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

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

Fault Signature Identification for BLDC motor Drive System -A Statistical Signal Fusion Approach

A hybrid approach based on multirate signal processing and sensory data fusion is proposed for the condition monitoring and identification of fault signal signatures used in the Flight ECS (Engine Control System) unit. Though motor current signature analysis (MCSA) is widely used for fault detection now-a-days, the proposed hybrid method qualifies as one of the most powerful online/offline techniques for diagnosing the process faults. Existing approaches have some drawbacks that can degrade the performance and accuracy of a process-diagnosis system. In particular, it is very difficult to detect random stochastic noise due to the nonlinear behavior of valve controller. Using only Short Time Fourier Transform (STFT), frequency leakage and the small amplitude of the current components related to the fault can be observed, but the fault due to the controller behavior cannot be observed. Therefore, a framework of advanced multirate signal and data-processing aided with sensor fusion algorithms is proposed in this article and satisfactory results are obtained. For implementing the system, a DSP-based BLDC motor controller with three-phase inverter module (TMS 320F2812) is used and the performance of the proposed method is validated on real time data.Comment: 7 Pages, 7 figure

Steering System of Electric Vehicle using Extreme Learning Machine

The development of electric vehicle technology is currently increasing and growing very fast. Some efforts have been conducted, one of which is using BLDC (brushless direct current) motors to improve efficiency. This study utilized extreme learning machine (ELM) embedded on the microcontroller as well as the differential method for controlling the rotational speed of the BLDC motor. The experimental results on the acceleration testing by traveling a distance of 200 meters achieved the average current of 1.09 amperes. The average power efficiency test is 104 watts. Furthermore, the results of the efficiency experiment with a track length of 3.3 km (kilometers) in 10 minutes obtained the energy efficiency of 177.34 km/kWh (kilowatt for one hour

Fault tolerant vector control of five-phase permanent magnet motors

Equipped with appropriate control strategies, permanent magnet (PM) machines are becoming one of the most flexible types of actuators for many industrial applications. Among different types of PM machines, five-phase BLDC machines are very interesting in fault tolerant applications of PM drives. Torque improvement in five-phase BLDC machines can be accomplished by optimizing their mechanical structure or by enhancing their controlling methods. New current controllers are proposed in this thesis to improve the quality of generated torque under normal operations of five-phase BLDC machines. Proposed current controllers are based on combination of predictive deadbeat controlling strategy and Extended Kalman Filter estimation. These controllers will be the basis for accurate faulty operation of the motor. Operation of five-phase BLDC machines under faulty conditions has also been considered in this study. To improve the generated torque under faulty conditions, both amplitude and phase angle of fundamental and third current harmonics are globally optimized for the remaining healthy phases. Under faulty conditions, appropriate reference currents of a five-phase BLDC machine have oscillating dynamics both in phase and rotating reference frames. As a result, the implemented current controllers under these conditions should be robust and fast. Predictive deadbeat controllers are also proposed for faulty conditions of five-phase BLDC machines. Fault tolerant five-phase BLDC machines are very interesting in automotive applications such as electrical vehicles and more electric aircraft. In addition, these devices are gaining more importance in other fields such as power generation in wind turbines. In all of these applications, the efficiency of PM machine is of most importance. The efficiency of a typical five-phase BLDC machine is evaluated in this thesis for normal and different faulty conditions. Experimental evaluations are always conducted to verify the theoretical developments. These developments include proposed controlling methods, optimized reference currents, and simulated efficiency of five-phase BLDC machine under different operational conditions

A New Method for Friction Estimation in EMA Transmissions

The increasing interest for adopting electromechanical actuators (EMAs) on aircraft demands improved diagnostic and prognostic methodologies to be applied to such systems in order to guarantee acceptable levels of reliability and safety. While diagnostics methods and techniques can help prevent fault propagation and performance degradation, prognostic methods can be applied in tandem to reduce maintenance costs and increase overall safety by enabling predictive and condition-based maintenance schedules. In this work, a predictive approach for EMAs friction torque estimation is proposed. The algorithm is based on the reconstruction of the residual torque in mechanical transmissions. The quantity is then sampled and an artificial neural network (ANN) is used to obtain an estimation of the current health status of the transmission. Early results demonstrate that such an approach can predict the transmission health status with good accuracy

Design of a High-Speed Ferrite-based Brushless DC Machine for Electric Vehicles

In the present paper an analytic procedure for the preliminary design of a High-Speed ferrite-based Brushless DC Machine (HS-BLDC) has been proposed. In particular, mechanical and electromagnetic modeling have been developed in order to take into account their mutual influence in the definition of the geometry of the electrical machine. In addition, suitable design targets have been imposed in accordance with electric vehicle application requirements. Hence, several mechanical and electromagnetic constraints have been introduced in order to comply with high-speed operation, preventing demagnetization issues of ferrite magnets as well. Subsequently, an HS-BLDC characterized by an inner rotor configuration has been designed in accordance with the proposed methodology. The analytical procedure and the corresponding results have been reported and validated by means of Finite Element Analyses (FEAs), highlighting the effectiveness of the proposed configuration and design solutions

In-wheel motor vibration control for distributed-driven electric vehicles:A review

Efficient, safe, and comfortable electric vehicles (EVs) are essential for the creation of a sustainable transport system. Distributed-driven EVs, which often use in-wheel motors (IWMs), have many benefits with respect to size (compactness), controllability, and efficiency. However, the vibration of IWMs is a particularly important factor for both passengers and drivers, and it is therefore crucial for a successful commercialization of distributed-driven EVs. This paper provides a comprehensive literature review and state-of-the-art vibration-source-analysis and -mitigation methods in IWMs. First, selection criteria are given for IWMs, and a multidimensional comparison for several motor types is provided. The IWM vibration sources are then divided into internally-, and externally-induced vibration sources and discussed in detail. Next, vibration reduction methods, which include motor-structure optimization, motor controller, and additional control-components, are reviewed. Emerging research trends and an outlook for future improvement aims are summarized at the end of the paper. This paper can provide useful information for researchers, who are interested in the application and vibration mitigation of IWMs or similar topics

Robust cascade H infinity control of BLDC motor systems using fixed-structure two degrees of freedom controllers designed via genetic algorithm

A robust control technique is proposed to regulate the current and angular velocity in typical brushless direct current (BLDC) motors. The proposed technique relies on two degree-of-freedom (2-DOF) H infinity control with loop shaping in which the structure of the two controllers and the loop shaping function are pre-specified parametrically (i.e., attain a fixed-structure). This consideration allows for striking a desirable balance between control effectiveness and controllers’ simplicity safeguarding feasibility of practical implementation. It further allows for using standard genetic algorithm (GA) for searching optimal controller parameters. Herein, two 2-DOF fixed-structure H infinity control structures are used in cascade to regulate BLDC motor response in time and in frequency domain subject to internal and external disturbances. Simulation results pertaining to a model of a particular commercial BLDC motor derived through standard system identification demonstrate the applicability and robustness of the proposed control technique to changes to internal BLDC resistance and external BLDC payload. It is shown that the proposed technique is more robust than optimal cascade 1-DOF PID control treated as a special case of the proposed technique