Performance Of Permanent Magnet Synchronous Motor Research Articles

Comparison of permanent magnet synchronous machine and permanent magnet flux‐modulated machine in direct drive considering number of rotor poles

AbstractDirect drive permanent magnet (PM) motors have become a very attractive choice for electric vehicles. Generally, PM motors are divided into PM synchronous motors (PMSMs) and PM flux‐modulated motors (PMFMMs). High torque density is the main requirement of direct drive motors, therefore, the structure of conventional PM motors is modified to boost the torque. Increasing the number of poles is a structural modification to achieve high torque density in PMSMs; however, a large number of poles affects motor characteristics such as power factor, flux weakening, losses, and efficiency. The other way to have high torque is to use PMFMMs, which have a high intrinsic torque density. This paper first compares the performance of PMSMs with different number of poles. Then the performance of PMFMMs and PMSMs are compared to show their differences. The performance of PMFMMs with different teeth modulators and magnetic gear ratios are predicted to show their effects upon its performance. The simulation results using finite elements method|finite element method (FEM) are presented, and finally, a PMFMM with 36 slots and 40 poles as case study is subjected to an experimental test and its results are compared with FEM.

Mechanism-Based Fault Diagnosis Deep Learning Method for Permanent Magnet Synchronous Motor

As an important driving device, the permanent magnet synchronous motor (PMSM) plays a critical role in modern industrial fields. Given the harsh working environment, research into accurate PMSM fault diagnosis methods is of practical significance. Time–frequency analysis captures the rich features of PMSM operating conditions, and convolutional neural networks (CNNs) offer excellent feature extraction capabilities. This study proposes an intelligent fault diagnosis method based on continuous wavelet transform (CWT) and CNNs. Initially, a mechanism analysis is conducted on the inter-turn short-circuit and demagnetization faults of PMSMs, identifying and displaying the key feature frequency range in a time–frequency format. Subsequently, a CNN model is developed to extract and classify these time–frequency images. The feature extraction and diagnosis results are visualized with t-distributed stochastic neighbor embedding (t-SNE). The results demonstrate that our method achieves an accuracy rate of over 98.6% for inter-turn short-circuit and demagnetization faults in PMSMs of various severities.

Modeling of genetic algorithm tuned adaptive fuzzy fractional order PID speed control of permanent magnet synchronous motor for electric vehicle

This study presents a novel Genetic Algorithm-optimized Adaptive Fuzzy Fractional Order Proportional Integral Derivative (GA-AFFFOPID) controller for enhancing the speed control performance of permanent magnet synchronous motor (PMSM) drives in Electric Vehicles. The proposed GA-AFFFOPID controller, which combines the advantages of genetic algorithm optimization and adaptive fuzzy fractional-order PID control, represents a unique and innovative approach to address the control challenges associated with PMSM drives. Permanent magnet synchronous motor technology, known for its efficiency, compactness, reliability, and versatility in motion control applications, is increasingly adopted in electric vehicle drive systems. However, the inherent non-linearity, dynamics, and uncertainties of permanent magnet synchronous motors pose significant control challenges. The exceptional performance of the GA-AFFFOPID controller, demonstrated through its superior system dynamics, precise speed tracking, and robustness against parameter variations and sudden load disturbances, underscores the significant advancements enabled by the genetic algorithm optimization technique in improving the control performance of PMSM drives for electric vehicle applications. Comparative analysis with traditional control methods demonstrates the exceptional performance of the Genetic Algorithm-optimized Adaptive Fuzzy Fractional Order Proportional Integral Derivative controller. These findings highlight the significant performance improvements facilitated by the genetic algorithm optimization technique in enhancing the control performance of the adaptive fuzzy fractional order PID controller in PMSM drives for electric vehicle applications.

