Model Of Permanent Magnet Synchronous Motor Research Articles

Self-Controlled PMSM Drive Employed in Light Electric Vehicle-Dynamic Strategy and Performance Optimization

This proposed work demonstrates the illustration of dynamic performance intensification of a Permanent Magnet Synchronous Motor (PMSM) employed by a PWM controlled inverter which synchronizes with the rotor movement intimation. PMSM are widely hired in electric vehicles since it possesses better dynamic response, improved torque-speed property, and reduced noise, energy-efficient and power factor in comparison with traditional motors. In the present work, it is observed that by few modifications of the position control strategy as good as sinusoidal stator currents response generates less torque ripples. The mathematical model for PMSM is derived from park’s transformation. Further, a maiden attempt is taken to introduce the performance indicator ’sensor angle’ to estimate the rotor position in this strategy. From the established model, the various dynamic behavior of the drive system is determined analytically without and under various load disturbances. Additionally, a particle swarm optimization (PSO) technique is adopted to optimize the performance of the proposed dynamic strategy. An efficient speed control strategy by the variation of DC bus voltage is achieved which is equivalent to the armature voltage control of the conventional dc machine. Further, efficient and simple control circuitry of the voltage source inverter (VSI) is obtained in this strategy. To verify the efficacy of the proposed algorithm, necessary tests are carried out in a real-time setup. Therefore, an improved control strategy obtained from the simulation and an experimental approach meets the dynamic behavior employed in light weight electric vehicles.

Analysis of the Impact of Stator Inter-Turn Short Circuits on PMSM Drive with Scalar and Vector Control

Permanent Magnet Synchronous Motor (PMSM) failures are currently widely discussed in the literature, but the impact of these failures on the operation of control systems and the ability to detect selected failures despite the compensating effect of control algorithms being relatively rarely analyzed. The article presents the impact of damage to the stator winding of a PMSM motor on the operation of two frequency control structures, scalar and vector control. The mathematical model of PMSM that takes into account the influence of a different number of shorted turns in the stator winding phase was presented, and its experimental verification was performed. Then, the influence of various degrees of damage to the stator winding on the waveforms of the motor state variables in an open scalar control structure and in a closed field-oriented control structure was analyzed. Based on the analysis of phase currents and rotational speed of the motor as well as the influence of the PMSM motor operating conditions, the basic techniques of extracting the symptoms of stator winding inter-turn short-circuits were analyzed, and the failure indicators were developed, which enable simple diagnostics of the stator winding.

The decoupled vector-control of PMSM based on nonlinear multi-input multi-output decoupling ADRC

The Permanent Magnet Synchronous Motor (PMSM) is widely used in many fields. Aiming at nonlinearity, strong coupling and uncertainty of the PMSM, this paper proposes a nonlinear multi-input multi-output (MIMO) decoupling PMSM algorithm based on Active Disturbance Rejection Control (ADRC). A Lower-Upper matrix factorization approach is introduced to solve a general inverse of the measured time-varying matrix in real-time decoupling ADRC. This PMSM is based on the vector control. First, the PMSM model and vector control are simulated. Then, a first-order ADRC is introduced and used to replace the PID controller in the d and q axis of PMSM respectively. The simulation shows that the replaced system has a smaller fluctuation, faster response and better stability. Finally, the nonlinear MIMO decoupling ADRC and its inverse matrix method are deduced. Then, the decoupling PMSM control based on ADRC is verified. The simulation shows that this system has a better static and dynamic performance, and it conforms to the PMSM characteristics better. All this shows that the nonlinear MIMO decoupling ADRC is a better strategy for the PMSM. The presented algorithm also has advantage in method compared with some recent results of decoupling PMSM control.

