Vector Controlled Induction Motor Research Articles

Intelligent Power Management of Electric vehicle with onboard PV by ANN-based Model Predictive Control

This paper presents a novel configuration of Hybrid Electric Vehicle (HEV) with fuel cell as primary source and an on-board Photovoltaic (PV) array as secondary source. It incorporates an advanced Model Predictive Control (MPC) system that utilizes Artificial Neural Network (ANN) technology to control induction motor of electric vehicle. The system has three main parts: a fuel cell, an electrolyzer, and a PV array. Each part has a different job in supplying power to the vehicle. The PV array functions to supplement the fuel cell by providing power when ample irradiation is available. When the car isn't being used, the extra power from the solar panels goes to the electrolyzer. There, it turns into chemical energy, making hydrogen. This hydrogen is stored in the onboard hydrogen tank for future use. To fulfil the voltage needs of both the motor and energy sources, a quadratic bidirectional buck-boost converter (QBBC) is utilized. This converter efficiently manages the voltage output while ensuring compatibility with various energy sources. The system undergoes comprehensive analysis under different irradiance and speed conditions to evaluate its performance and efficiency. To make the solar power system more efficient and cost-effective, a Maximum Power Point Tracking (MPPT) algorithm is used. This algorithm helps get the maximum power from the PV system. An improved incremental conductance algorithm is used for this, making sure it accurately finds the best power point. The control strategy incorporates both outer voltage and inner current control, to effectively control the DC output voltage of the QBBC. The vehicle's drive system employs an indirect vector-controlled induction motor. To regulate the motor's speed effectively, an advanced ANN-MPC system is used, providing precise control and high efficiency. This paper introduces a novel approach for predictive torque control of AC machines, eliminating the need for weighting factors. An ANN estimator is used to accurately expect the motor speed, which improves the overall performance of the control system. By using MATLAB/SIMULINK to test how well the proposed Electric Vehicle (EV) setup works and make sure it's effective in real-life situations. Through this comprehensive approach, the paper contributes to advancing the development of efficient and intelligent electric vehicle systems..

Implementation of MLP-Based Classifier of Current Sensor Faults in Vector-Controlled Induction Motor Drive

Implementation of MLP-Based Classifier of Current Sensor Faults in Vector-Controlled Induction Motor Drive

Artificial intelligence and PI gain optimisation for sensorless indirect vector control of induction motor-based electric vehicle drives

This paper aims to develop indirect vector-controlled induction motor (IM) drives for EV applications. In this paper, controllers are used in two places. The first is the speed controller, used to reduce the error between reference speed and actual EV speed. And the second one is the speed estimator used to reduce the error between the adaptive model and reference model during sensorless operation. The performance of these controllers is compared by using ANFIS, ANN and PI controllers. The real-time performance of the IM-based EV drive system is dependent on the PI gain value. Hence in this paper, seven human and nature-inspired metaheuristic optimisation techniques such as JFO, GWO, ABC, PSO, GA, TLBO and DE are used to obtain the optimised value of the PI controller.

Compensation of Current Sensor Faults in Vector-Controlled Induction Motor Drive Using Extended Kalman Filters

In electric drive systems, one of the most common faults is related to measurement equipment, including current sensors (CSs). As information about the stator current is crucial to ensure precise control of AC drives, such a fault significantly affects the quality and security of the entire system. For this reason, a modified extended Kalman filter (EKF) has been presented in this paper as an algorithmic solution to restore stator current in the event of CS failure. In order to minimize the impact of rotor and stator resistance variations on the quality of the estimation, the proposed model includes an estimation of the general coefficient of their changes. In contrast to solutions known in the literature, the presented model considers changes in both resistances in the form of a single coefficient. This approach allows us to maintain a low order of the estimator (fifth) and thus minimize the tendency to system instability and decrease computation burden. Extensive simulation tests have shown a significant improvement in the accuracy of stator current estimation under both motor and regenerating modes, a wide speed range (1–100%), and changes in motor parameters.