Experimental Implementation of a TD3 Agent Based Speed Controller for Direct Torque Control of PMSM Drives

This manuscript presents a novel control approach for Permanent Magnet Synchronous Motors (PMSMs) by integrating the widely used Direct Torque Control (DTC) method with Deep Reinforcement Learning (DRL), specifically the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. The TD3 algorithm is well-suited to address the challenges posed by high-dimensional state and action spaces in PMSM drives. The conventional Proportional–Integral (PI) controller in the outer loop of DTC is replaced with the DRL based TD3 agent to overcome the limitations of traditional PI controllers, such as model dependency and complex parameter tuning. The DRL technique allows the agent to learn optimal control policies from experience, making it suitable for complex and nonlinear control problems. Extensive simulation studies demonstrate the effectiveness of the TD3-based control in achieving accurate speed tracking under varying operating conditions. Real-time experimental validation using a TMS320F28379D digital signal processor confirms the feasibility of the proposed DRL based approach. The research offers new insights into improving the performance of PMSM drives and paves the way for future advancements in electric drive control.

PMSM Design for Elevators: Determination of the Basic Topology Affecting Performance

In recent years, with the increase in high-rise building construction, there has been a rise in demand for elevators. This demand has spurred mutual growth between the vertical transportation sector and high-rise building construction. Both sectors are improving technology, security, and comfort standards. Traditional elevator traction machines typically employ asynchronous motors coupled with geared reducers, despite their low energy efficiency and requirement for large machine rooms. However, with the proliferation of rare-earth magnet materials and advancements in driver technologies, the use of Permanent Magnet Synchronous Motors (PMSMs) in elevator traction machines has increased. PMSMs operate with direct drive, eliminating the need for reducers. High efficiency and passenger comfort are crucial parameters for elevator traction machines. This study focuses on identifying the fundamental topology that could influence the performance of PMSMs for gearless elevator machines. Factors such as stator materials, rotor structures, permanent magnets, winding configurations, slot/pole combinations, cogging torque and torque ripple have been examined to select suitable design topologies for elevator motors. Important topics such as the magnetic properties of grain-oriented silicon steel sheets used in stator construction, B-H curves, lamination forming and stacking factors have been addressed. Two different rotor topologies and magnet arrangements, namely internal and external, have been investigated in PMSM designs. Additionally, the use of four different magnet materials, namely AlNiCo, Ferrite, NdFeB, and SmCo have been compared for PMSM traction motors in elevators. Single-layer and double-layer winding configurations, distributed and concentrated winding configurations have been compared. Fractional slot/pole combinations, which directly affect the performance and comfort of PMSMs, have also been examined based on information obtained from the literature. Particularly, structures with high winding factors, such as 12/10, 12/14, 18/16, 18/20, 24/22, and 24/26 have been identified. Finally, studies on skewing to reduce cogging torque and torque ripple have been addressed.

Research on Predictive Speed Control Scheme for Surface-Mounted Permanent Magnet Servo Systems

In order to improve the dynamic response and disturbance rejection performance of electric machines, a deadbeat predictive speed control (DPSC) scheme for a permanent magnet synchronous motor (PMSM) is proposed. To begin with, a DPSC controller was proposed with the purpose of achieving precise control for the next control cycle, and the control parameters were determined based on the optimal parameter design method. For better application, performance comparisons were made with a conventional PI control, and the mismatch effects of inertia and torque were analyzed. In order to improve the disturbance rejection performance of the system, an extended sliding mode observer (ESMO) was constructed to compensate for disturbances. Experimental verification with a conventional PI control indicates that the proposed DPSC control can reduce the speed response time from 0.675 s to 0.650 s. When the electric machine operates stably and is applied to a torque disturbance of 0.4 Nm, the speed fluctuation and settling time can be reduced from 9 rpm and 1.7 s to 6 rpm and 0.5 s, respectively. This proposed method effectively enhances the speed control performance of PMSM and can be applied to high-performance electric machine applications.

