Search Results (168)

In this study, a novel direct torque control (DTC) strategy is proposed to mitigate the torque ripple issue inherent in switched reluctance motors (SRMs), which is caused by the double salient pole configuration and the pulse power supply mode. The strategy is based on the prediction and optimization of a long-time-domain model. Central to this method is the development of a multi-step predictive optimization framework. By incorporating hysteresis control, the conventional approach of minimizing instantaneous error in predictive control is shifted towards minimizing tracking error over an extended time frame. A dual-objective evaluation function is also introduced, which simultaneously optimizes both torque smoothness and switching frequency, ensuring their collaborative enhancement. To validate the proposed method, a 6/4-pole SRM simulation model was implemented using MATLAB/Simulink 2024B, and comparisons were made with traditional methods. The results demonstrate that this strategy significantly reduces torque pulsation and lowers the system’s switching frequency, even under varying operational conditions such as different rotational speeds and sudden load variations. Consequently, this approach not only guarantees improved dynamic performance but also enhances the motor’s efficiency and stability. Full article

Advances in semiconductor technologies, particularly in power transistors and switching diodes, have enabled higher switching frequencies and converter efficiency, renewing interest in Switched Reluctance Motors (SRMs) for electric vehicles. This work presents a data-driven approach utilizing a Long Short-Term Memory (LSTM) network capable of effectively managing temporal dependencies for estimating rotor position without sensors in SRMs. The motor investigated was custom-designed, subsequently manufactured as a prototype. The LSTM was trained and validated with experimental data collected at various speeds and load conditions. The outcomes demonstrate the model’s strong performance, with a mean squared error (MSE) of ${1.77}^{{°}^2}$, a mean absolute error (MAE) of 1.09°, and 97.35% accuracy. Compared to typical estimation methods such as back-electromotive force (EMF)-based techniques, fuzzy logic, model predictive control, feed-forward neural networks (FFNNs), and back-propagation neural networks (BPNNs), the LSTM stands out as one of the most effective and widely used models. Previous neural networks (NN)-based studies typically report ±5° accuracy, whereas LSTM keeps the error about 1° in this study. This strategy eliminates position sensors, reduces cost and complexity, and enables reliable real-time SRM control. Results indicate that the method has significant potential for electric motor drives, particularly for SRMs. Full article

To reduce the computational burden of 3D finite element analysis for hybrid-flux high-speed switched reluctance motors (HFHSRMs), a quasi-3D parameterized equivalent magnetic network (EMN) is proposed. A parameterized radial–circumferential cross-grid is used to discretize the stator, air-gap, and rotor regions, and axial coupling branches are introduced to represent key 3D flux paths. Rotor rotation and rotor dislocation are implemented through a circumferential node-shift mapping, thereby avoiding topology reconstruction at different rotor positions. Core nonlinearity is incorporated using a piecewise fit of measured B–H data, and sparse-matrix assembly is adopted to improve solution efficiency. Based on the proposed EMN, key electromagnetic quantities are evaluated, including air-gap flux density, static characteristics, and dynamic characteristics. The results are validated against 3D finite element method (FEM) and prototype experiments. In the prototype experiments, the EMN prediction errors of key quantities are within 6%. In addition, computational efficiency is significantly improved compared with the 3D FEM, enabling rapid parameter iteration and early-stage design evaluation for HFHSRMs. Full article

This study presents a novel cooperative control strategy designed to mitigate torque ripple and enhance the disturbance rejection capability of switched reluctance motors (SRMs). The proposed approach integrates model predictive control (MPC) with direct instantaneous torque control (DITC), leveraging the torque sharing function (TSF) to generate phase-specific reference torque profiles. MPC employs rolling optimization to compute the optimal duty cycle in real time, achieving low torque ripple and consistent switching frequency during steady-state operation. To overcome the inherent delay in MPC’s dynamic response, DITC is incorporated as a fast-acting compensation loop that activates immediately upon detecting abrupt variations in speed or load, thereby delivering rapid torque adjustment and reinforcing system resilience. For validation, an 8/6-pole SRM control model was developed using Ansys/Maxwell and MATLAB/Simulink, and subjected to multi-scenario simulations. The results reveal that, compared to conventional MPC, the proposed method reduces steady-state torque ripple by 19.4% and shortens dynamic recovery time by 40%, demonstrating superior torque smoothness and improved robustness against external disturbances. Full article

The aim of this research is to investigate and reduce the torque ripple in Switched Reluctance Motor (SRM) drives, which is one of the major barriers to their acceptance for electric vehicle propulsion applications despite the advantages of robustness, efficiency, and wide operating range. High torque ripple not only deteriorates drive smoothness but also contributes to noise and vibration, demanding an advanced control strategy beyond traditional current-shaping and switching-based approaches. In this context, this work proposes a DDPG (Deep Deterministic Policy Gradient) Actor–Critic Neural Network-based reinforcement learning control framework that learns the optimal firing angle offsets dynamically to ensure less ripple electromagnetic torque under varying speeds and load conditions. The developed strategy has been designed and trained in MATLAB Simulink R2024b and then deployed in real time using an FPGA-based digital controller for validation on hardware. Comparative analysis with TSF (Torque Sharing Function) and DITC (Direct Instantaneous Torque Control) demonstrates that the reinforcement learning approach gives a much smoother torque response with better dynamic behavior over the operating range analyzed. Full article

