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Direct torque control has been widely used to control surface-mounted permanent magnet synchronous motors (SPMSMs). To reduce the torque ripple and improve the flux tracking accuracy of SPMSM drives, sliding mode direct torque control (SMDTC) was developed. However, its optimal performance is hardly obtained by trial and error tuning of the control parameters. Hence, a hybrid wolf optimization algorithm (HWOA) is proposed to automatically adjust the controller's parameters of SMDTC for SPMSMs in this article. This algorithm combines the grey wolf optimization algorithm and coyote optimization algorithm. A conversion probability is designed to use them simultaneously. The proposed HWOA holds the advantages of the two algorithms. It converges very fast and can avoid local optimums effectively. Furthermore, a special fitness index with penalty terms is designed to enhance flux tracking accuracy and reduce the torque ripple of SPMSM drives. The superiority of the proposed control method is verified by an experiment. [ABSTRACT FROM AUTHOR]

Switched reluctance motors are a potential competitor to permanent magnet motors and induction motors for various industrial applications. The switched reluctance motors without permanent magnets have gained increased interest due to their simple structure, low cost, robustness, and high fault tolerance. However, the nonlinear characteristics caused by the double salient structure limit its wide applications. Advanced control techniques have been developed to suppress torque ripple, enhance anti-disturbance ability, reduce the switching frequency, expand speed range, and improve efficiency. The control strategies such as direct torque control and control algorithms like sliding mode control are two main approaches considered. Previous work only focuses on one side of control techniques. Thus, based on various control techniques proposed in recent years, this review classifies and summarizes their benefits and shortcomings. In addition, some essential trends in control development are presented and highlighted as future perspectives. [Display omitted] • The fundamentals including the SRM model and power converter are investigated and summarized. • Advanced control strategies of SRM are comprehensively illustrated and discussed. • The control algorithms including state-of-the-art control approaches with multiple control techniques are classified and analyzed. • Future perspectives of this emerging area have been pointed out. [ABSTRACT FROM AUTHOR]

The Taguchi optimization method is an efficient method for motor design optimization. However, it is hard to handle the multiobjective motor optimization problem with big design space for the parameters. To deal with this problem, in this article, a fuzzy method and sequential Taguchi method to optimize an inter permanent magnet synchronous motor (IPMSM) is employed. The fuzzy inference system is introduced to convert the multiple objectives to a single-objective optimization problem. The sequential Taguchi method is used to optimize the structural parameters at multiple levels to improve the accuracy of optimization. After the optimal selection analysis, the best combination of motor structure factors is obtained. By comparing the optimization result of the proposed method with that of the conventional Taguchi optimization method, the effectiveness and superiority of the proposed method are verified. [ABSTRACT FROM AUTHOR]

Permanent magnet synchronous hub motors (PMSHMs) have been gradually introduced into the applications of electric vehicles. On this basis, the six-phase motor has the characteristics of high reliability, high power density and low torque ripple. And its fault tolerance is a large advantage compared with the three-phase motor. The multi-phase permanent magnet synchronous motor is redundant due to the number of phases. When the motor fails, it does not have to stop running, merely adjust its control mode and enter the fault-tolerant compensation control algorithm to resume the operation of the system. The FCS-MPC algorithm can replace the cascade structure in the traditional control and eliminate the modulation module. This makes it have good steady-state performance. The speed response is also improved. It can be combined with multiple control objectives with strong flexibility by simply changing the objective function. The prediction model is compensated. Finally, the experimental results show the effectiveness of this method. [ABSTRACT FROM AUTHOR]

The belt-driven starter/generator (BSG), as a cost-effective solution, has been widely employed in hybrid electric vehicles (HEVs) to improve the stability and reduce the fuel consumption of the vehicles. It can provide more than 10% reduction in CO2. Electrical machine is the heart of the BSG system, which is functioned both as motor and generator. In order to optimize both aspects of motor and generator simultaneously, this paper presents a new multimode optimization method for the switched reluctance machines. First, the general multimode concept and optimization method are presented. The switched reluctance motor and the switched reluctance generator are the two operation modes. The optimization models are established based on motor and generator functions. Sensitivity analysis, surrogate models and genetic algorithms are employed to improve the efficiency of the multimode optimization. Then, a design example of a segmented-rotor switched reluctance machine (SSRM) is investigated. Seven design variables and four driving modes are considered in the multiobjective optimization model. The Kriging model is employed to approximate the finite element model (FEM) in the optimization. Finally, the optimization results are depicted, and an optimal solution is selected. The comparison between the initial and optimal designs shows that the proposed method can improve the foremost performance of the SSRM under all driving modes. [ABSTRACT FROM AUTHOR]