Modeling and Study of Different Magnet Topologies in Rotor of Low Rating IPMSMs

A study and performance analysis of magnet positions in different topology on the surface of rotor in permanent magnet synchronous motors (PMSMs) based on finite element method is observed here. A 3-phase interior PMSM is numerically simulated with ANSYS Maxwell 2018.1. Differently positioned permanent magnets improve the performance of PMSMs. Apart from handling nonlinear equations, FEM is used for simulation. ANSYS is used to analyze PMSM performance under variable conditions. This proposal examines a three-phase, 0.55 kW PMSM at 220 V, 50 Hz. Here rotor topologies of five types, namely, (i) spoke/tangential, (ii) saturable bridge / u-shape, (iii) V-shape, (iv) radial, and (v) segmented bridge permanent magnet rotor are taken for observation. The motive of this study is to analyze the performance of PMSMs in different rotor designs with different magnet positions. All topologies have been modeled and simulated with ANSYS. Each topology is compared in terms of electromagnetic and mechanical parameters. The 2D model is used to model the distribution of magnetic fields and the performance of operating parameters under transient and steady-state conditions. Magnetic flux, and efficiency are heavily influenced by the rotor shape with the volume of magnet. A study of radial force distribution and mechanical stress across rotor surface is also discussed. Motor performance is affected by the design of PMs.

Combining finite-time control and prescribed tracking performance for uncertain PMSM driven steer-by-wire system with unknown disturbance

To solve the uncertainties in permanent magnet synchronous motor (PMSM) driven steer-by-wire (SbW) system, a prescribed-performance-control (PPC) based finite-time fuzzy controller is proposed. Specifically, the dynamics of SbW system driven by PMSM is analyzed, and combine PPC technology to transform the tracking error coordinates. Then, the velocity signal is obtained from the sigmoid function-based tracking differentiator by using the position signal. To solve the problem of mismatched disturbance, fuzzy logic system (FLS) are used as a universal approximator to estimate the lumped nonlinear friction and other unknown functions, the boundary of lumped disturbance is estimated by the designed adaptive updating law. By fusing the designed virtual controller and the intermediate control law, the controller needs no precise parameters of system, under which the effect of uncertainty of control gain can be compensated completely. Finally, rigorous theoretical analysis based on the Lyapunov stability theory is provided to demonstrate the finite-time stability of the closed-loop system under consideration. Simulation and experiment results are presented to show the effectiveness of the developed control approach, and some comparisons are given to show the rapid and accurate position tracking control performance.

Improved control of permanent magnet synchronous motors using adaptive nonlinear control with Deadbeat Observer

Permanent Magnet Synchronous Motor (PMSM) control optimization is crucial for a variety of applications, including electric vehicles, industrial automation, and renewable energy systems (RESs). However, traditional control methods often struggle to adapt to the dynamic and non-linear nature of PMSMs, leading to sub-optimal performance. To address these challenges, this paper proposes a hybrid adaptive nonlinear control strategy with a deadbeat observer (DO) designed to improve the performance of PMSMs. This approach aims to improve the accuracy and robustness of the control while taking into account the system parameters variations and disturbances. Simulation tests comparing the proposed method with an adaptive nonlinear control (ANLC) are presented, highlighting its superior effectiveness in controlling PMSMs under varying load conditions and speed fluctuations. The proposed strategy achieves a maximum relative speed error around of 6% at 0.4 s under gradually varying load torque disturbances, which is better than the ANLC with 8%. Furthermore, under large speed variations, the suggested method maintains a maximum relative speed error of 0.82% at 0.85 s. Furthermore, the robustness assessment under system parameters variations, stator resistance, and inductance, shows extraordinary performances of the proposed scheme. These results highlight the effectiveness and robustness of the proposed strategy in achieving precise PMSM control while dynamically adapting to changing conditions. This research underscores the potential of our approach to advance key technologies, including electric vehicles, industrial automation, and renewable energy systems. By optimizing PMSM control, this strategy contributes to increased efficiency, reliability, and adaptability, facilitating broader adoption of electric propulsion systems and sustainable energy solutions.