Multi‐objective comprehensive teaching algorithm for multi‐objective optimisation design of permanent magnet synchronous motor

The design of permanent magnet synchronous motor (PMSM) is a constrained multi-objective problem, and there are often conflicts between the goals. Aiming at the complexity of PMSM optimisation and a large number of variables, multi-objective optimisation design of PMSM using a multi-objective comprehensive teaching algorithm (MCTA) is proposed. This algorithm is improved based on the teaching–learning-based optimisation algorithm and can better adapt to large-scale sample space and multivariate optimisation. Also, based on a magnetic equivalent circuit, a static electromagnetic model of the radial magnetic field PMSM is derived, and the design requirements and goals of the PMSM are analysed. In the two PMSM multi-objective optimisation calculation processes, the optimal solution was calculated based on the composite fitness function and Pareto front, respectively. The results of the MCTA were compared with other natural optimisation algorithms. Finally, according to the optimisation results of two sets of experiments, the correctness of the optimal solution of the algorithm was verified by finite element analysis and actual measurement of the prototype. The analysis of results shows the effectiveness, simplicity, and inspiration of MCTA in the PMSM multi-objective structure optimisation design.

Support-Vector Machine Approach for Robust Fault Diagnosis of Electric Vehicle Permanent Magnet Synchronous Motor

Permanent magnet synchronous motor (PMSM) is a leading technology for electric vehicles (EVs) and other high-performance industrial applications. These challenging applications demand robust fault diagnosis schemes, but conventional strategies based on models, system knowledge, and signal transformation have limitations that degrade the agility of diagnosing faults. These methods require extremely detailed design and consideration to remain robust against noise and disturbances in the actual application. Recent advancements in artificial intelligence and machine learning have proven to be promising next-generation solutions for fault diagnosis. In this paper, a support-vector machine (SVM) utilizing sparse representation is developed to perform sensor fault diagnosis of a PMSM. A simulation model of the pertinent PMSM drive system for automotive applications is used to generate a set of labelled training example sets that the SVM uses to determine margins between normal and faulty operating conditions. The PMSM model includes input as a torque reference profile and disturbance as a constant road grade, against both of which faults must be detectable. Even with limited training, the SVM classifier developed in this paper is capable of diagnosing faults with a high degree of accuracy, suggesting that such methods are feasible for the demanding fault diagnosis challenge in PMSM.

Characteristic Analysis and Predictive Torque Control of the Modular Three-Phase PMSM for Low-Voltage High Power Application

In this study, a novel modular three-phase permanent magnet synchronous motor (PMSM) is proposed for low-voltage high power applications. The proposed modular three-phase PMSM has an independent segregated three-phase winding configuration, facilitating the implementation of the control algorithm. Firstly, on the basis of the electromagnetic properties, the mathematical model of the modular three-phase PMSM is established, considering the asymmetrical mutual inductances investigated by finite element analysis (FEA). Then, the predictive torque control (PTC) method combining the inductance characteristics of modular three-phase PMSM is developed, and excellent performance is obtained by adjusting the stator flux and torque. Finally, simulation and experiment are performed, and the results show that the proposed novel modular three-phase PMSM with the PTC method exhibits excellent control performance, and small stator current total harmonic distortion (THD).

A Novel Model-Free Intelligent Proportional-Integral Supertwisting Nonlinear Fractional-Order Sliding Mode Control of PMSM Speed Regulation System

This study proposes a novel model-free intelligent proportional-integral supertwisting nonlinear fractional-order sliding mode control (MF-iPI-ST-NLFOSMC) strategy for permanent magnet synchronous motor (PMSM) speed regulation system. First of all, a model independent intelligent proportional-integral (iPI) control strategy is adopted for the motor speed regulation system. Next, a novel model-free supertwisting nonlinear fractional-order sliding mode control (ST-NLFOSMC) strategy is constructed based on the ultralocal model of PMSM. Meanwhile, a linear extended state observer (LESO) is used to estimate the unknown terms of the ultralocal model. Then, this study presents the novel hybrid MF-iPI-ST-NLFOSMC strategy which integrates the model-free ST-NLFOSMC strategy, the model-free iPI control strategy, and the LESO. Moreover, the stability of the proposed hybrid MF-iPI-ST-NLFOSMC strategy is proved by the Lyapunov stability theorem and fractional-order theory. Finally, the simulations and comparison results verify that the hybrid MF-iPI-ST-NLFOSMC strategy proposed in this paper has better performance than the other model-free controllers in terms of the static characteristic, dynamic characteristic, and robustness.

