Selective Voltage Vector Based Predictive Torque Control of an Open-end Winding Induction Motor Drive with Four-Level Inversion for EV’s

Direct Torque Control (DTC) and Predictive Torque Control (PTC) Strategies emerged as popular techniques for speed control of Induction Motor Drives. In this article, an effective PTC strategy is introduced to an Open-End Winding Induction Motor Drive (OEWIMD) to control torque and flux directly. PTC offers all the features of classical DTC and it provides lesser ripple in torque and flux. The limitations of classical PTC are: (i) higher common-mode voltage (ii) variable switching frequency (iii) high computational burden and (iii) selection of weighting factors. To trounce some of these problems PTC of OEWIMD is developed. OEWIMD is operated with dual inverter configuration. In dual inverter configuration, if the inverters are operated with equal DC bus voltages (1:1 ratio), it can deliver ‘18’ space vector locations, out of these space vector locations only six active space vectors and a null vector has been considered. The voltage vectors used to implement the proposed algorithm can maintain less common-mode voltage. The proposed algorithm is verified by comparing the obtained experimental results with simulation results.

Open end winding induction motor (OEWIM) drives are better alternate for multi‐level inverter fed induction motor drives. OEWIM drives can be used in industries and electric vehicles but they entail ripple‐free torque. Predictive torque control (PTC) strategy offers high dynamic performance and lesser ripple in torque, flux when compared with direct torque control. Classical PTC involves high switching frequencies and empirical methods to select weighting factors. The selection and tuning of weighting factors are cumbersome. In this article, a new normalised weighted sum model (WSM) based PTC of four‐level inverter fed OEWIM is introduced to curtail torque, flux ripples, switching frequency and enhance the selection of weighting factors. The proposed algorithm uses multi‐objective cost function and the optimisation of cost function is performed by using normalised WSM. The normalisation of individual cost function simplifies the selection of weighting factors to select optimal voltage vector. As a result, the proposed PTC offers all the features of classical PTC and overcomes the difficulties involved in classical PTC. Simulation and experimental studies are performed on dual inverter fed OEWIM with four‐level inversion. The effectiveness of proposed algorithm is verified by comparing proposed PTC algorithm with classical PTC algorithm.

In this paper, the direct torque control (SVM-DTC) and predictive torque control (SVM-PTC) strategies based on space vector modulation (SVPWM) were studied, which effectively solved the problems about the high torque and flux ripples of traditional direct torque control (TDTC). Firstly, the voltage vector applied to the PMSM was accurately calculated in the stationary and rotating coordinate system respectively. Next, these three different DTC strategies were simulated and got corresponding diagrams of torque and flux. What’s more, the amplitude of ripple and the total harmonic distortion (THD) of current were compared and analyzed. Finally, the obtained results verify the superiority of the SVM-DTC and SVM-PTC and their effectiveness in reducing the PMSM torque and flux ripples. Furthermore, it came to a conclusion that the SVM-PTC could achieve the best control effect.

This paper presents discrete space vector modulation (DSVM) based predictive torque control (PTC) of induction motor for Electric Vehicle (EV). In the conventional PTC, only seven voltage vectors are utilized for determination of flux and torque. In order to utilize the converter control region effectively without decreasing the sampling time of the controller and to decrease the ripple in the torque, flux and current, DSVM based PTC is applied for induction motor. According to the DSVM, each sampling time is divided into 3 equal parts and by applying standard voltage vectors totally 37 virtual voltage vectors are generated. These voltage vectors are used for flux and torque prediction. To decrease the computational burden of DSVM-PTC, number of voltage vectors used for the prediction is reduced by direct torque control (DTC) based sector selection method. The starting current of the motor is limited by a nonlinear function added in the cost function. The speed of the motor is estimated by rotor flux based speed estimation technique. Torque ripple, motor current THD are analysed and compared with conventional techniques in different modes of operation of the motor. The DSVM – PTC algorithm is evaluated in simulation and experimental platform, which gives excellent response even in low sampling frequency compared to conventional methods.

The performance of conventional direct torque control strategy from the viewpoint of flux and torque ripples has been unsatisfactory. Therefore, this study aims to propose a novel finite-control-set model predictive direct torque control strategy with extended output based on two-step prediction. An appropriate pre-selected vector, which is modulated in a specific manner, is selected through a look-up table and then used to optimise the pre-selected voltage vector based on the computing result from the model prediction and output it. The proposed strategy extends the range of the vectors that can be used to enhance the flux and torque control performance and reduce ripples and computational complexity in comparison with the conventional finite-control-set model predictive direct torque control. The feasibility of the proposed method is verified by conducting a verification test using dSPACE and Tyhpoon HIL 402 experimental platform.