Composite control based on FNTSMC and adaptive neural network for PMSM system

In this paper, a novel fixed-time non-singular terminal sliding mode control (NFNTSMC) method with an adaptive neural network (ANN) is proposed for permanent magnet synchronous motor (PMSM) system to improve PMSM performance. For nominal PMSM system without disturbance, a novel fixed-time non-singular terminal sliding mode control is designed to achieve fixed-time convergence property to improve the dynamic performance of the system. However, parameters mismatch and external load disturbances generally exist in PMSM system, the controller designed by NFNTSMC requires a large switching gain to ensure the robustness of the system, which will cause high-frequency sliding mode chattering. Therefore, an adaptive radial basis function (RBF) neural network is designed to approximate the unknown nonlinear lumped disturbance including parameters mismatch and external load disturbances online, and then the output of the neural network can be compensated to the NFNTSMC controller to reduce the switching gain and sliding mode chattering. Finally, the fixed-time convergence property and stability of the system are proved by Lyapunov method. The simulation and experimental results show that the presented strategy possesses satisfactory dynamic performance and strong robustness for PMSM system. And the proposed control scheme also provides an effective and systematic idea of the controller design for PMSM.

Experimental Validation of the Generation of Direct and Quadratic Reference Currents by Combining the Ant Colony Optimization Algorithm and Sliding Mode Control in PMSM using the Process PIL

This article aims to enhance the control efficiency of the Permanent Magnet Synchronous Motor (PMSM) by generating optimal reference currents and using Ant Colony Optimization (ACO), while ensuring a minimal absorbed current condition to reduce energy consumption and optimize PMSM performance. The ACO algorithm is chosen for its ability to find global solutions and robustness in complex environments, while Sliding Mode Control (SMC) provides advantages in terms of robustness against disturbances and the ability to maintain the system in a desired state. The implementation of the processor-in-the-loop (PIL) technique using MATLAB software with code composer and the LAUNCHXL- F28069M board enables the controller to be implemented in real hardware (LAUNCHXL-F28069M) to test the simulation environment (inverter and PMSM). Our results demonstrate the efficiency of ACO compared to the analytical method (AM) in terms of response time and minimizing absorbed current for different load values. Artificial intelligence (AI) has successfully and efficiently addressed the non-linearity between torque and reference currents, thus reducing energy consumption. This has allowed for the optimization of PMSM performance in a straightforward and efficient manner.

Comparison and analysis of power and torque performance of PMSM with different rotor structures

In this paper, according to the double reaction principle of permanent magnet synchronous motor (PMSM), the power, torque, and power angle characteristics are derived theoretically. Four motor models of tangential type, radial type, V-type, and U-type PMSM are established by finite element software. The magnetic flux density of the motor is analyzed in detail. At the same time, performance indicators such as efficiency, power factor, and air gap power of permanent magnet synchronous motors were calculated and evaluated, further improving the accuracy and credibility of the research. Based on the comparative study of the analysis results, the advantages and disadvantages of electromagnetic performance corresponding to different rotor structures are obtained, providing the design basis of PMSM.

Fuzzy compensation of model predictive control for PMSMs with external disturbances

In some high-speed scenarios, encoders may prove ineffective in providing real-time feedback, thereby increasing the installation and maintenance costs of Permanent Magnet Synchronous Motors (PMSMs). Consequently, the development of sensorless algorithms for medium and high-speed scenarios will enhance the practical applicability of PMSMs. In contrast to the angle estimation scheme for a rotated coordinate system, model predictive control (MPC) commences from the stationary coordinate system to explore Lyapunov stability and directly derive speed and angle based on the stability condition. The primary advantage of this approach lies in its ability to mitigate the accumulation of observation angle iteration errors, while simultaneously simplifying stability conditions. Furthermore, it is important to note that uncertain factors will significantly impede the performance of PMSMs. Subsequently, various fuzzy schemes are introduced to mitigate the impact of uncertain factors in the sensorless mode, and a rigorous proof is provided for the relationship between the fuzzy error and current error. Furthermore, a comprehensive analysis of controller stability conditions is conducted. Ultimately, experimental results demonstrate that the proposed fuzzy compensations effectively counteract external disturbances.