This paper focus on a state feedback controller (SFC)-based optimal control scheme for surface-mounted permanent-magnet synchronous motor (SPMSM) with auto-tuning of controller built on seeker optimization algorithm (SOA). First, based on the nonlinear state-space model of SPMSM, voltage feedforward compensation is used to design a linear SFC. Then in order to guarantee the steady performance in speed and current, integral models considering the errors of rotor speed and current response in d-axis are added in the state space model of SPMSM. Furthermore, by statically decoupling the load torque in the state equation, feedforward compensation is implemented on the load torque to improve the dynamic performance of the controller. The load torque is estimated by using disturbance observer with reasonable parameter selection. Then, with the consideration of the search capacity of seeker optimization algorithm (SOA), it is adopted to acquire matrix coefficient of the presented controller. Furthermore, in order to suppress the speed overshoot, a penalty term is introduced to the fitness index. The performance of the proposed method has been validated experimentally and compared with the conventional method under different conditions. [ABSTRACT FROM AUTHOR]

Model predictive current control (MPCC) is a high-performance control strategy for permanent-magnet synchronous motor (PMSM) drives, with the features of quick response and simple computation. However, the conventional MPCC results in high torque and current ripples. This article proposes an improved MPCC scheme for PMSM drives. In the proposed scheme, the back electromotive force is estimated from the previous stator voltage and current, and it is used to predict the stator current for the next period. To further improve the steady state and dynamic performance, the proposed MPCC selects the optimal voltage vector based on a current track circle instead of a cost function. Compared with the calculation of cost function, the prediction of the current track circle is simple and quick. The proposed MPCC is compared with conventional MPCC and a duty-circle based MPCC by simulation and experiment in the aspect of converter output voltage and sensitivity analysis. Results prove the superiority of the proposed MPCC and its effectiveness in reducing the torque and current ripples of PMSM drives. [ABSTRACT FROM AUTHOR]

The multiobjective optimization design of interior permanent magnet synchronous motors (IPMSMs) is a challenge due to the high dimension and huge computation cost of finite element analysis. This article presents a new multilevel optimization strategy for efficient multiobjective optimization of an IPMSM. To determine the multilevel optimization strategy, Pearson correlation coefficient analysis and cross-factor variance analysis techniques are employed to evaluate the correlations of design parameters and optimization objectives. A three-level optimization structure is obtained for the investigated IPMSM based on the analysis results, and different optimization parameters and objectives are assigned to different levels. To improve the optimization efficiency, the Kriging model is employed to approximate the finite element analysis for the multiobjective optimization in each level. It is found that the proposed method can provide optimal design schemes with a better performance, such as smaller torque ripple and lower power loss for the investigated IPMSM, while the needed computation cost is reduced significantly. Finally, experimental results based on a prototype are provided to validate the effectiveness of the proposed optimization method. The proposed method can be applied for the efficient multiobjective optimization of other electrical machines with high dimensions. [ABSTRACT FROM AUTHOR]

This article investigates the efficient robust design optimization of a five-phase permanent magnet (PM) hub motor for electric vehicles. Besides the requirement of high-performance, like high torque density, low torque ripple and efficiency, fault-tolerant operation capability are also considered in the design optimization. To ensure that the motor performance is not sensitive to the variations of manufacturing tolerances, robust design optimization is employed to the investigated motor. To improve the fault tolerant capability of the motor, the motor performances under fault operation are also considered in the optimization. A Fuzzy-based sequential Taguchi robust optimization method is proposed to improve the comprehensive performance and save computing time. The proposed method is efficient because it holds the advantages of Taguchi method, fuzzy theory, and sequential optimization strategy. The motor performance is improved significantly by using the proposed method. Experimental results verify the accuracy of the model used in this study. [ABSTRACT FROM AUTHOR]