Повышение адекватности моделей электромеханотронных систем

Development of electromechanotronic systems is connected with modeling and calculation of processes of various kinds. Electromagnetic, mechanical and thermal processes are analyzed, control, regulation, protection, monitoring and diagnostics problems are solved. Systems may contain a large number of power semiconductor elements. Short-term and multi-hour processes are calculated. Taking into account mutual connections of processes of various kinds in calculations of complex systems requires inadmissibly large expenditures of machine time. The problems are solved under more or less significant assumptions, which leads to a decrease in the adequacy of the results. The methods of systems calculation are proposed, which allow to reduce machine time consumption and to use more detailed description of installations and more accurate computer models. Possibilities of calculation of processes by «smooth components» of variables, and also application of dual models, in which speed and accuracy are combined, are considered. As an example, a system with an active cascade frequency converter containing 780 transistors and diodes, parasitic inductances and snubber circuits, and a vector-controlled induction motor with ladder circuits accounting for current displacement is analyzed. The model takes into account energy losses in the converter and in the motor, as well as the distribution of losses by components. It also takes into account emergency operation modes associated with disconnection of a part of low-voltage units of the cascade converter and maintenance of power quality at its input and output by symmetrization of the main current components and suppression of parasitic components.

Sensorless Saliency-Based Control of Dual Induction Machines Under Dynamic Load Imbalances Using Three Current Sensors

To perform vector control of single-inverter dual induction motors, drives are standardly equipped with current and position sensors. In medium-power railway applications, a shaft encoder is usually mounted on each motor, and the inverter output current is measured using two current sensors. Yet, this configuration features some disadvantages. First, torque-sharing cannot be computed because stator current of each motor is unknown. Similarly, saliency extraction of each motor is not achievable since individual current drawn by each motor is not measured. An option that achieves torque-sharing calculation is to attach two current sensors to each motor, provided that both rotor positions are known. However, industrial inverter/control platforms considered offer a maximum of three input channels for current sensors. Besides, some industry fields tend towards encoderless speed control. Therefore, this paper proposes to use three current sensors in total and no shaft encoders. Thanks to a special current sensor arrangement and a voltage step excitation method, saliency-based encoderless control is achieved. In addition, a method is presented to estimate torque-sharing. Experimental measurements, taken on a dual induction motor test bench, will show the capability of the proposed methodology to identify individual loading as well as excellent encoderless control performance.

Sensorless Vector Control for Induction Motor Drive at Very Low and Zero Speeds Based on an Adaptive-Gain Super-Twisting Sliding Mode Observer

To achieve a balance between estimation accuracy and the chattering problem for sensorless induction motor IM drive systems at very low and zero speeds, an adaptive-gain super-twisting sliding mode observer (AGSTSMO) is proposed. The value of the adaptive gains (experimental observer coefficients) in the control law is assigned utilizing Lyapunov stability theory to ensure expeditious convergence of the estimated variables to improve the accuracy and suppress chattering at very low and zero speeds operation. The AGSTSM observer eliminates the need to use a low-pass filter to obtain the equivalent control, as in the traditional first-order sliding-mode observer, which causes a delay in estimating the equivalent control law. Simulation and experimental results are shown under different operating conditions to illustrate the effectiveness of the suggested approach, which achieves excellent estimation precision and chattering elimination capability simultaneously, thus increasing robustness versus deviations of the motor parameters. Moreover, to prove the superiority and efficiency of the proposed observer is compared to that of a super-twisting sliding mode observer (STSMO) under various operating conditions.

