Critical Aspects of Electric Motor Drive Controllers and Mitigation of Torque Ripple - Review

Electric vehicles (EVs) are playing a vital role in sustainable transportation. It is estimated that by 2030, Battery EVs will become mainstream for passenger car transportation. Even though EVs are gaining interest in sustainable transportation, the future of EV power transmission is facing vital concerns and open research challenges. Considering the case of torque ripple mitigation and improved reliability control techniques in motors, many motor drive control algorithms fail to provide efficient control. To efficiently address this issue, control techniques such as Field Orientation Control (FOC), Direct Torque Control (DTC), Model Predictive Control (MPC), Sliding Mode Control (SMC), and Intelligent Control (IC) techniques are used in the motor drive control algorithms. This literature survey exclusively compares the various advanced control techniques for conventionally used EV motors such as Permanent Magnet Synchronous Motor (PMSM), Brushless Direct Current Motor (BLDC), Switched Reluctance Motor (SRM), and Induction Motors (IM). Furthermore, this paper discusses the EV-motors history, types of EVmotors, EV-motor drives powertrain mathematical modelling, and design procedure of EV-motors. The hardware results have also been compared with different control techniques for BLDC and SRM hub motors. Future direction towards the design of EV by critical selection of motors and their control techniques to minimize the torque ripple and other research opportunities to enhance the performance of EVs are also presented.publishedVersio

Advances in Rotating Electric Machines

It is difficult to imagine a modern society without rotating electric machines. Their use has been increasing not only in the traditional fields of application but also in more contemporary fields, including renewable energy conversion systems, electric aircraft, aerospace, electric vehicles, unmanned propulsion systems, robotics, etc. This has contributed to advances in the materials, design methodologies, modeling tools, and manufacturing processes of current electric machines, which are characterized by high compactness, low weight, high power density, high torque density, and high reliability. On the other hand, the growing use of electric machines and drives in more critical applications has pushed forward the research in the area of condition monitoring and fault tolerance, leading to the development of more reliable diagnostic techniques and more fault-tolerant machines. This book presents and disseminates the most recent advances related to the theory, design, modeling, application, control, and condition monitoring of all types of rotating electric machines

A TWELVE-PULSE LOAD COMMUTATED CONVERTER DRIVE SYSTEM WITH VSI FOR STARTING UP AND ACTIVE POWER FILTERING IN AN LNG APPLICATION

Variable Frequency Drives (VFDs) are an integral component of the industry in today’s age. VFDs provide a great range of control for electrical machines, and can be integrated in a variety of applications to meet the desired objectives of operation with improved reliability and efficiency. This thesis presents the Load-Commutated Converter (LCC) drive, which belongs to the Current Source Converter (CSC) based drive system family. Such drives are widely used in high power applications, due to power handling capabilities and the maturity of the drive system. The application under study is that of a helper/starter motor for a turbine compressor in a Liquefied Natural Gas (LNG) plant. Primarily, the thesis presents real-life scenarios of drive system operation such as constant/variable speed operation at constant/varying torque. The respective controllers for the LCC drive are presented alongside their results. In addition to simulating the drive system in this LNG application, current harmonic mitigation measures are presented in this study. The typical converter topology presented in this thesis is the 12-pulse type, however comparisons with different topologies (6, 18, and 24-pulse) have also been presented. Finally, a dual-purpose external Voltage Source Inverter (VSI) is used both as a starter and an Active Power Filter (APF), therefore addressing the issues of drive/load induced harmonics and LCC starting. As a conclusion, a controlled LCC drive model is simulated in SIMULINK to emulate the drive operation in actual plant conditions. The controlled drive is further studied for the presence of harmonics and their subsequent mitigation, by using passive as well as active power filters. The results obtained present the adequacy of the control system as well as the efficacy of the filters used for harmonics mitigation. Future work revolves around improving the efficiency of the APF, and the drive control system to be more robust and reliable. The system can further be investigated for enhancements as per operational requirements

Identification and Adaptive Control for High-performance AC Drive Systems.

