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When the interior permanent magnet synchronous motor (IPMSM) is running, there are abundant harmonics in the stator current. In order to achieve the suppression of current harmonics, the current harmonic extraction method and current harmonic controller are studied in this paper. Firstly, a simple and accurate method for extracting current harmonics is proposed by means of multiple synchronous rotating frame transformation (MSRFT). Secondly, an improved deadbeat predictive control (IDPC) based current harmonic controller is designed after analyzing the advantages and disadvantages of traditional current harmonic controllers. Thirdly, IDPC-based current harmonic suppression strategy is proposed by combining the proposed current harmonic extraction method and the proposed current harmonic controller. The proposed strategy can still effectively achieve current harmonic suppression when the motor runs at low speed, medium speed and high speed and the controller parameters are mismatched with the motor parameters. Finally, the feasibility and effectiveness of the proposed strategy are verified by simulation and experiments.

This article presents a static-errorless rotor position estimation method based on the linear extended state observer (LESO) for interior permanent magnet synchronous motor (IPMSM) drives. Two second-order LESOs are utilized to estimate the α-β axis back-EMFs. A third-order LESO is incorporated into the quadrature phase-locked loop (QPLL) to achieve a high robustness of position tracking against external disturbance. In addition, considering that the nonideal back-EMF will bring DC and harmonic fluctuation errors to the estimated position, an enhanced LESO-based QPLL with static-errorless rotor position estimation is proposed. On the one hand, the DC position esti mation error caused by the phase lag of the back-EMF estimator is analyzed and compensated. On the other hand, to suppress the position harmonic fluctuations induced from the nonsinusoidal back-EMFs, a second-order generalize integrator (SOGI) is embedded in the feedforward path of the LESO-based QPLL. The experimental results on the 1.0 kW IPMSM drive platform show that, compared to the conventional method, the proposed method can achieve better position estimation performance both in steady-state operation and in transient-state operation.

The highly efficient Interior Permanent Magnet Synchronous Motor (IPMSM) is ubiquitous choice in Electric Vehicles (EVs) for today’s automotive industry. IPMSM control requires accurate knowledge of an immeasurable critical Permanent Magnet (PM) flux linkage parameter. The PM flux linkage is highly influenced by operating temperature which results in torque derating and hence power loss, unable to meet road loads and reduced life span of electrified powertrain in EVs. In this paper, novel virtual sensing scheme for estimating PM flux linkage through measured stator currents is designed for an IPMSM centric electrified powertrain. The proposed design is based on a Uniform Robust Exact Differentiator (URED) centric Super Twisting Algorithm (STA), which ensures robustness and finite-time convergence of the time derivative of the quadrature axis stator current of IPMSM. Moreover, URED is able to eliminate chattering without sacrificing robustness and precision. The proposed design detects variation in PM flux linkage due to change in operating temperature and hence is also able to establish characteristics of fault detection. The effectiveness and accuracy in different operating environments of the proposed scheme for nonlinear mathematical IPMSM model with complex EV dynamics are verified thorough extensive simulation experiments using MATLAB/Simulink.

This article presents an improved linear active disturbance rejection control (LADRC) method for interior permanent magnet synchronous motor (IPMSM) drives. The proposed method adopts a dual LADRC structure. The outer LADRC-based speed regulator adopts position feedback instead of speed feedback so that the low-pass filter for speed calculation can be eliminated. The inner LADRC-based current regulator incorporates a maximum torque per ampere (MTPA) operation scheme to improve the torque output capacity and the efficiency of the motor. In addition, considering the variation of load inertia in real applications, a systematic modelling and analysis on the effect of inertia mismatch is presented. To enhance the robustness of the drive system to inertia mismatch, an inertia identification method is proposed, in which the inertia information is extracted from the estimated disturbance of speed loop LESO. Finally, the effectiveness of the proposed method is verified on a 1.0-kW IPMSM drive platform.