Decoupling control of three-dimensional electromagnetic forces in linear induction motors based on novel equivalent circuit

Since the single-sided linear induction motor not only produces thrust, but also produces vertical and transversal forces, if no specific control is applied, vertical and transversal forces can have a negative effect on the load propelled by the motor, especially in the application of the railway transit. Therefore, the best solution to the above issues is to independently control the vertical force or transversal force without changing the thrust in the motor. First, this paper presents a 3-D electromagnetic forces, i.e. thrust, vertical and transversal forces, decoupling strategy for the single-sided linear induction motor based on a novel equivalent circuit considering the longitudinal end effect and 3-D electromagnetic forces. Then, the slip-frequency calculator, thrust observer and thrust controller in the conventional vector control for induction motors are reconstructed, and a force decoupling controller is proposed. Finally, the simulation to verify the vector control with the force decoupling controller for the linear motor is performed by using a prototype motor which is used for railway transit.

Optimized Reference Points Based Vector Control of Induction Motor Drive for Electric Vehicle

In this paper, to reduce the power consumption and to improve the efficiency in both steady state and transient condition, optimized operating point selection based control of induction motor is presented. Firstly, the steady state non-linear loss function, which incorporates iron loss and saturation is solved for steady state optimized operating point (OOP) references. To enhance the maximum torque along with reduction in energy consumption during low speed transients, over fluxing based OOP reference selection is proposed. To overcome the adverse impact of over fluxing, in the decoupled control and to make the selection process robust, model reference adaptive system (MRAS) is used for parallel estimation of rotor time constant and stator resistance. Super twisting sliding mode observer (ST-SMO) is incorporated for speed estimation, which is also utilized as reference model (rotor time constant estimation) and adjustable model (stator resistance estimation) in MRAS based observer. This OOP reference selection method is implemented along with the rotor field-oriented control (RFOC) for the induction machine. From the simulation and experimental results, it is justified that the presented algorithm reduces the power consumption and improves the efficiency at all the modes of operation of electric vehicles.

Sensorless vector control of Dual Star induction Motor Associated with Fuzzy Mutual - MRAS estimator in electric vehicle

Sensorless vector control of Dual Star induction Motor Associated with Fuzzy Mutual - MRAS estimator in electric vehicle

PERFORMANCE ANALYSIS AND SPEED CONTROL USING INDIRECT VECTOR CONTROLLED FOR INDUCTION MOTOR DRIVES

This paper presents the speed control scheme of an indirect vector-controlled induction motor (IM) drive. PWM controlling scheme is based on Voltage source inverter type space vector pulse width modulation (SVPWM) and the Conventional-PID controller or Fuzzy-PID controller is employed in closed-loop speed control. Decoupling of the stator current into torque and flux producing (d-q) current components model of an induction motor is involved in the indirect vector control. The torque component Iq current of an IM is developed by an intelligent-based Fuzzy PID controller. Based on settling time and dynamic response the performance of the Fuzzy Logic Controller is compared with that of the PID Controller to sudden load changes. It provides better control of motor torque with high dynamic performance. The simulated design is tested using various toolboxes in MATLAB. Simulation results of both controllers are presented for comparison.

African vulture optimizer algorithm based vector control induction motor drive system

<span lang="EN-US">This study describes a new optimization approach for three-phase induction motor speed drive to minimize the integral square error for speed controller and improve the dynamic speed performance. The new proposed algorithm, African vulture optimizer algorithm (AVOA) optimizes internal controller parameters of a fuzzy like proportional differential (PD) speed controller. The AVOA is notable for its ease of implementation, minimal number of design parameters, high convergence speed, and low computing burden. This study compares fuzzy-like PD speed controllers optimized with AVOA to adaptive fuzzy logic speed regulators, fuzzy-like PD optimized with genetic algorithm (GA), and proportional integral (PI) speed regulators optimized with AVOA to provide speed control for an induction motor drive system. The drive system is simulated using MATLAB/Simulink and laboratory prototype is implemented using DSP-DS1104 board. The results demonstrate that the suggested fuzzy-like PD speed controller optimized with AVOA, with a speed steady state error performance of 0.5% compared to the adaptive fuzzy logic speed regulator’s 0.7%, is the optimum alternative for speed controller. The results clarify the effectiveness of the controllers based on fuzzy like PD speed controller optimized with AVOA for each performance index as it provides lower overshoot, lowers rising time, and high dynamic response.</span>