High-performance AC machinery and drive systems can be found in a variety of applications ranging from motion control to vehicle propulsion. However, machine parameters can vary significantly with electrical frequency, flux levels, and temperature, degrading the performance of the drive system. While adaptive control techniques can be used to estimate machine parameters online, it is sometimes desirable to estimate certain parameters offline. Additionally, parameter identification and control are typically conflicting objectives with identification requiring plant inputs which are rich in harmonics, and control objectives often consisting of regulation to a constant set-point. In this dissertation, we present research which seeks to address these issues for high-performance AC machinery and drive systems. The first part of this dissertation concerns the offline identification of induction machine parameters. Specifically, we have developed a new technique for induction machine parameter identification which can easily be implemented using a voltage-source inverter. The proposed technique is based on fitting steady-state experimental data to the circular stator current locus in the stator flux linkage reference-frame for varying steady-state slip frequencies, and provides accurate estimates of the magnetic parameters, as well as the rotor resistance and core loss conductance. Experimental results for a 43 kW induction machine are provided which demonstrate the utility of the proposed technique by characterizing the machine over a wide range of flux levels, including magnetic saturation. The remainder of this dissertation concerns the development of generalizable design methodologies for Simultaneous Identification and Control (SIC) of overactuated systems via case studies with Permanent Magnet Synchronous Machines (PMSMs). Specifically, we present different approaches to the design of adaptive controllers for PMSMs which exploit overactuation to achieve identification and control objectives simultaneously. The first approach utilizes a disturbance decoupling control law to prevent the excitation input from perturbing the regulated output. The second approach uses a Lyapunov-based adaptive controller to constrain the states to the output error-zeroing manifold on which they are varied to provide excitation for parameter identification. Finally, a receding-horizon control allocation approach is presented which includes a metric for generating persistently exciting reference trajectories.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120862/1/davereed_1.pd

Design and Control of Electrical Motor Drives

Dear Colleagues, I am very happy to have this Special Issue of the journal Energies on the topic of Design and Control of Electrical Motor Drives published. Electrical motor drives are widely used in the industry, automation, transportation, and home appliances. Indeed, rolling mills, machine tools, high-speed trains, subway systems, elevators, electric vehicles, air conditioners, all depend on electrical motor drives.However, the production of effective and practical motors and drives requires flexibility in the regulation of current, torque, flux, acceleration, position, and speed. Without proper modeling, drive, and control, these motor drive systems cannot function effectively.To address these issues, we need to focus on the design, modeling, drive, and control of different types of motors, such as induction motors, permanent magnet synchronous motors, brushless DC motors, DC motors, synchronous reluctance motors, switched reluctance motors, flux-switching motors, linear motors, and step motors.Therefore, relevant research topics in this field of study include modeling electrical motor drives, both in transient and in steady-state, and designing control methods based on novel control strategies (e.g., PI controllers, fuzzy logic controllers, neural network controllers, predictive controllers, adaptive controllers, nonlinear controllers, etc.), with particular attention to transient responses, load disturbances, fault tolerance, and multi-motor drive techniques. This Special Issue include original contributions regarding recent developments and ideas in motor design, motor drive, and motor control. The topics include motor design, field-oriented control, torque control, reliability improvement, advanced controllers for motor drive systems, DSP-based sensorless motor drive systems, high-performance motor drive systems, high-efficiency motor drive systems, and practical applications of motor drive systems. I want to sincerely thank authors, reviewers, and staff members for their time and efforts. Prof. Dr. Tian-Hua Liu Guest Edito

A New Position and Speed Estimation Scheme for Position Control of PMSM Drives Using Low-Resolution Position Sensors

A new position control method for permanent magnet synchronous motor (PMSM) drive with a low-resolution encoder is proposed in this paper. Three binary Hall position sensors are utilized to realize a moderate-performance position control system for the consideration of economy and simplicity in servo application. Compared with sensorless control, the usage of binary Hall position sensors is a guarantee of both control performance and low cost. However, the low resolution of the Hall sensor will heavily deteriorate the accuracy of the position and speed calculation. Such drawback can be effectively minimized by using appropriate position and speed estimation schemes. With the help of polynomial fitting and state observer techniques, a solution is provided to realize semi-closed loop control by treating the position and speed estimators as separate systems. The performance can be improved (1) by proposing a polynomial fitting scheme with least squares method, high-resolution rotor-position predictor can be derived by fitting the predefined position data from binary Hall position sensors in a linear or quadratic manner; (2) by adopting the dual-sampling-rate observer, instantaneous speed can be estimated at each control cycle and the estimation error is corrected once a new measurement form the Hall arrives. Furthermore, a nonlinear position control algorithm is introduced to increase standstill stability. Extensive experimental results are given to demonstrate the feasibility of the proposed method and its superiority over conventional methods