Modelling and analysis of the effects of cutting of core laminations in electric machines

Experimental measurements of electrical machine performances reveal a significant difference between simulations and measurements. It is known that the manufacturing processes of machines, particularly, the cutting of core laminations adversely affects material properties. The current techniques for considering the effects of cutting lack accuracy, computation efficiency and applicability for finite element (FE) software. This study proposes a method to model the variation in the magnetic properties of electrical steel due to cutting based on a non-equal multi-slice technique. In this work, the method has been validated and implemented on a FE permanent magnet synchronous motor model. Also, the proposed method has been compared with the single-layer method.

Model-Free Hybrid Control with Intelligent Proportional Integral and Super-Twisting Sliding Mode Control of PMSM Drives

In this research, based on the ultra-local model, a novel compound model-free control strategy with an intelligent Proportional-Integral and super-twisting Sliding Mode Control (MFiPISTSMC) strategy for permanent magnet synchronous motor (PMSM) drives is proposed. Firstly, an intelligent Proportional-Integral (iPI) control strategy is designed for motor speed regulation. Secondly, a super-twisting Sliding Mode Control (STSMC) strategy is constructed based on the ultra-local model of PMSM. At the same time, the unknown term of the ultra-local model of PMSM is estimated by a Linear Extended State Observer (LESO). The stability of the compound MFiPISTSMC strategy is proved by the Lyapunov stability theorem. As a result of the compound MFiPISTSMC strategy integrating the STSMC strategy, the iPI control strategy and the LESO is proposed to have excellent performance. Finally, the static characteristic, dynamic characteristic and robustness of the novel compound MFiPISTSMC strategy are verified by simulation and experimental results.

Luenberger Position Observer Based on Deadbeat-Current Predictive Control for Sensorless PMSM

Two problems can cause control performance degradation on permanent magnet synchronous motor (PMSM) systems, namely, fluctuation of PMSM parameters and the time delay between current sampling and command value update. In order to reduce the influence of these problems, a new current-predictive control strategy is proposed in this article for medium- and high-speed PMSM. This strategy is based on the discrete mathematical model of PMSM. This new control strategy consists of two main steps: First, an integrator is applied to calculate current compensation value; second, the predictive current value is obtained through deadbeat-current predictive method. The stability of predictive control system is also proved in the article. With this deadbeat-current predictive control scheme, the real current can reach the desired value within one control-step. Based on this new current control method, Luenberger observer and phase-locked loop position tracker is applied in this article. Experimental results for 0.4 kW surface-mounted PMSM confirm the validity and excellent performance for parameters fluctuation of new current predictive control.

Design Space Analysis Including Experimental Verification for an Electrical Machine Based on a Parametric and Functional IPMSM Model

In this article, based on the recommendations of systems engineering, a procedure for generating a parametric and functional interior permanent magnet synchronous motor model is proposed. The developed and applied current-adaptive procedure allows to transform the functional relationships of the motor topologies into an advanced model with higher prediction accuracy over a considerably extended definition range. Compared to the state of the art, the proposed approach allows a more general and flexible analysis of electrical machines. A study demonstrates through multicriteria optimization that the design space has a major influence on efficiency and must be taken into account when defining architectures. Existing approaches are typically limited by model constraints to local investigations, the proposed method has advantages over these procedures. In addition to the analysis and optimization of systems, it is shown that the proposed procedure supports decision-making processes due to the integrated system focus. Potentials are identified and discussed in terms of efficiency-enhancing opportunities. A prototype was built and measured to verify the results.

PMSM Vector Control Based on Fuzzy PID Controller

The traditional motor speed control method is to use PID controller to adjust it, but the accuracy and anti-interference ability of its control effect are difficult to be guaranteed. Because the side effects of its error selection, “weighted sum” strategy may not be the best and integral feedback. In the case of motor load variation, this paper presents a control method based on Fuzzy PID controller. The simulation model of PMSM (permanent-magnet synchronous motor) is built in Matlab / Simulink. The simulation experiment shows that the fuzzy PID controller has higher accuracy and anti-interference ability than the classical double closed-loop PID vector control in PMSM vector control.