Application of sliding mode variable structure control algorithm in PMSM vector control system in complex environment.

In order to meet the increasing demand of high-performance control in industrial production, a new sliding mode variable structure control algorithm, Asymptotic Sliding Mode Control (ASMC), is designed in this study to solve the serious chattering problem of sliding mode control. Firstly, a traditional sliding mode exponential approximation law control model and a state space and control function are constructed based on sliding mode control. Secondly, by eliminating the jitter factor, ASMC algorithm is combined with sliding mode control to achieve precise control of permanent magnet synchronous motor (PMSM) and improve its performance. The experimental results indicated that in the simulation experiment, the research system tended to stabilize within 0.2-0.3 seconds, and the system chattering was significantly suppressed. And its output was smoother, the jitter amplitude was significantly reduced by 1/3, and the output torque was more stable. In addition, when the parameter H0 changed to 2H0, the overall speed curve did not change much, with only a slight overshoot. The overshoot was only 2.8%, and the change amplitude was maintained at around 25r/min, indicating that the research system had strong self stability performance. In actual experiments, the current command oscillation of the research system was significantly reduced. The local graph showed that the output fluctuation amplitude of the asymptotic approach law actual control was significantly smaller under no-load disturbance. When the H0 changed towards 2H0, the actual adjustment time was about 0.1 seconds, which was consistent with the simulation experiment. Therefore, the contribution of the research is that the ASMC algorithm can suppress the chattering problem of the system and improve the approaching speed, thus improving the speed regulation quality of the system. This new algorithm has great theoretical and practical significance for improving the performance of PMSM, and is practical in the actual vector control system of PMSM.

Exploring the Effects of Voltage Variation and Load on the Electrical and Thermal Performance of Permanent-Magnet Synchronous Motors

Europe has initiated the transition process toward IE4 efficiency motor classes, setting an example for other regions to follow in adopting higher-efficiency motor standards. However, in some regions, the operating voltage may differ from the nominal voltage according to the IEC 60038-2009. Therefore, the performance of new technologies under conditions of voltage variations (VVs) must also be assessed. This study presents a comprehensive analysis of a 0.75 kW line-start permanent-magnet motor (LSPMM) under different VV magnitudes, including undervoltage and overvoltage, while considering different load conditions. The study incorporates technical, economic, statistical, and thermal analyses to obtain important indicators related to power consumption, efficiency, power factor, and temperature. This study provides valuable insights to specialists regarding the technical and economic impacts of voltage-magnitude variation on LSPMMs.

Current decoupling control of permanent magnet synchronous motor based on improved nonlinear extended state observer

Precise current decoupling is required to achieve good control performance in the vector control of permanent magnet synchronous motor (PMSM), but traditional decoupling methods are unable to solve the problem of poor current decoupling effectiveness when the system inductance parameters are mismatched. Therefore, a decoupling control method of PMSM based on improved nonlinear extended state observer (INESO) is proposed in this paper. Firstly, a smooth, differentiable nonlinear function is used to replace the traditional fal function, which overcomes the problem of discontinuous gain changes. Secondly, a time-varying gain is designed to address the problem of reduced observer performance due to differential peak values in traditional NESO. Finally, the stability of INESO and the current loop closed-loop system is proven, followed by simulation and experimental verification. The simulation and experimental results show the proposed method effectively observes and compensates for system uncertainties, including parameter perturbations, external disturbances, and unmodeled dynamics. This method achieves complete decoupling of d and q-axes currents, while suppressing interference and enhancing the control performance of PMSM. The proposed INESO decoupling method reduces the d-axis current fluctuation about 96% and 90% when the load changes abruptly under motor inductance parameter mismatch, and can reduce q-axis current fluctuation about 88% and 78% when the d-axis current steps, respectively, compared with the conventional Current Feedback Decoupling Control (CFDC) and CDDC decoupling methods.