Design and Modeling of Hybrid Electric Vehicle Powered by Solar and Fuel Cell Energy with Quadratic Buck/Boost Converter

Considering technological advancements and international standards mandating reduced greenhouse gas emissions, automobile manufacturers have shifted their focus toward innovative technologies related to fuel-cell electric vehicles. Despite having an all-electric powertrain, fuel cell electric vehicles (FCEVs) utilize a fuel cell stack as their energy source, which runs on hydrogen and results in the emission of only water and heat. Due to the absence of tailpipe pollutants, fuel-cell electric vehicles are considered zero-emission vehicles. Among fuel cell types, low-temperature and low-pressure fuel cells, such as Proton Exchange Membrane Fuel Cells (PEMFCs), are well-suited for vehicular applications as they exhibit high power density, operate at lower temperatures (60-80°C), and are less prone to corrosion than other types of fuel cells. The main objective of this paper is to investigate solar-assisted electric fuel cell vehicles that efficiently integrate the fuel cell system with an electrolyzer and solar power to fulfill the fluctuating power demands of the electric motor and auxiliary systems. A novel EV configuration with a fuel cell, electrolyzer and onboard PV cell is proposed. An onboard PV cell can assist the fuel cell when the irradiation is enough to generate the power. During the idle conditions of vehicles, PV-generated power can be converted into chemical energy using an electrolyzer and generated hydrogen can be stored in a hydrogen tank. To match the voltage required by the motor and sources a quadratic bidirectional buck-boost converter is employed. The proposed configuration is examined by considering variable irradiance and variable speed values. To obtain the maximum power output from the photovoltaic (PV) panel, a Maximum Power Point Tracking (MPPT) algorithm is employed to regulate the PV system. To enhance the efficiency and cost-effectiveness of PV systems, an enhanced version of the incremental conductance algorithm is utilized as the MPPT control strategy. Outer voltage and inner current control are adopted to regulate the DC output voltage of the QBBC converter. An indirect vector-controlled induction motor is used as vehicle drive. Simulations are performed to investigate the proposed EV configuration in MATLAB/SIMULINK.

Evaluation of Start-Up Performance of Induction Motor Speed Sensorless Vector Control Using Adaptive Flux Observer Considering Inverter Voltage Error

Speed sensorless vector control for induction motors using an adaptive flux observer makes it difficult to output high and stable torque at a standstill condition when the output voltage error mainly due to dead-time of the inverter. This voltage error leads to a speed estimation error. Consequently, axis misalignment occurs, and a torque value that tracks the reference command value cannot be output. A method has been proposed to compensate for this voltage error by using a current command value; however, the compensation accuracy is limited because of the forward voltage drop and nonlinear characteristics of power switching devices. This study applies the conventional dead-time compensation to the model reference adaptive system, and the effect of varying the amount of compensation, instead of investigating the true dead-time length, on the start-up is evaluated using a 2.2 kW experimental system and through dynamic analysis.

A Comprehensive Examination of Vector-Controlled Induction Motor Drive Techniques

This paper introduces a comprehensive examination of vector-controlled- (VC-) based techniques intended for induction motor (IM) drives. In addition, the evaluation and critique of modern control techniques that improve the performance of IM drives are discussed by considering a systematic literature survey. Detailed research on variable-speed drive control, for instance, VC and scalar control (SCC), was conducted. The SCC-based systems’ speed and V/f control purposes are clarified in closed and open loops of IM drives. The operations, benefits, and drawbacks of the direct and indirect field-oriented control systems are illustrated. Furthermore, the direct torque control (DTC) method for IMs is reviewed. Numerous VC methods established along with microprocessor/digital control, model reference adaptive control (MRAC), sliding mode control (SMC), and intelligent control (in terms of fuzzy logic (FL) and artificial neural networks (ANNs)) are described and examined. Uncertainties in the IM parameter are a considerable problem in VC drives. Therefore, this problem is addressed, and some studies that attempted to provide solutions are listed. Magnetic saturation and core loss impact are mentioned, as they are important issues in IM drives. Toward demonstrating the strengths and limitations of various VC configurations, a few experiments were simulated via MATLAB® and Simulink® that show the influence of machine parameter variation. Efforts are made to supply powerful guidelines for practicing engineers and researchers in AC drives.