This study proposes an outer‐rotor permanent magnet synchronous hub motor (PMSHM) with similar pole and slot numbers for an electric vehicle (EV). First, the design rule of the motor size is introduced. Second, the appropriate numbers of motor poles and slots are selected to improve the torque density and save the radial space of the motor. Meanwhile, the selected poles and slots numbers can ensure that the high‐efficiency area of the motor meets the requirements of the test EV. Moreover, different winding arrangements are studied to select the appropriate winding arrangement method for the motor. Then, the key dimensions of the stator are optimised to improve the torque output and reduce the core loss of the motor by using the finite element analysis. The results indicate that the proposed outer‐rotor PMSHM with similar pole and slot numbers and double‐layer short distance concentrated slot winding exhibits high‐efficiency and high‐torque output. Finally, the predicted performance of the proposed PMSHM is verified by experiments on a prototype. Furthermore, the vehicle test is also carried out, and the results show that the prototype maintains high efficiency in the main working area of the EV. [ABSTRACT FROM AUTHOR]

This article presents a novel method for the sensorless control of interior permanent-magnet synchronous motors. An iterative search strategy based on dichotomy is proposed to provide a finite number of rotor position angles with good accuracy. These position angles are used to calculate the back electromotive force (EMF) in d-axis. The optimal rotor position angle is the one that yields a back EMF minimizing the defined cost function. With the increase of the iterations, the accuracy of rotor position angle increases geometrically. To effectively extract the back EMF signal under the low-speed condition, the high-frequency signal injection method is used to realize the low-speed operation of the motor. A hybrid control strategy is adopted to achieve the smooth switching from the low-speed to high-speed. The performance of the proposed method has been validated experimentally and compared with that of the conventional phase locked loop under different conditions. [ABSTRACT FROM AUTHOR]

This paper presents an optimal control strategy for a permanent-magnet synchronous hub motor (PMSHM) drive using the state feedback control method plus the gray wolf optimization (GWO) algorithm. First, the linearized PMSHM mathematical model is obtained by voltage feedforward compensation. Second, to acquire satisfactory dynamics of speed response and zero d-axis current, the discretized state-space model of the PMSHM is augmented with the integral of rotor speed error and integral of d-axis current error. Then, the GWO algorithm is employed to acquire the weighting matrices Q and R in linear quadratic regulator optimization process. Moreover, a penalty term is introduced to the fitness index to suppress overshoots effectively. Finally, comparisons among the GWO-based state feedback controller (SFC) with and without the penalty term, the conventional SFC, and the genetic algorithm enhanced proportional–integral controllers are conducted in both simulations and experiments. The comparison results show the superiority of the proposed SFC with the penalty term in fast response. [ABSTRACT FROM AUTHOR]

This work presents the analysis, design and optimization of a permanent magnet synchronous motor (PMSM) for an electric vehicle (EV) used for campus patrol with a specific drive cycle. Firstly, based on the collected data like the parameters and speed from a test EV on the campus road, the dynamic calculation of the EV is conducted to decide the rated power and speed range of the drive PMSM. Secondly, according to these requirements, an initial design and some basic design parameters are obtained. Thirdly, optimization process is implemented to improve the performance of the designed PMSM. The permanent magnet (PM) structure, airgap length and stator core geometry are optimized respectively in this step. Different optimization processes are proposed to meet multiple optimization objectives simultaneously. Based on the finite element analysis (FEA) method, it is found that the harmonic of the optimized PMSM is lower than that of the initial design, and the torque ripple is reduced by 24%. The effectiveness of optimization on the core loss and PM eddy loss is validated and the temperature rise is suppressed effectively. Finally, a prototype is fabricated for the optimized PMSM and an experimental platform is developed. The test results verify that the optimized PMSM meets the requirements of the specific campus patrol EV well. [ABSTRACT FROM AUTHOR]