Modelling and control techniques for multiphase electric drives: a phase variable approach

Multiphase electric drives are today one of the most relevant research topics for the electrical engineering scientific community, thanks to the many advantages they offer over standard three-phase solutions (e.g., power segmentation, fault-tolerance, optimized performances, torque/power sharing strategies, etc...). They are considered promising solutions in many application areas, like industry, traction and renewable energy integration, and especially in presence of high-power or high-reliability requirements. However, contrarily to the three-phase counterparts, multiphase drives can assume a wider variety of different configurations, concerning both the electrical machine (e.g., symmetrical/asymmetrical windings disposition, concentrated/distributed windings, etc...) and the overall drive topology (e.g., single-star configuration, multiple-star configuration, open-end windings, etc…). This aspect, together with the higher number of variables of the system, can make their analysis and control more challenging, especially when dealing with reconfigurable systems (e.g., in post-fault scenarios). This Ph.D. thesis is focused on the mathematical modelling and on the control of multiphase electric drives. The aim of this research is to develop a generalized model-based approach that can be used in multiple configurations and scenarios, requiring minimal reconfigurations to deal with different machine designs and/or different converter topologies, and suitable both in healthy and in faulty operating conditions. Standard field-oriented approaches for the analysis and control of multiphase drives, directly derived as extensions of the three-phase equivalents, despite being relatively easy and convenient solutions to deal with symmetrical machines, may suffer some hurdles when applied to some asymmetrical configurations, including post-fault layouts. To address these issues, a different approach, completely derived in the phase variable domain, is here developed. The method does not require any vector space decomposition or rotational transformation but instead explicitly considers the mathematical properties of the multiphase machine and the effects of the drive topology (which typically introduces some constraints on the system variables). In this thesis work, the proposed approach is particularized for multiphase permanent magnet synchronous machines and for multiphase synchronous reluctance machines. All the results are obtained through rigorous mathematical derivations, and are supported and validated by both numerical analysis and experimental tests. As proven considering many different configurations and scenarios, the main benefits of the proposed methodology are its generality and flexibility, which make it a viable alternative to standard modelling and control algorithms

Advances in the Field of Electrical Machines and Drives

Electrical machines and drives dominate our everyday lives. This is due to their numerous applications in industry, power production, home appliances, and transportation systems such as electric and hybrid electric vehicles, ships, and aircrafts. Their development follows rapid advances in science, engineering, and technology. Researchers around the world are extensively investigating electrical machines and drives because of their reliability, efficiency, performance, and fault-tolerant structure. In particular, there is a focus on the importance of utilizing these new trends in technology for energy saving and reducing greenhouse gas emissions. This Special Issue will provide the platform for researchers to present their recent work on advances in the field of electrical machines and drives, including special machines and their applications; new materials, including the insulation of electrical machines; new trends in diagnostics and condition monitoring; power electronics, control schemes, and algorithms for electrical drives; new topologies; and innovative applications

Current commutation and control of brushless direct current drives using back electromotive force samples