This article focuses on power supply control issues in high-speed switched reluctance motors (SRMs). The primary scientific objective of this study was to determine whether and to what extent, the controller itself imposes limitations on SRM drive operation at very high rotational speeds, and to identify the maximum achievable speed range resulting from these limitations. Unlike most existing studies, which focus mainly on motor or power electronics constraints, this work explicitly analyses the dynamic limitations introduced by the control system architecture. An analysis of the essential controller functionalities required for implementing the SRM drive control algorithm with a C-dump converter was performed. The control system, composed of specialised hardware modules operating concurrently, was implemented in an field-programmable gate array (FPGA) device. Simulation and experimental investigations were conducted to evaluate signal propagation delays within the FPGA and their impact on the motor control process. Key functional modules contributing to the maximum signal propagation delays were identified, enabling a direct determination of the maximum motor speed at which correct power supply operation can be ensured. Furthermore, delays introduced by the power electronic components were characterized for the developed test controller, allowing a comprehensive assessment of both control and hardware-induced speed limitations. The research concluded that the FPGA-based controller introduces no significant limitations to the drive’s maximum speed. The maximum speed is limited by the mechanical constraints of the rotor and the inertia of the phase windings. Furthermore, expanding the controller with additional functionality does not significantly slow down the control algorithm’s execution. Full article

In response to the “carbon peak and carbon neutrality” strategy, industrial energy conservation has become increasingly important. Interior Permanent Magnet Synchronous Motors (IPMSMs) exhibit significant potential for efficient flux-weakening control due to their asymmetric rotor reluctance. However, conventional control strategies often cause instability during transitions across speed zones. This paper proposes a novel adaptive fuzzy-based smooth transition strategy to address this issue. First, a composite control framework integrating Maximum Torque per Ampere (MTPA) and leading-angle control is established to enhance flux-weakening capability. Then, within this framework, adaptive fuzzy controllers are designed for different weakening zones, incorporating a Lyapunov-based parameter adaptation mechanism for real-time compensation. Simulation results demonstrate that the proposed strategy achieves smooth switching across the entire speed range of IPMSMs. Quantitatively, it reduces speed overshoot by 5–15%, suppresses torque ripple by over 10%, and virtually eliminates switching current pikes compared to conventional methods, thereby significantly improving system dynamic performance and operational reliability. Full article

The switched reluctance motors (SRMs) are an attractive solution for electric vehicles (EVs) and hybrid electric vehicles (HEVs). However, the main drawbacks of SRMs are their highly nonlinear magnetic characteristics, complicated control algorithms, and the inherent torque ripples. This paper presents a simple structure average torque control (ATC) technique with a better ability to reduce torque ripples. Based on the detailed analysis of an inductance profile, this paper introduces a novel current compensation mechanism (CCM) that has the ability to profile the phase current and, hence, reduce the torque ripple. The proposed CCM is meant for the minimum inductance zone (MIZ) to profile the current of the ongoing phase. Over the MIZ, the inductance is independent of the phase current that helps to simplify the deduced mathematical formulations and provides a simple structure ATC with a lower computational burden, making it a feasible solution for real-time implementations and future developments. A series of experimental results are achieved to show the feasibility and effectiveness of the proposed ATC technique. The results show the superior performance of the proposed ATC, providing better torque profiles and reducing the torque ripples with an average value of 30% compared to conventional ATC. Full article

Switched reluctance motors (SRMs) are promising for applications such as air compressors due to their robust structure and fault tolerance, but suffer from high torque ripple and radial electromagnetic forces that cause vibration and noise. This paper proposes a game-theoretic multi-objective design optimization framework to enhance electromagnetic performance by simultaneously maximizing average torque and minimizing radial force. The optimization problem is transformed into a game model where objectives are treated as players with strategy spaces derived through fuzzy clustering and correlation analysis. Particle swarm optimization (PSO) is employed to solve the payoff functions under both novel cooperative and non-cooperative game scenarios of SRMs’ structural design. Finite element analysis (FEA) validates the optimized motor topology, showing that the cooperative game model achieves a balanced performance with high torque density and reduced vibration, meeting the requirements for air compressor drives. The proposed method effectively resolves the weight selection challenge in traditional multi-objective optimization and demonstrates strong engineering feasibility. Full article