This study proposes direct torque and flux control of dual-inverter-fed open-end winding induction motor (OEWIM) with the help of model predictive control. OEWIMs are extensively used in electric vehicles and for ship propulsion but they require a high dynamic performance. Predictive torque control (PTC) retains the features of direct torque control and offers a high dynamic performance by eliminating start-up problems. In this study, predictive torque control is implemented for multilevel inversion-fed OEWIMs. Multilevel inversion is obtained by operating two two-level inverters with equal and unequal DC link voltages. The proposed study gives a comparative analysis of PTC of OEWIM for various speeds and numerical analysis of torque ripple and flux ripple. The proposed methods are simulated using MATLAB/SIMULINK and experimental response shows the validity of the developed methods.

Nowadays electric vehicle has increasingly gained much popularity indicated by growing global share market targeted at 30% by 2030 after recording 7.2million global stock in 2019. Compared to Internal Combustion Engine (ICE) counterpart, Battery Electric Vehicles (BEV) produce zero tailpipe emission which greatly reducing carbon footprints. Induction motor has been widely used and its control technology has evolved from scalar type volt/hertz to recent predictive control technology. This allows induction motor’s application to expand from being the workhorse of industry to become prime mover in electric vehicle, where high performance is expected. Among vector control scheme, Direct Torque Control (DTC) has gained interest over Field Oriented Control (FOC) with simpler structure, better robustness and dynamics performance yet suffer from high torque and flux ripple. In electric vehicle applications, high ripple at low speed is highly undesirable, potentially causing torsional vibration. High performance control requires speed sensor integration, which often increase complexity in the design. The work aims to review the best control technology for induction motor in electric vehicle application through performance parameter evaluation such as improvement on dynamic response, torque and flux ripple reduction, and component optimization. Several arise issues in motor control and possible methods to circumvent are highlighted in this work. In conclusion, model predictive torque control (MPTC) is the most promising scheme for electric vehicle with excellent dynamic response, good low speed performance, and 50% torque ripple reduction compared to conventional DTC and potential integration with sliding mode observer for sensorless solution.

This work deals with the performance improvement study of the direct torque control (DTC) of induction Motor (IM) based on Fuzzy Second Order Sliding Mode Control (FSOSMC). Direct torque control using conventional Second Order Sliding Mode Control regulators has certain disadvantages such as significant flux, torque ripples and sensitivity to parametric variations. To overcome these drawbacks, we apply a new type with more robust regulators such as the fuzzy second order sliding mode control. In recent years, Ant Colony Optimization (ACO) algorithm have attracted considerable attention among various modern heuristic optimization techniques. This paper proposes the ant colony optimization algorithm with fuzzy second order sliding mode controller based direct torque control of induction motor to enhance the system performance and stability. Simulation results demonstrate the feasibility and validity of the proposed DTC-FSOSMC system by effectively accelerating system response, reducing torque and flux ripple and a very satisfactory performance has been achieved.KeywordsIMDTCFSOSMCACOParametric Uncertainties

Nowadays, Predictive Torque Control (PTC) strategy is recognized as a strong tool for controlling the motor drive. Intuitive and multi-objective controlling are significant benefits of PTC. Owing to these benefits, its application is introduced for Open Winding Permanent Magnet Synchronous Motor (OW-PMSM) drive. The basic PTC operated OW-PMSM drive consequences high Common Mode Voltage (CMV) and leads to early failure of motor bearings. In this paper, CMV elimination is proposed for OW-PMSM drive using voltage vector selection in PTC operation. In proposed PTC, the possible voltage vectors (VVs) are identified to gain zero CMV and preselected as prediction VVs for cost-function evaluation. From cost-function evaluation, optimal VV is considered for controlling OW-PMSM drive. In addition, decrease in switching frequency is achieved through proper utilization of optimal VV’s redundant switching states. Therefore, the overall modifications in proposed PTC of OW-PMSM drive ensure simple operational control, CMV elimination, and switching frequency and loss reduction. The claims of proposed PTC are verified through Matlab/Simulink platform and its proficiency is highlighted against basic PTC operation. Thus, the enhanced operation of proposed PTC for OW-PMSM drive with zero CMV is justified.KeywordsCommon Mode Voltage (CMV)Open Winding Permanent Magnet Synchronous Motor (OW-PMSM) drivePredictive Torque Control (PTC)