An online parameter estimation methodology using the d-axis current injection, which can estimate the distorted voltage of the current-controlled voltage source inverter (CCVSI), the varying dq-axis inductances, and the rotor flux, is proposed in this study for interior permanent magnet synchronous motor (IPMSM) drives in the constant torque region. First, a d-axis current injection-based parameter estimation methodology considering the nonlinearity of a CCVSI is proposed. Then, during current injection, a simple linear model is developed to model the cross- and self-saturation of the dq-axis inductances. Since the d-axis unsaturated inductance is difficult to obtain by merely using the recursive least square (RLS) method, a novel tuning method for the d-axis unsaturated inductance is proposed by using the theory of the maximum torque per ampere (MTPA) with the combination of the RLS method. Moreover, to improve the bandwidth of the current loop, an intelligent proportional-integral-derivative (PID) neural network controller with improved online learning algorithm is adopted to replace the traditional PI controller. The estimated the dq-axis inductances and the rotor flux are adopted in the decoupled control of the current loops. Finally, the experimental results at various operating conditions of the IPMSM in the constant torque region are given.

The speed response of the interior permanent magnet synchronous motor (IPMSM) drive at low speeds was analyzed. To eliminate the effect of external disturbance or parameter uncertainty, a nonlinear speed control loop was designed based on the sliding-mode exponential reaching law, which reduces chatter, which is the major drawback of the constant reaching law sliding-mode control technique. The proposed nonlinear speed control eliminates speed ripples at low speed under load disturbance. The problem of speed convergence at low speed is caused by electromagnetic torque ripples, which cause shaft speed oscillations that affect drive performance. The main objective of the proposed method is to change the traditional IPMSM control design by compensating with an appropriate signal along the reference current and across the output of the speed control loop. To optimize the speed tracking performance during disturbances or parametric variations, a nonlinear speed control scheme is designed that can vigorously adapt to the change in the controlled system. The comparative analysis shows that the method provides excellent transient performance (e.g., fast convergence response, less overshoot, and fast settling time) and standstill performance (e.g., reduced steady-state error) compared with conventional control methods at low speed under varying load conditions. The method is easy to implement and does not require additional computational cost. To demonstrate the effectiveness and feasibility of the design approach, a numerical analysis was conducted, and the control scheme was verified using MATLAB/Simulink considering various operating conditions.

This paper presents a multi-objective optimal rotor design for an interior permanent magnet synchronous motor (IPMSM) based on finite element analysis. Due to the importance of torque characteristic in electromagnetic design of IPMSMs, the main efforts of this study are focused on finding a proper trade-off for its torque profile challenges. In this regard, in order to attain high average torque and low torque ripple, the influence of several key factors, such as the permanent magnet (PM) arrangements, PM positions and PM sizes, are examined. Subsequently, according to the outcomes of the performed sensitivity analysis, the appropriate variation interval of the parameters as well as their initial values is determined. Employing such a deterministic optimization algorithm, which does not need large sample points, minimizes the finite element computational cost and leads to accelerate the convergence process. The two-dimensional finite element model (FEM) of an IPMSM is used to perform a sensitivity analysis and establish a multi-objective FEM-based optimization.

In an interior permanent magnet synchronous motor, an adhesive such as bond is generally injected into the magnet tolerance to prevent vibration of the permanent magnet within the insertion space. In this case, a disadvantage is that the magnet tolerance does not contribute to the performance. In this paper, ferrofluid is inserted to improve the torque density, utilizing the magnet tolerance. When inserting ferrofluid into the magnet tolerance, it is important to fix the magnet because conventional adhesives are not used, and it is important that the ferrofluid does not act as a leakage path within the insertion space. In this study, a new rotor configuration using a plastic barrier that satisfies these considerations was introduced. The analysis was conducted through finite element analysis (FEA), and this technique was verified by comparing the simulation results and the experimental results through a dynamo test. It was confirmed that the no-load back electromotive force in the final model increased through ferrofluid injection.

