. In this way, the best performance, possible with geometric design, can be achieved.Design/methodology/approachThe rotor boundary of a 4/2 SRM is defined by a few B-splines. Some control points are located around the rotor and changing their locations causes customized changes in the rotor boundary. Locations of these points are defined as design variables. A 2-D finite element analysis using MATLAB/PDE is applied to the SRM model and sensitivity analysis is used to optimization design by means of minimizing of objective function.FindingsThe proposed method has many more capabilities for matching different objective functions. For the suggested objective function, while the conventional rotor torque profile difference with the desired torque profile reaches 40%, this difference for B-spline rotor is about 17%. Experimental results from a prototype motor have a close agreement with analysis results.Originality/valueThe B-splines have been used to design machines and electromagnetic devices. However, this method is used for the first time in design of the whole rotor of a SRM.

Performance investigation on ANFIS and FFNN assisted direct and indirect PV-fed switched reluctance motor water pumping system

ABSTRACT Alternative energy sources have impacted as the ideal choice for reducing the burden on the grid and producing clean energy for water pumping. As an alternative to conventional motors for pumping, switched reluctance motor (SRM) is promising, cost-effective and highly efficient. This paper presents artificial intelligence (AI) supported variable voltage angle control (VVAC) for indirect and direct photovoltaic (PV)-fed SRM-based water pumping systems (WPS). Proposed feedforward neural network (FFNN) and adaptive neuro-fuzzy inference system (ANFIS) models are simulated and analysed for their percentage average error and real-time execution speed in the dSPACE-1104 control board. ANFIS resulted in 1/10th reduction of execution time and improved performance accuracy in contrast to FFNN counterparts. A 4kW, 8/6 SRM model, has been developed in MATLAB/Simulink environment to validate the proposed system for both direct and indirect schemes. Further, the performance comparison of peak current, RMS current, torque ripple, and efficiency for direct and indirect PV-fed systems are presented in this work. These comparative assertions reveal the feasibility of both the methods with good performance. Moreover, a reduction in overall system size and cost with the absence of an intermediate converter, the direct method provides a reduced torque ripple as compared to the indirect counterpart.

Optimal Tracking Current Control of Switched Reluctance Motor Drives Using Reinforcement Q-Learning Scheduling

In this article, a novel Q-learning scheduling method for the current controller of a switched reluctance motor (SRM) drive is investigated. The Q-learning algorithm is a class of reinforcement learning approaches that can find the best forward-in-time solution of a linear control problem. An augmented system is constructed based on the reference current signal and the SRM model to allow for solving the algebraic Riccati equation of the current-tracking problem. This article introduces a new scheduled-Q-learning algorithm that utilizes a table of Q-cores that lies on the nonlinear surface of an SRM model without involving any information about the model parameters to track the reference current trajectory by scheduling the infinite horizon linear quadratic trackers (LQT) handled by Q-learning algorithms. Additionally, a linear interpolation algorithm is proposed to improve the transition of the LQT between trained Q-cores to ensure a smooth response as state variables evolve on the nonlinear surface of the model. Lastly, simulation and experimental results are provided to validate the effectiveness of the proposed control scheme.

Analytical Modelling of Radial Coupled Vibration and Superharmonic Resonance in Switched Reluctance Motor

Switched reluctance motor (SRM) generates considerable radial magnetic force during its running, which is much larger than the tangential magnetic force. The radial magnetic force interacts with the vibration displacement of the stator/rotor such that the coupled vibration causes a widespread concern in the industry. By building the stator/rotor coupled vibration model of SRM under the action of complex radial magnetic forces, the coupled vibration response of the motor could be quickly and effectively calculated. Furthermore, it has become a reality to summarize the response law of stator/rotor coupled vibration and superharmonic resonance. An analytical model of the stator/rotor coupled vibration in a 12/8-pole SRM excited by complex radial magnetic force is built with a finite element method (FEM). Each radial magnetic force applied onto the stator teeth and the magnetic resultants of the rotor are strictly derived via intermediate variables that vary with the rotational angle. The stator/rotor vibration response at the rated speed is numerically simulated, and its main characteristics both in time and frequency domain are also discussed in detail. The vibration of the stator exhibits dominant square deformation due to four orthogonal teeth being excited simultaneously. The eccentric rotor causes the radial magnetic force of a single stator tooth to fluctuate so that the vibration of the stator is transferred from square deformation to the coexistence of oval and square deformation. In addition, by solving the coupled vibration at different speeds cyclically, the corresponding speed when the stator or rotor has a superharmonic resonance could be found, which is more effective than existing commercial software. Using only the basic technical parameters of SRM, this proposed analytical model can be used to quickly evaluate the radial coupled vibration of the stator/rotor. Most importantly, the model can conveniently study the effects of other operating parameters on coupled vibrations and guide the selection of appropriate parameters.

Torque–flux linkage recurrent neural network adaptive inversion control of torque for switched reluctance motor

In order to reduce the torque ripple for switched reluctance motor (SRM), the learning error preprocessing-based torque–flux linkage recurrent neural network adaptive inversion control (TFRNNAIC) for SRM torque is proposed by the filter preprocessing and the non-linear mechanism characteristics for SRM. In TFRNNAIC with the advantages of parallel and series–parallel structure, the torque feedback error learning method is employed to update the weights of the torque–flux linkage recurrent neural network. To suppress the ripple effectively in torque error used for the weight learning of the torque–flux linkage recurrent neural network and obtain an accurate TFRNNAIC, namely the torque–flux linkage model for SRM, the low-pass filter preprocessing for the torque error is used. Moreover, the other low-pass filter is executed to reduce the ripple in the output for the PD torque control. The superposition of outputs for TFRNNAIC and PD torque control is taken as the reference flux linkage. Compared with other control strategies, such as the classical parallel neural network control, the classical series–parallel neural network control and TFRNNAIC, the simulation results show that the learning error preprocessing-based TFRNNAIC for SRM torque is capable of effectively reducing the torque ripple for SRM with the good recurrent performance.