Electromagnetic and Thermal Analysis of a Permanent Magnet Motor Considering the Effect of Articulated Robot Link

This paper presents the electromagnetic and thermal characteristics of a permanent magnet synchronous motor (PMSM) in a joint actuator which is used for articulated robot application. In an attempt to design a compact PMSM for the articulated robot, robot link should be taken into consideration during the motor design process as it can reduce the temperature distribution on motor, thus reducing the volume of the motor. A lumped-parameter thermal model of PMSM with and without a link is proposed considering the core loss, copper loss, and mechanical loss as heat sources. The electromagnetic and thermal analysis results are well confirmed by the experiment in a 400 W 20-pole/24-slot PMSM. The experiment results show that the robot link helps to reduce the motor end-winding temperature by about 40%, and this leads to an increase in power density of the motor.

Adaptive Torque Ripple Suppression Methods of Three-Phase PMSM During Single-Phase Open-Circuit Fault-Tolerant Operation

This article proposes adaptive torque ripple suppression methods by utilizing resonant controllers during the single-phase open-circuit fault-tolerant operation of a three-phase star-connected permanent magnet synchronous motor (PMSM). A zero-sequence current is supposed to be injected to maintain the postfault torque the same as the prefault torque. According to the postfault PMSM model, a feedforward voltage compensation can be adopted to inject the zero-sequence currents and, hence, attenuate the postfault speed and torque ripples in the vector control framework. However, the feedforward voltage compensation, which is related to the phase resistance and leakage inductance, may be inaccurate due to the temperature variation, etc., and hence, an unexpected second harmonic torque ripple still exists. In this article, the adaptive suppression methods of the second harmonic torque are proposed. The first one constructs a novel closed current loop of the zero-axis and the second one modifies the controllers in the dq -axis current loop. These methods are based on the resonant controllers, which can adaptively compensate for the voltage needed for the zero-sequence current injection. Experiments are implemented based on a four-leg inverter and the performance comparison between the feedforward compensation and adaptive compensation validates that the proposed methods can effectively suppress the postfault torque and speed ripple.

Artificial neural network aided real-time simulation of electric traction system

Real-time simulation-based validation plays an essential role in the early stage development of electric traction systems. The high-fidelity real-time simulation relies on the accurate modeling of the power electronics and motors and usually represents the nonlinear characteristics as many as possible while obeying the computing time constraints. In this paper, an artificial neural network (ANN) aided modeling approach is proposed for the field-programmable gate array (FPGA)-based real-time simulation to deal with the nonlinearities in permanent magnet synchronous motor (PMSM) and its drive. With the help of the ANN, the switches power losses, and the PMSM nonlinear flux linkage and electromagnetic torque can be modeled. An electro-thermal model of inverters and the finite element analysis (FEA)-based PMSM model are thus enabled in the FPGA-based real-time simulation, which can significantly improve the performances of the real-time simulation-based test bench. A two-level inverter fed PMSM drive system is modeled using the ANN-aided modeling approach and simulated in the National Instrument PXIe FlexRIO FPGA real-time system. The accuracy and effectiveness of the proposed approaches are tested and validated by comparing the results with the offline simulation tools.

Robust Multiobjective Model Predictive Control with Computation Delay Compensation for Electric Vehicle Applications Using PMSM with Multilevel Inverter

The Three-Level Neutral-Point-Clamped (3L-NPC) inverter fed Permanent Magnet Synchronous Motor (PMSM) drive is an attractive configuration for high performance Electric Vehicle (EV) applications. For such configuration, due to their high performances, the Finite-Control-Set Model Predictive Control (FCS-MPC) is a very attractive control solution. The FCS-MPC scheme is based on the prediction of the future behavior of the controlled variables using the dynamic model of PMSM and the discrete nature of the 3L-NPC inverter. However, the parametric uncertainties and time-varying parameters affect the FCS-MPC algorithm performances. In this paper, robust FCS-MPC controls based on “dynamic error correction” (DEC) and “modified revised prediction” (MRP) are proposed to improve the FCS-MPC robustness without affecting the controller performances and complexity. The proposed strategies are improved also by multiobjective (MO) algorithm optimization and computation delay compensation. The simulation results included prove the performance in robustness and efficiency of the proposed robust FCS-MPC-DEC.