An interior permanent magnet synchronous motor (IPMSM) drive system with machine learning-based maximum torque per ampere (MTPA) as well as flux-weakening (FW) control was developed and is presented in this study. Since the control performance of IPMSM varies significantly due to the temperature variation and magnetic saturation, a machine learning-based MTPA control using a Petri probabilistic fuzzy neural network with an asymmetric membership function (PPFNN-AMF) was developed. First, the d-axis current command, which can achieve the MTPA control of the IPMSM, is derived. Then, the difference value of the dq-axis inductance of the IPMSM is obtained by the PPFNN-AMF and substituted into the d-axis current command of the MTPA to alleviate the saturation effect in the constant torque region. Moreover, a voltage control loop, which can limit the inverter output voltage to the maximum output voltage of the inverter at high-speed, is designed for the FW control in the constant power region. In addition, an adaptive complementary sliding mode (ACSM) speed controller is developed to improve the transient response of the speed control. Finally, some experimental results are given to demonstrate the validity of the proposed high-performance control strategies.

In this paper, the Novel Immune Algorithm (NIA) is proposed for an optimal design of electrical machines. By coupling the conventional Immune Algorithm and Steepest Descent Method, the NIA can perform fast and exact convergence to both global solutions and local solutions. Specifically, the concept of an antibody radius is newly introduced to improve the ability to navigate full areas effectively and to find new peaks by excluding already searched areas. The validity of the NIA is confirmed by mathematical test functions with complex objective function regions. The NIA is applied to an optimal design of an interior permanent magnet synchronous motor for fuel cell electric vehicles and to derive an optimum design with diminished torque ripple.

In the applications of rail transit and electric vehicles, sensorless control of interior permanent magnet synchronous motor (IPMSM) usually uses high frequency (HF) signal injection in low speed or zero speed. Rotating HF signal injection based on the stationary reference frame can identify the rotor position, but its accuracy is easily affected by various nonlinearities of the control system and stator resistance. In this paper, the causes of rotor position estimation deviation are analyzed and deduced in detail. It is proposed that the rotor position estimation deviation can be divided into high frequency phase deviation (HFPD) and stator resistance phase deviation. On the basis of these analyses, a novel sensorless rotor position estimation strategy for IPMSM is proposed. This strategy can theoretically eliminate the HF phase deviation caused by the nonlinearity of the control system and reduce the phase deviation caused by the stator resistance. Although the factors that cause the estimation deviation of rotor position may change with the time and the operation status of the motor, the proposed strategy has the characteristics of online calculation and real-time compensation, which can improve the accuracy of the estimated rotor position. In addition, this paper provides a detailed theoretical derivation of resolving rotor position considering stator resistance and HF phase deviation. Finally, the result analysis on an IPMSM demonstrate the correctness of the theoretical analysis and the effectiveness of the strategy.

The scheme based on direct torque and flux control (DTFC) as well as active flux is a good choice for the interior permanent magnet synchronous motor (IPMSM) sensorless control. The precision of the stator flux observation is essential for this scheme. However, the performance of traditional observers like pure integrator and the low-pass filter (LPF) is severely deteriorated by disturbances, especially dc offset. Recently, a sliding-mode stator flux observer (SMFO) was proposed to reduce the dc offset in the estimated stator flux. However, it cannot eliminate the dc offset totally and will cause the chattering problem. To solve these problems, a novel super-twisting sliding-mode stator flux observer (STSMFO) is proposed in this paper. Compared with SMFO, STSMFO can reduce the chattering and fully eliminate the dc offset without any amplitude and phase compensation. Then, the precision of the stator flux and rotor position can be greatly improved over a wide speed region. The detailed mathematical analysis has been given for comparing it with another three traditional observers. The numerical simulations and experimental testing with an IPMSM drive platform have been implemented to verify the capability of the proposed sensorless scheme.

