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.

Artificial Neural Network-based Control of a Switched Reluctance Motor for a High-precision Positioning System

This paper presents a approach to control a switched reluctance motor (SRM) in the context of a high-precision positioning system using artificial neural networks (ANNs). The SRM is known for its robustness and simplicity, making it suitable for various applications, including positioning systems where precision is paramount. Traditional control methods often struggle to achieve the desired level of accuracy due to the non-linear and dynamic nature of the SRM. In this study, we propose an advanced control strategy leveraging the adaptive learning capabilities of ANNs. The neural network is trained to capture the intricate relationships between the motor's inputs and outputs, allowing for precise control in real-time. By measuring the electromagnetic torque and phase currents, the neural network is able to estimate the rotor position, facilitating the elimination of the rotor position sensor. The training data set of the neural network consists of magnetization data for the SRM with the electromagnetic torque and current as inputs and the corresponding position as outputs in this set. With a sufficiently large training data set, the artificial neural networks (ANN) can be correlated for appropriate network architecture.

Enhancing SRM Performance through Multi-Platform Optimization and FPGA-Based Real-Time Simulation

This paper proposes a multi-platform algorithm methodology in order to define firing angles of torque sharing functions (TSFs) for the indirect torque control of switched reluctance motors (SRM) through a hardware-in-the-loop (HIL) system. This proposal is used to achieve optimal levels of torque ripple and losses with accuracy and reliability, while taking advantage of real-time simulation. The analysis is performed assuming steady-state conditions of speed and torque reference for levels below the base speed, aiming to obtain firing angles ensuring optimal tracking performance. A novel methodology is proposed by using a grid search algorithm that handles the communication between Python, Code Composer, and the Typhoon HIL device. For that, an experimental data FPGA-based model with 500~ns of simulation time step is used to ensure highly accurate dynamic responses of current and electromagnetic torque. Moreover, controller-HIL (C-HIL) is used to have a safe and high fidelity testing environment, allowing rapid testing before transitioning to an experimental test bench. The simulation results are experimentally validated, demonstrating that the proposed strategy is effective and ensures optimal performance, taking into account peripheral systems of A/D, signal conditioning, PWM, and sensor emulation.

Fault-Tolerant Performance Analysis of a Modified Neutral-Point-Clamped Asymmetric Half-Bridge Converter for an In-Wheel Switched Reluctance Motor

Reliability is an essential factor for the operation of the Switched Reluctance Motor (SRM) drive. Electric vehicles operate in harsh environments, which may degrade the operation of power converters. These failure modes include transistor open- and short-circuits, freewheeling diode open- and short-circuits, and DC-link capacitor failures. This work presents a performance analysis of an in-wheel SRM for an electric vehicle under short-circuit (SC) and open-circuit (OC) faults of a modified Neutral-Point-Clamped Asymmetric Half-Bridge (NPC-AHB) Converter. The SRM is modeled as an in-wheel electric vehicle. A separate vehicle model attached to the motor is also developed for validation and performance of the NPC-AHB under different faulty scenarios. The performance of the modified NPC-AHB is also compared with that of a conventional AHB under faulty conditions for an in-wheel 8/6 SRM. The performance indicators such as torque, speed, current, and flux are presented from MATLAB/Simulink 2023b numerical simulations.

Robust deadbeat predictive current control for unipolar sinusoidal excited SRM with multi-parameter mismatch compensation

In the control of unipolar sinusoidal excited switched reluctance motors (SRMs), deadbeat predictive current controllers (DPCCs) have gained attention for their enhanced dynamic performance. However, periodic disturbances caused by mismatches between the predictive model’s nominal and actual system parameters degrade the control performance of SRMs. To address this issue, a robust DPCC with multi-parameter compensation is proposed to improve dq0-axes current control performance. By analyzing the impact of parameter mismatches, a Kalman filter (KF) is developed to compensate for inductance coefficient mismatches, mitigating periodic disturbances. Additionally, a disturbance estimator with measurement noise suppression is integrated into the DPCC for both state and disturbance estimation to handle residual uncertainties, including winding resistance mismatches, magnetic saturation, and unmodeled dynamics. Compared simulation and experimental results validate the effectiveness of the proposed robust DPCC.