Stabilisation of event‐triggered‐based neural network control system and its application to wind power generation systems

This study addresses the event-triggered (ET)-based stabilisation problem of neural-network-based control system (NNBCS) and illustrates the direct application to wind power generation system. In this regard, the novel ET-based controller algorithm is designed for NNBCS instead of sampled data controller (sampling will be initiated at a fixed rate regardless whether it is required or not) which reduces the computation complexity by avoiding the unnecessary details over the transmission. The novel stability and stabilisation conditions are expressed in terms of linear matrix inequalities that are derived through constructing the time-dependent Lyapunov functional candidate. For deepening the knowledge in the outcomes of the proposed conditions, the study numerically evaluates the dynamic models such as variable-speed wind turbine drive system, permanent magnet synchronous motors model and traditional inverted pendulum model and validates the effectiveness of the proposed controller scheme. Finally, a comparison result shows the superiority of the derived conditions.

Vehicle Drive Control Using Fuzzy Based PI Speed Controller

This paper proposes a fuzzy PI speed controller which is used in electric vehicle’s drive control system insensitive to parameter change and disturbance, using fuzzy control theory. A permanent magnet synchronous motor (PMSM) is modelled mathematically in d-q reference frame. In this paper, a sliding model and fuzzy control theory are used to simulate the PMSM models using fuzzy control theory. Simulink software is used to analyze and simulate the simulation models which show that the proposed PI control based on fuzzy logic will have better anti-interference, better dynamic performance, and faster dynamic response speed when compared with the sliding motor control. Hence the proposed methodology is considered to be the ideal control method with a predefined vector control reference value for the electric vehicle’s motor.

Current harmonic selection for torque ripple suppression based on analytical torque model of PMSMs

For permanent magnet synchronous motors (PMSMs) without sufficient design optimization, it is necessary to inject current harmonics to suppress torque ripple. Due to the influence of magnetic saturation, the current harmonics for torque ripple suppression changes with the electrical loads. Based on an existing analytical torque model of a PMSM considering spatial harmonics and magnetic saturation, this paper established a torque harmonic coupling model through a Taylor expansion and a harmonic balance method. Then, based on the torque harmonic coupling model, current harmonic selection for torque ripple minimization was proposed. The validity of the torque harmonic coupling model and current harmonic selection was verified by a finite element analysis (FEA) and experiments on a laboratory PMSM drive system.

Design Optimization and Experimental Verification of Permanent Magnet Synchronous Motor Used in Electric Compressors in Electric Vehicles

In this study, a shape design optimization method is proposed to improve the efficiency of a 3 kW permanent magnet synchronous motor (PMSM) used in an electric compressor intended for use in an electric vehicle. The proposed method improves the efficiency performance of the electric compressor by improving the torque characteristics of the initial PMSM model. The dimensions of the rotor were set as the design variables and were chosen to maximize efficiency and reduce cogging torque. During the determination of the design points with conventional Latin hypercube design, the experimental points may be closely related to each other. Therefore, the optimal Latin hypercube design was used to optimally distribute the experimental points evenly and improve the space filling characteristics. The Kriging model was used as an interpolation model to predict the optimal values of the design variables. This allowed the formulation of more accurate prediction models with multiple design variables, complex reactions, or nonlinearities. A genetic algorithm was used to identify the optimal solution for the design variables. It was used to satisfy the objective function and to determine the optimal design variables based on established constraints. The optimal design results obtained based on the proposed shape optimization method were confirmed by finite element analyses. For practical verification, the optimal model of the prototype PMSM of an electric compressor was manufactured, and a 1.5% improvement in its efficiency performance was confirmed based on an experimental dynamometer test.