This paper presents a systematic review of linear motor-based electromagnetic suspension, a key technology for reconciling vehicle comfort, handling stability, and energy consumption. The review focuses on two core areas: actuator configuration and control strategy. In configuration design, a comparison of moving-coil, permanent magnet synchronous (PMSLM), and switched-reluctance linear motors identifies the PMSLM as the mainstream approach due to its high-power density and performance. Key design challenges for meeting stringent vehicle operating conditions, such as mass-volume optimization, thermal management, and high reliability, are also analyzed. Regarding control strategy, the review outlines the evolutionary path from classical to advanced and intelligent control. It also examines the energy-efficiency trade-off between vibration suppression and energy recovery. Furthermore, the paper summarizes three core challenges for industrialization: nonlinear issues like thrust fluctuation and friction, the coupling of electromagnetic–mechanical–thermal multi-physical fields, and bottlenecks related to high costs and reliability verification. Finally, future research directions are envisioned, including new materials, sensorless control, and active safety integration for autonomous driving. Full article

Compared to switched reluctance motors, permanent magnet-assisted switched reluctance motor (PMa-SRM) have higher torque density, but the torque ripple remains severe, especially under high speeds or a heavy load. A novel torque shared function (TSF) control method with an adaptive turn-on angle in three regions is presented in this paper in order to suppress torque ripple. Firstly, the inductance and flux linkage of the PMa-SRM is presented, and the look-up table of the current is obtained by the inverse interpolation method to build the PMa-SRM model. Secondly, the reason for torque ripple caused by conventional TSF is explained. Thirdly, the two-phase exchange (TpE) region is sub-divided into two regions, and the control method in each region is set accordingly. An adaptive turn-on angle algorithm is designed, which can automatically accommodate different speeds and loads. Finally, simulation and prototype tests verify that the novel TSF control method could validly suppress the torque ripple of the PMa-SRM in a wide speed range. Full article

In recent years, switched reluctance motors have emerged as a promising option for various applications due to their low manufacturing cost, rare-earth-free construction, and mechanical robustness. However, their widespread adoption is often limited by high torque ripple and acoustic noise. To address these challenges, this paper presents a comparative study of the acoustic noise performance of an 18/12 switched reluctance motor under various current control techniques. This comparison offers valuable insight into the motor’s vibroacoustic characteristics, which is essential for optimizing SRM performance, particularly in applications where noise reduction is critical. Dynamic simulations of an SRM are carried out in MATLAB/Simulink, and multi-physics analyses are performed in ANSYS Workbench. The multi-physics modeling includes electromagnetic, modal, and harmonic response analyses for four current control techniques evaluated across different operating speeds under light-load conditions. The simulation results are validated experimentally using an actual motor mounted on a dynamometer setup. The corresponding acoustic signatures for each control technique are presented as 2D plots of equivalent radiated power from simulations and sound power level from experimental tests. In addition, experimental waterfall diagrams are provided for each control technique. Full article

The Switched Reluctance Motor (SRM) is a strong candidate for high-performance industrial drives and electric vehicle (EV) propulsion due to its robust, magnet-free construction and high fault tolerance. However, its main drawback lies in its nonlinear behavior, which produces significant torque ripple and acoustic noise, thereby hindering its widespread adoption. In recent years, Finite Control Set Model Predictive Control (FCS-MPC) has emerged as a promising alternative to mitigate these issues. Nevertheless, existing implementations typically rely on an eight-vector set comprising both single-phase and dual-phase excitations with unequal magnitudes, resulting in a nonuniform distribution in the αβ-plane. Unlike the conventional square-shaped distribution of vectors where excitation alternates between one and two phases, this study proposes a novel vector set that consistently energizes two phases in each selection. This approach achieves a uniform circular distribution in the αβ-plane, enabling the voltage magnitude to remain constant. The proposed eight-vector set leads to smoother current transitions, reduced torque ripple, and improved dynamic behavior. The strategy is validated on the MATLAB/Simulink platform, with detailed comparative results presented against the conventional method. The findings demonstrate a torque ripple reduction of up to 58% and an acceleration time improvement of up to 64%. These results highlight the strong potential of the proposed method for scalable SRM performance enhancement in demanding applications such as EV propulsion systems. Full article

In the context of fuzzy logic speed control for switching reluctance motor (SRM) applications, the objective of this work is to propose a unique zone-based simplification technique. Using the procedure that has been outlined, it is made easier to reduce membership functions as well as rule sets in a logical manner. This is accomplished by splitting the error–change-of-error plane into discrete decision zones. This method is separate from heuristic or adaptive reduction strategies since it employs a systematic framework that reduces the number of rules from 49 in the standard design to 9 and 5 without compromising the accuracy of the control. This is accomplished without adversely affecting the performance of the control. The simplified controller that was produced as a consequence of this study decreases the amount of overshoot, enhances the speed at which a dynamic response happens, and makes it simpler to use on digital platforms that are affordable. All of these capabilities were achieved by the controller. Based on simulations and testing carried out in the real world, it has been determined that the zone-based simplified fuzzy controller that was proposed has a superior performance to traditional PID and full-rule fuzzy systems in terms of reaction time, stability, and energy efficiency. Taking all of this into consideration, it is evident that it has the potential to be useful in real-world applications for SRM drives that demand a high level of speed while maintaining a low cost factor. Full article