New method for determining iron power losses in switched reluctance motors with laminated and composite magnetic cores

Power losses from motors and drives can decrease their efficiency. The development of devices with high efficiency is a necessity nowadays, with a view to reducing CO2 emissions. The application of new magnetic materials is one method for increasing the efficiency of motors, although prediction and calculation of the losses in the magnetic circuits of motors are also very important. To reduce the iron losses, iron powder composites are used here instead of Fe-Si laminated steel sheets, in a low-power switched reluctance motor (SRM), and a new method of determining the iron loss in the magnetic circuit is developed. Through the use of new materials in the motor, we can achieve a 19% decrease in the iron loss in comparison to a commercial motor. A comparison of the measurement results for our new method of iron loss prediction showed that the difference is approximately 20-26%, depending on the type of magnetic circuit used for the motor. The usage of soft magnetic composites rather than electrical steel sheets can decrease the loss in magnetic circuits. The results from our new method of determination of iron loss are in good agreement with results of measurements, meaning that it can be applied in a detailed analysis of SRMs. The application of soft magnetic composites in electromagnetic and electromechanical converters is a new method for lowering iron losses in new devices. The determination of such losses in SRMs is still challenging, and the proposed method broadens the state of the art in this area. The method developed here is based on motor current measurements, FEM calculations, and measurements of the magnetisation curves and iron loss for samples according to IEC standards. The results obtained in this work can be used by designers for the development of new kinds of SRMs.

An improved finite set model predictive control of SRM drive based on a voltage vectors strategy for low torque ripple

The robust rotor structure and fault-tolerance characteristics of the Switched Reluctance Motors (SRMs) are the best choice for next-generation Electric Vehicle (EV) applications. This machine has few restraints like high torque and flux ripples. However, the existing Model Predictive Control (MPC) using multiple control objectives and maximum sectors in the switching table results in high torque ripples due to the improper sector partition, Voltage Vectors (VVs) and weight factor (K1) selection. This paper proposes a Finite Set-Model Predictive Control (FS-MPC) for an analytical model of a non-linearity SRM machine to analyze the torque ripple performance. The proposed VVs are derived using sector partition based on the rotor position. The control is designed as a single cost function with the weighting factor contributing to smooth torque by selecting optimal control signals. Simulation studies and experiments with a four-phase 8/6 SRM drive verifies the enhanced FS-MPC for real-time implementation. The dynamic speed and ripple values of SRM Drives are measured using a mixed signal oscilloscope and the sensor probes. The laboratory outcomes calculate the analytical equations to validate the findings. The calculated value of torque ripple is 9 % through this FS-MPC. The study reveals that the proposed method is well suited for torque ripple reduction than flux ripples.

Improved Reduced Switch Converter Topology for Torque Ripple Reduction in Four-Phase Switched Reluctance Motor Under Dynamic Loading

In this paper, simulation, and performance comparison of a switched reluctance motor (SRM) for a classical asymmetric bridge converter and an improved reduced switch converter topology are provided. In an improved converter, switches are shared by adjacent phases, and one diode is connected in series with each phase winding to avoid reversal of current. Double-phase magnetization is adopted for the analysis of both converter topologies. The performance of both converter topologies are demonstrated using a proportional plus integral (PI) based hysteresis current controller and dynamic load on a 7.5 kW, 8/6 pole, 4- phase SRM. Improved reduced switch converter topology uses half the number of switches as compared to the classical asymmetric bridge converter. According to the simulation results, an improved converter offers superior starting and average torque with less speed settling time. An improved converter reduces the most serious problem of torque ripple in SRM by 50% compared to the classical topology. The reduced switches lower the winding losses and improve the efficiency of the drive system.

Design of Novel Double-Stator Switched Reluctance Motor Drive System for Electric Vehicles

Design of Novel Double-Stator Switched Reluctance Motor Drive System for Electric Vehicles

Design and Evaluation of Modular Stator Hybrid-Excitation Switched Reluctance Motor for Torque Performance Improvement

Design and Evaluation of Modular Stator Hybrid-Excitation Switched Reluctance Motor for Torque Performance Improvement

An Online Torque Sharing Function Method Based on Region Division for Reducing Torque Ripple and Copper Losses of Switched Reluctance Motors

An Online Torque Sharing Function Method Based on Region Division for Reducing Torque Ripple and Copper Losses of Switched Reluctance Motors