Brushless DC machines (BLDC) are widely used in home, automotive, aerospace and military applications. The reason of this interest in different industries in this type of machine is due to their significant advantages. Brushless DC machines have a high power density, simple construction and higher efficiency compared to conventional AC and DC machines and lower cost comparing to permanent magnet AC synchronous machines. The phase currents of a BLDC machine have to commutate properly which is realised by using power semiconductors. For a proper commutation the rotor position is often obtained by an auxiliary instrument, mostly an arrangement of three Hall-effect sensors with 120 spatial displacement. In modern and cost-effective BLDC drives the focus is on replacing the noise sensitive and less reliable mechanical sensors by numerical algorithms, often referred to as sensorless or self-sensing methods. The advantage of these methods is the use of current or voltage measurements which are usually available as these are required for the control of the drive or the protection of the semiconductor switches. Avoiding the mechanical position sensor yields remarkable savings in production, installation and maintenance costs. It also implies a higher power to volume ratio and improves the reliability of the drive system. Different self-sensing techniques have been developed for BLDC machines. Two algorithms are proposed in this thesis for self-sensing commutation of BLDC machines using the back-EMF samples of the BLDC machine. Simulations and experimental tests as well as mathematical analysis verify the improved performance of the proposed techniques compared to the conventional back-EMF based self-sensing commutation techniques. For a robust BLDC drive control algorithm with a wide variety of applications, load torque is as a disturbance within the control-loop. Coupling the load to the motor shaft may cause variations of the inertia and viscous friction coefficient besides the load variation. Even for a drive with known load torque characteristics there are always some unmodelled components that can affect the performance of the drive system. In self-sensing controlled drives, these disturbances are more critical due to the limitations of the self-sensing algorithms compared to drives equipped with position sensors. To compensate or reject torque disturbances, control algorithms need the information of those disturbances. Direct measurement of the load torque on the machine shaft would require another expensive and sensitive mechanical sensor to the drive system as well as introducing all of the sensor related problems to the drive. An estimation algorithm can be a good alternative. The estimated load torque information is introduced to the self-sensing BLDC drive control loop to increase the disturbance rejection properties of the speed controller. This technique is verified by running different experimental tests within different operation conditions. The electromagnetic torque in an electrical machine is determined by the stator current. When considering the dynamical behaviour, the response time of this torque on a stator voltage variation depends on the electric time constant, while the time response of the mechanical system depends on the mechanical time constant. In most cases, the time delays in the electric subsystem are negligible compared to the response time of the mechanical subsystem. For such a system a cascaded PI speed and current control loop is sufficient to have a high performance control. However, for a low inertia machine when the electrical and mechanical time constants are close to each other the cascaded control strategies fail to provide a high performance in the dynamic behavior. When two cascade controllers are used changes in the speed set-point should be applied slowly in order to avoid stability problems. To solve this, a model based predictive control algorithm is proposed in this thesis which is able to control the speed of a low inertia brushless DC machine with a high bandwidth and good disturbance rejection properties. The performance of the proposed algorithm is evaluated by simulation and verified by experimental results as well. Additionally, the improvement on the disturbance rejection properties of the proposed algorithm during the load torque variations is studied. In chapters 1 and 2 the basic operation principles of the BLDC machine drives will be introduced. A short introduction is also given about the state of the art in control of BLDC drives and self-sensing control techniques. In chapter 3, a model for BLDC machines is derived, which allows to test control algorithms and estimators using simulations. A further use of the model is in Model Based Predictive Control (MBPC) of BLDC machines where a discretised model of the BLDC machine is implemented on a computation platform such as Field Programmable Gate Arrays (FPGA) in order to predict the future states of the machine. Chapter 4 covers the theory behind the proposed self-sensing commutation methods where new methodologies to estimate the rotor speed and position from back-EMF measurements are explained. The results of the simulation and experimental tests verifies the performance of the proposed position and speed estimators. It will also be proved that using the proposed techniques improve the detection accuracy of the commutation instants. In chapter 5, the focus is on the estimation of load torque, in order to use it to improve the dynamic performance of the self-sensing BLDC machine drives. The load torque information is used within the control loop to improve the disturbance rejection properties of the speed control for the disturbances resulting from the applied load torque of the machine. Some of the machine parameters are used within speed and load torque estimators such as back-EMF constant Ke and rotor inertia J. The accuracy with which machine parameters are known is limited. Some of the machine parameters can change during operation. Therefore, the influence of parameter errors on the position, speed and load torque is examined in chapter 5. In Chapter 6 the fundamentals of Model based Predictive Control for a BLDC drive is explained, which are then applied to a BLDC drive to control the rotor speed. As the MPC algorithm is computationally demanding, some enhancements on the FPGA program is also introduced in order to reduce the required resources within the FPGA implementation. To keep the current bounded and a high speed response a specific cost function is designed to meet the requirements. later on, the proposed MPC method is combined with the proposed self-sensing algorithm and the advantages of the combined algorithms is also investigated. The effects of the MPC parameters on the speed and current control performance is also examined by simulations and experiments. Finally, in chapter 7 the main results of the research is summarized . In addition, the original contributions that is give by this work in the area of self-sensing control is highlighted. It is also shown how the presented work could be continued and expanded