This paper deals with efficient operation method for the electromechanical brake (EMB). A three-phase interior permanent magnet synchronous motor (IPMSM) is applied to the EMB operation. A current controller, speed controller, and position controller based on proportional-integral (PI) control are used to drive the IPMSM. Maximum torque per ampere (MTPA) control is applied to the current controller to perform efficient control. For MTPA control, the angle β is calculated from total input current, and the synchronous frame d–q axis current reference is determined by the angle β. The IPMSM is designed and analyzed with finite element analysis (FEA) software and current control is simulated by Matlab/Simulink using a motor model designed by FEA software. The simulation results were verified to compare with experimental results that are input current and clamping force of caliper. In addition, the experimental results showed that the energy consumption is reduced by MTPA.

In traditional sensorless control of the interior permanent magnet synchronous motors (IPMSMs) for medium and high speed domains, a control strategy based on a sliding-mode observer (SMO) and phase-locked loop (PLL) is widely applied. A new strategy for IPMSM sensorless control based on an adaptive super-twisting sliding-mode observer and improved phase-locked loop is proposed in this paper. A super-twisting sliding-mode observer (STO) can eliminate the chattering problem without low-pass filters (LPFs), which is an effective method to obtain the estimated back electromotive forces (EMFs). However, the constant sliding-mode gains in STO may cause instability in the high speed domain and chattering in the low speed domain. The speed-related adaptive gains are proposed to achieve the accurate estimation of the observer in wide speed range and the corresponding stability is proved. When the speed of IPMSM is reversed, the traditional PLL will lose its accuracy, resulting in a position estimation error of 180°. The improved PLL based on a simple strategy for signal reconstruction of back EMF is proposed to ensure that the motor can realize the direction switching of speed stably. The proposed strategy is verified by experimental testing with a 60-kW IPMSM sensorless drive.

Finite-control-set model predictive current control (FCS-MPCC) has been widely investigated in the field of motor control. When the discrete motor prediction model is not obtained accurately, prediction error often occurs, which can result in improper determinations of optimal voltage vectors and can further affect the control performance of motor systems. However, papers evaluating the motor control performance employing FCS-MPCC rarely consider prediction error and its utilization to weaken the influence of inaccurate prediction model. This paper investigates in depth the prediction error caused by three influencing factors from the perspective of model accuracy—discretization method, prediction stepsize, and parameter mismatch. Firstly, the evaluation index, prediction error, is defined and its formulas considering the above three factors are derived based on interior permanent magnet synchronous motor (IPMSM). Then, the theoretical analysis of prediction error is provided. Finally, experimental results of an IPMSM drive system are presented to verify and complement the theoretical analysis. Both the theoretical analysis and experimental results fully elaborate the prediction error, which can offer practical guidelines for the evaluation and improvement of motor control performance, especially for FCS-MPCC in IPMSM applications.

This paper presents a new adaptive torque estimation algorithm for an interior permanent magnet synchronous motor (IPMSM) with parameter variations and cross-coupling between d- and q-axis dynamics. All cross-coupled, time-varying, or uncertain terms that are not part of the nominal flux equations are included in two equivalent mutual inductances, which are described using the equivalent d- and q-axis back electromotive forces (EMFs). The proposed algorithm estimates the equivalent d- and q-axis back EMFs in a recursive and stability-guaranteed manner, in order to compute the equivalent mutual inductances between the d- and q-axes. Then, it provides a more accurate and adaptive torque equation by adding the correction terms obtained from the computed equivalent mutual inductances. Simulations and experiments demonstrate that torque estimation errors are remarkably reduced by capturing and compensating for the inherent cross-coupling effects and parameter variations adaptively, using the proposed algorithm.

This paper presents a novel position and speed estimation method for low-speed sensorless control of interior permanent-magnet synchronous machines (IPMSMs). The parameter design of the position and speed estimator is based on the sampled current rather than the motor electrical parameters. The proposed method not only simplifies the parameter design, it enables the estimator to work normally even in the condition that the electrical parameters are uncertain or varied. The adaptive filters are adopted to extract the desired high frequency current. The structure and corresponding transfer function are analyzed. To address the shortage of insufficient stop-band attenuation, the structure of the adaptive filter is modified to provide suitable bandwidth and stop-band attenuation simultaneously. The effectiveness of the proposed sensorless control strategy has been verified by simulations and experiments.