Model-Free Current Predictive Control Method for Switched Reluctance Motors

Model-Free Current Predictive Control Method for Switched Reluctance Motors

Direct Instantaneous Torque and Axial Force Control Method for Linear-Rotary Switched Reluctance Motor With Two Radial Windings

Direct Instantaneous Torque and Axial Force Control Method for Linear-Rotary Switched Reluctance Motor With Two Radial Windings

Current Profiling Control for Torque Ripple Reduction in the Generating Mode of Operation of a Switched Reluctance Motor Drive

The benefits of utilizing Switched Reluctance Motor (SRM) drives in traction applications can be realized fully by improving the electromagnetic performance of the machine in the generating mode of operation. This is because the generating capability of an SRM drive could be utilized for regenerative braking and also for the machine to generate power for the vehicle while the engine is in operation. In this paper, a current profiling-based control strategy is proposed to reduce the torque ripple in an SRM drive in the generating mode. The reference current profile is determined using a multi-step computation method to minimize torque ripple and maximize the average torque. The reference current profile is derived based on the reference torque command by utilizing the torque–current–angle look up table. The flux linkage characteristics of the SRM are considered when deriving the phase reference current profile. Then, the performance of the proposed profiling method, analytical linear and cubic torque sharing functions (TSFs), and the average torque optimization scheme are compared using simulation results. Finally, an experimental correlation is performed to validate the efficacy of the proposed control scheme.

Research on Heave Compensation System Based on Switched Reluctance Motor

Aiming at the requirements of the marine work platform for real-time control, time-varying speed and the time-varying torque control of the motor-driven active heave compensation device, this paper introduces a composite control strategy based on the switched reluctance motor (SRM)-driven active heave compensation device. As for the compensation error caused by the time lag in the real-time system, the model prediction trajectory algorithm is used to predict the compensation displacement obtained using the dynamic model. The next time, the control parameters are then provided for the SRM control system in advance to reduce the compensation error. The SRM control strategy selects a double closed-loop compound control strategy of Back Propagation (BP) fuzzy neural network Proportion Integration Differentiation (PID) control. Its outer speed loop uses a fuzzy controller to quickly track a wide range of speed changes. The torque inner loop uses BP neural network adaptive PID control. This helps to reduce torque ripple and to ensure that the electromagnetic torque output of the SRM remains stable. Finally, the system feasibility is verified by setting different wave parameters. The simulation results show that the simulation conditions can reach 97.5% and 96.4% under the 3 and 4 wave levels, respectively. The simulation effect is satisfying, which verifies the feasibility of the proposed scheme.

Reduction of Torque Ripple and Axial Force in a Fully Pitched Axial Flux Switched Reluctance Motor Using a Double Stator Structure

Reduction of Torque Ripple and Axial Force in a Fully Pitched Axial Flux Switched Reluctance Motor Using a Double Stator Structure

New transverse linear switched reluctance motor design and analysis for transit uses

New transverse linear switched reluctance motor design and analysis for transit uses

Comparative Evaluation of Speed Control Techniques for Switched Reluctance Motors in Industrial Applications

Abstract This paper offers a thorough summary of the comparative assessment of speed control methods for Switched Reluctance Motors (SRMs) in industrial settings. It is primarily concerned with two topics: optimization methods and PID controller applications. Studies on PID controllers have demonstrated improved speed control performance for SRMs; however, these findings frequently lack real-world validation and comparisons with other approaches. The difficulties with implementation are also covered. Fuzzy logic, evolutionary computation, particle swarm optimization, and genetic algorithms are some of the optimization approaches that have been used to enhance SRM speed control. However, there are still several issues with computational complexity, noise and volatility in parameters and real-time implementation. Numerous research assessed the ways in which different strategies and algorithms decreased torque ripples and controlled speed. However, these comparisons frequently fall short in terms of thoroughness and in-depth talks about real-world difficulties. All things considered, the analysis of the literature indicates a wide variety of approaches and methods for SRM speed control, each with advantages and disadvantages. Subsequent investigations may tackle these constraints, carry out more thorough comparative evaluations, and investigate the viability of actual application in industrial settings.

Direct torque control of switched reluctance motor driven by full bridge power converter

Direct torque control of switched reluctance motor driven by full bridge power converter

Bi-directional DC-DC Converter and Fuzzy Logic Controller Based Switched Reluctance Motor for EV Applications

Bi-directional DC-DC Converter and Fuzzy Logic Controller Based Switched Reluctance Motor for EV Applications