This paper presents Artificial Neural Networks (ANN) and Adaptive Neural-Fuzzy Inference System (ANFIS) for reduction of torque and flux ripples in transient and steady state response of Direct Torque Control (DTC) for Induction Motor drive. The Flux and Electromagnetic torque can be controlled by using efficient Direct Torque Control (DTC) scheme This proposed technique is to improve the torque, speed and flux response with the Artificial Neural Network (ANN) and then with the Adaptive Neuro-Fuzzy Inference (ANFIS). This paper shows implementation of DTC system using ANN and ANFIS on three phase induction motor to optimize the flux and to improve the performance of fast stator flux response in transient state. To improve the performance of DTC with the modern technique using ANN and ANFIS approach is implemented and performance of ANN DTC compared with CDTC and ANN DTC with ANFIS is done, conclusion is about the ANN approach shows the better performance than CDTC and ANFIS shows superior performance than ANN. The performance has been tested by using MATLAB/SIMULINK and NEURAL NETWORK toolbox.

The purpose of this study is to provide a literature review on power suspension for Open-End Winding Induction Machine Drives (OEWIMD) using two 2-level inverters. It also displays how far OEWIM’s Space Vector Modulation (SVM) technique has progressed. In this arrangement, the Common-Mode Voltage (CMV) problem, zero sequence current circulation in motor phases, and overcharging of DC link capacitors have all been thoroughly explored. In addition, strategies for controlling the driving system, such as classical Direct Torque Control (DTC), Optimum Status Detection & Mode Selection (OSD & MS) in DTC, Predictive Torque Control (PTC), Modified predictive torque and flux control, and Model Predictive Control, where the weighting factor is estimated via an optimization approach, were investigated.

In this paper, a predictive direct torque control (DTC) of induction motor (IM) drive is presented. The control uses stator flux and current vectors as state variables. Predicted torque variation, with reference to its unforced response during each sampling time interval, is imposed with the aim of determining the pulse widths of the chain voltages of the supply inverter. The existence domain of the state variables in each sampling time interval is determined and depicted graphically. Inverter voltage saturation and current limitation constraints are also taken into account and shown graphically. The proposed control technique is employed in simulating a DTC IM drive by the Matlab Simulink tool. The simulation results, which are compared with those obtained by the traditional constant switching frequency DTC, highlight the good performance of the drive, especially referring to torque ripple and low-speed operation.

Compared with field orient control (FOC), direct torque control (DTC) is known to provide fast and robust response for induction motors. However, while offering high dynamic performance, classical DTC produces notable torque, flux, and current ripples, and operates with a variable inverter switching frequency. In this paper, a novel torque control scheme for induction motors is proposed. The stator flux and the electrical torque are directly controlled by variable-structure controllers, and stator voltage vectors calculated by the variable-structure controllers are applied to the motor by means of space vector modulation. The proposed scheme therefore provides smooth, fast and robust regulations of the electrical torque and the stator flux. Moreover, the implementation of the scheme is simple. Theoretical analysis shows the asymptotical convergence of electrical torque and stator flux tracking. Simulation and experimental results are provided to evaluate the performance of the proposed scheme.

Among all the control methods developed for Induction Motor (IM) drivers, the hysteresis controller based Direct Torque Control (DTC) method has an important place. This control method does not require other rotor and stator parameters except the stator resistance and does not require position or velocity sensors. However, there are some disadvantages of the DTC method, such as high torque, flux and current ripples. In this study, in order to reduce the high torque ripples occurring in an induction motor that is controlled by the hysteresis controller based conventional DTC method, a simple and effective Sugeno type Neuro-Fuzzy Torque Controller (NFTC) is proposed. This proposed controller is used instead of hysteresis controller. An experimental setup consisting of 1.1 kW induction motor, current and voltage measurement, DS1103 control card and two-level voltage source inverter was installed. To evaluate the performance of the proposed controller structure, various experimental studies were performed. Results obtained from the proposed NFTC based structure and conventional hysteresis controller based DTC structures are given comparatively. By the obtained experimental results, it was confirmed that the proposed NFTC-based controller structure considerably reduced flux and torque ripples in the motor.

Considering the disadvantages such as large torque ripple and unfixed switching frequency of the traditional direct torque control (TDTC), a deadbeat predictive torque control strategy (DB-PTC) based on stator flux coordinate was proposed. First of all, the control principle of TDTC and the reason of large flux and torque ripple were analyzed. Secondly, due to the fact that the parameters setting the controllers are complex and the control effect is not ideal in the SVM-DTC and the complex strategy with large computation in the model predictive torque control (MPTC) and DB-MPTC, the decoupling control of the stator flux and the torque was realized based on the stator flux coordinate, which combined the deadbeat theory to calculate the voltage vector and simply realize the decoupling control. Finally, the simulation results show that the strategy has the advantages of simple strategy and low computation while can effectively eliminate the torque ripple.