Fitness distance balance-based Runge–Kutta algorithm for indirect rotor field-oriented vector control of three-phase induction motor

Fitness distance balance-based Runge–Kutta algorithm for indirect rotor field-oriented vector control of three-phase induction motor

Real-time implementation of SVPWM-sensorless vector control of induction motor using an extended Kalman filter

<p>In this research paper, space vector pulse width modulation (SVPWM)-sensorless vector control of an induction motor using an extended Kalman filter is presented. The aim of the proposed sensorless control method is to design, implement, and test a sensorless vector control scheme by simulation and experimental implementation. An extended Kalman filter (EKF) simultaneously estimates the rotor speed, the stator stationary axis components (<em>i<sub>αs</sub></em>, <em>i<sub>βs</sub></em>), and the rotor fluxes (<em>j<sub>αs</sub></em>, <em>j<sub>βs</sub></em>). The measured stator voltages and currents are employed as inputs for a recursive filter. Simulation results under various operating conditions validate the performances and effectiveness of the proposed observer. The experimental system consists of a host computer with two subsystems: console (SC) and master (SM). The SM subsystem converts to real-time C code, and this code is uploaded into OP5600 real time digital simulation (RTDS) for real-time execution. The obtained experimental results prove that the EKF speed observer can replace the speed or position sensor. This has the benefits of reducing the drive system’s size and overall cost as well as high system reliability.</p>

Novel Virtual Speed Sensor Design for Fault-Tolerant Vector Control for Induction Motor Drives Based on the Non-Linear Unscented Kalman Filter

Novel Virtual Speed Sensor Design for Fault-Tolerant Vector Control for Induction Motor Drives Based on the Non-Linear Unscented Kalman Filter

Optimized Kalman filters for sensorless vector control induction motor drives

<span lang="EN-US">This paper presents the comparison between optimized unscented Kalman filter (UKF) and optimized extended Kalman filter (EKF) for sensorless direct field orientation control induction motor (DFOCIM) drive. The high performance of UKF and EKF depends on the accurate selection of state and noise covariance matrices. For this goal, multi objective function genetic algorithm is used to find the optimal values of state and noise covariance matrices. The main objectives of genetic algorithm to be minimized are the mean square errors (MSE) between actual and estimation of speed, current, and flux. Simulation results show the optimal state and noise covariance matrices can improve the estimation of speed, current, torque, and flux in sensorless DFOCIM drive. Furthermore, optimized UKF present higher performance of state estimation than optimized EKF under different motor operating conditions.</span>

Quantitative feedback theory based robust speed control of vector controlled induction motor

In this research paper a method for the robust speed control of Indirect Field orient controlled induction motor (IM) is proposed. The quantitative feedback theory (QFT) in implemented to design the controller in order to achieve the desired performance for the closed loop system in the presence of uncertainties and parameter variations. In this research work the QFT based controller is designed for the simplified model of IM. The worst case of uncertainties and all possible parameter variations are taken into consideration. The IM with the controller developed is simulated using the MATLAB/Simulink and the results are analyzed and compared with the conventional proportional integral derivative (PID) controller. The time domain and frequency domain analysis of both controllers were conducted and compared. A study on the nature of electromagnetic torque and control signal is also included to justify the effectiveness of proposed controller. The simulation results verify the superior performance of the proposed robust control method compared to PID controller.