Design Optimization of an Automotive Permanent-Magnet Synchronous Motor by Combining DOE and NMGWO

This study proposes an optimization methodology for automotive permanent-magnet synchronous motors (PMSMs) to achieve maximum efficiency, maximum average torque, and minimum torque ripple. Many geometrical parameters can be used to define the PMSM of an automobile. To identify the most significant parameters for optimization, the fractional factorial design of the design of experiment (DOE) was employed for screening, considering the interaction effects. The central composite design was used to construct the proxy model between the optimization target and optimization variable, and the effectiveness of the model was judged. Aiming at the multi-objective optimization problem of a motor, a new mechanism for grey wolf optimizer (NMGWO) algorithm combining an elite reverse learning strategy, a local search strategy, and a nonlinear control parameter strategy is innovatively proposed. This algorithm was applied to solve the multi-objective optimization model. The numerical calculation results show that this is an effective optimization design method that can improve the performance of automotive PMSMs. The effectiveness of the NMGWO algorithm on the optimization results of permanent-magnet synchronous motors is verified by the experimental results.

Model-based robust servo control for permanent magnet synchronous motor with inequality constraint

Based on the dynamic model, a robust constrained control method for permanent magnet synchronous motor (PMSM) is proposed in this paper to achieve better trajectory tracking performance. Firstly, the constrained output variable of PMSM is transformed into unconstrained output variable by the monotone unbounded property of tangent function. After conversion, regardless of the uncertainty, the output angular displacement of PMSM always remains within the boundary. In addition, after state transformation of control variables, a new dynamic model of the system is obtained. Secondly, based on the new dynamic model, a control method based on model and error is proposed. Theoretical analysis shows that the proposed control satisfies the requirements of uniformly bounded and uniformly bounded limits. Thirdly, the practicability and effectiveness of the robust controller are verified by simulation and experiments. This method can restrain the influence of uncertainty well and improve the servo performance and operation reliability of PMSM significantly.

Stator current operation compensation (SCOC): A novel preprocessing method for deep learning-based fault diagnosis of permanent magnet synchronous motors under variable operating conditions

This paper proposes a novel preprocessing method, namely stator current operation compensation (SCOC), for deep learning-based fault diagnosis of a permanent magnet synchronous motor (PMSM) under variable operating conditions. To solve the problem that a change in operating conditions modifies characteristics of stator current signals in a way that makes it hard to discriminate fault modes, SCOC includes two stages: 1) tacho-less resampling and 2) main operating component subtraction with rescaling. First, the stator current signal is resampled to contain the same cycle for each deep learning input. Next, the signal is transformed to alleviate amplitude change by torque and to emphasize relatively small fault components. The effectiveness of the proposed method was validated by applying it to experimental data acquired under different types of PMSM operations. The results showed that each SCOC stage can reduce the variability between the data and effectively increase the fault diagnosis performance of PMSMs.

Comparative simulation study of electric motors for high performance of 3D printers

3D printing is a process in which material is joined layer by layer under computer control to make a 3D object easy to get almost any shape. This technology finds applications in various fields and it is attracting considerable interest from industry and academia. There are many different brands and models of motors used in 3D printers, and they all vary in quality and effectiveness. In this article we present the principal of the 3D printer, types of motors used in 3D printer and mathematical modelling of this motors like brushless direct current (DC) motor, permanent magnet synchronous motor (PMSM), and stepper motor. The models of analytical equations describing the three systems of motors are evaluated and developed by Matlab/Simunlink. In this paper, we have conducted a comparison of the series of results from simulation between the performance of PMSM, brushless DC and stepper motor. Among several criteria, this simulation study allows to select the criterion of response time to reach the desired speed and the precision of the rotor position to choose the most performant motor for 3D printers.