This paper presents a compound propulsion system with a high-speed permanent-magnet synchronous motor (PMSM) and two in-wheel motors for electric vehicles (EVs). In this paper, the longitudinal dynamic model of EVs is first presented. Then traction distribution ratio $\alpha $ is introduced to express the traction distribution between the front and the rear axles. Moreover, the function of power consumption concerned with the traction distribution ratio $\alpha $ is established. Therefore, the $\alpha $ that minimizes the power consumption function is selected as the optimal traction distribution ratio. To improve the performance of motor controllers, the model predictive torque control (MPTC) method is employed for high-speed and in-wheel motor drives. Experimental comparison with field-oriented control (FOC) shows the advantages of MPTC in dynamic response. Finally, experimental comparisons and hardware-in-loop (HiL) tests are presented to verify the MPTC method and the proposed energy allocation method, respectively. [ABSTRACT FROM AUTHOR]

Due to the advantages such as high energy density, high power density, high cyclic life, and environmentally friendly, the flywheel battery has the potential to solve the problem of energy storage. In order to improve the torque density and suspension performance of bearingless synchronous permanent magnet (PM) synchronous motors (BPMSMs), a novel rotor structure with V-shaped PMs is designed in this paper. Furthermore, the interior BPMSM (IBPMSM) with V-shaped PM which used for flywheel batteries of electric vehicles is researched in detail. Especially, the influence of geometrical parameters of V-shaped PM on suspension force and electromagnetic torque is investigated. Moreover, the corresponding static electrical magnetic characteristics including inductances and electromagnetic torque are also studied. The finite-element method is employed to evaluate the theoretical analysis of the proposed IBPMSM. In addition, the optimized motor is validated to have good suspension performance by some experiments. [ABSTRACT FROM AUTHOR]

To effectively enhance the control accuracy and dynamic performance of a bearingless permanent magnet synchronous motor (BPMSM), this paper presents a novel control scheme combining the inverse system method and the internal model control. By cascading the inverse model of the BPMSM with the original BPMSM system, a decoupling pseudo-linear system is constituted. Moreover, in order to improve the robustness of the whole system and reject the influence of the unmodeled dynamics and system noise to the decoupling control accuracy, the internal model control scheme is employed for the pseudo-linear system to design extra closed-loop controllers. Consequently, the proposed decoupling control scheme incorporates the advantages of both the inverse system method and the internal model control. The effectiveness of the proposed control scheme is verified by experimental results at various operations. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel decoupling scheme for a bearingless permanent-magnet synchronous motor (BPMSM) to achieve fast-response and high precision performances and to guarantee the system robustness to the external disturbance and parameter uncertainty. The proposed control scheme incorporates the neural network inverse (NNI) method and 2-degree-of-freedom (DOF) internal model controllers. By introducing the NNI systems into the original BPMSM system, a decoupled pseudo-linear system can be constituted. Additionally, based on the characteristics of the pseudo-linear system, the 2-DOF internal model control theory is utilized to design extra controllers to improve the robustness of the whole system. Consequently, the proposed control scheme can effectively improve the static and dynamic performances of the BPMSM system, as well as adjust the tracking and disturbance rejection performances independently. The effectiveness of the proposed scheme has been verified by both simulation and experimental results. [ABSTRACT FROM PUBLISHER]

Bearingless permanent-magnet (PM) synchronous motors (BPMSMs) are a new type of machines combining the characteristics of conventional PM synchronous motors and magnetic bearings. With ever-increasing concerns on applications, such as high-speed electric machines, canned pumps, high-speed-precision mechanical processing, aeronautics and astronautics, flywheel energy storage, life science, vacuum technique, and semiconductor and biotechnology industries, there is a fast growing interest in BPMSMs. In this paper, after a general description of the generation principle of radial suspension forces, a comprehensive overview of BPMSMs is presented, with particular emphasis on mathematical models, motor topologies, and control strategies. Moreover, several possible development trends of the BPMSM are also discussed. [ABSTRACT FROM PUBLISHER]

In this paper, the inductance characteristics of a 2-pole surface-mounted bearingless permanent magnet synchronous motor (BPMSM) with 4-pole suspension force windings are investigated based on theoretical analysis and experimental investigation. Firstly, the winding configuration and operation principle of the BPMSM are presented. Secondly, the static and incremental inductances are defined. Then, based on the finite element (FE) analysis method, the static inductances of torque windings and suspension force windings in stator static coordinate reference are researched. In addition, the d- and q-axes static inductances in rotor revolving coordinate reference are obtained by 3/2 Transformation. Furthermore, taking the torque windings as an example, the incremental inductance of torque windings is studied, and the validity of the theory is confirmed by comparing the static and incremental inductances. Finally, the experimental study is carried out to verify the discussed numerical analysis. The results are helpful to measure parameters and build the mathematic model of BPMSMs, and lay a foundation for the further research on BPMSMs. [ABSTRACT FROM AUTHOR]

In this paper, a compensation prediction model current control based on the extended state observer is proposed for the static errors and oscillations of output current caused by parameter disturbance deviations. First, the parameter change part is regarded as the total disturbance, and a mathematical model of the motor is established. Then, virtual voltage vectors are introduced to eliminate the effect of leakage inductance mismatch and to further suppress current harmonics. Furthermore, the state observer is designed to estimate disturbances and currents. The estimated values from the state observer are used as the feedforward part to cancel disturbances in the system. Then, the stability of the proposed algorithm is verified by a pole configuration analysis. Finally, the effectiveness of the proposed method is confirmed by a six-phase permanent magnet synchronous hub motor experiment. [ABSTRACT FROM AUTHOR]

The square-wave voltage signal injection into the estimated d-axis is an effective and novel sensorless control scheme for interior permanent magnet synchronous motor at zero speed and low speed. However, the notable acoustic and electromagnetic noises induced by the conventional fixed-frequency signal injection are very harsh and shrill to hear, which limits the practical application. Aiming at reducing unnecessary acoustic noise, a novel random voltage injection method based on the Markov chain (MC) is proposed in this article. Two high-frequency square-wave voltages with different types of frequencies and amplitudes are randomly injected into the estimated d-axis by MC rules, and this way has spread spectrum characteristics, which can effectively reduce the unnecessary noise. In addition, a new signal demodulation compensation method considering the digital delay effect in high-frequency signals is proposed, which effectively reduces the position and velocity estimation errors. Then, the power spectra density of the induced high-frequency currents under the fixed frequency injection method and the proposed MC method are compared and analyzed. The high performance of the proposed MC method has been validated by the experiment drive platform and compared with the traditional fixed frequency under different control conditions. [ABSTRACT FROM AUTHOR]

This paper presents a model predictive flux control strategy for a six-phase permanent magnet synchronous motor with novel virtual voltage vectors. First, the novel virtual vector combination suppresses the stator current harmonics in the x–y subspace and simplifies the cost function. In order to be easily implemented in a real-time system, the vectors are cleverly synthesized in two different ways. In addition, the use of 24 auxiliary vectors of two different magnitudes effectively reduces torque ripple. For the sake of reducing the computational burden, the range of prediction vectors is further reduced by dividing sectors and look-up tables. Finally, the effectiveness of the proposed method is verified by experimental comparison. [ABSTRACT FROM AUTHOR]

This paper proposes a model predictive current control (MPCC) method of six-phase permanent magnet synchronous hub motor (PMSHM) using auxiliary voltage vectors. First of all, 26 voltage vectors can be obtained from the switching states of six inverters, and 24 auxiliary voltage vectors can be generated by combining them. Second, four virtual voltage vectors can be found through the required locations, and the two vectors with the lowest cost will be selected by cost function. Then, additional current error correction module can better track the current. Finally, the proposed method is compared with the conventional MPCC method and another method with virtual voltage vectors. The innovation of this paper is that the recombination of vectors can better track and predict the current. A current error compensation module is added to reduce the error between the actual current and the predicted current. And the experimental results show that the proposed method has better performance such as smaller torque ripple and current THD both in steady and dynamic states. Finally, the strategy proposed in this paper was applied to the vehicle model through HIL test platform. The possibility of applying the strategy proposed in this paper to pure electric vehicles was verified under New European Driving Cycle (NEDC). [ABSTRACT FROM AUTHOR]