Speed Sensorless Induction Motor Drive Control for Electric Vehicles

Fast diminishing fossil fuel resources, deterioration in air quality and concerns for environmental protection, continuously promote the interest in the research and development of Alternative Energy Vehicles (AEVs). Traction motor drive is an integral part and common electric propulsion system in all kinds of AEVs. It plays an utmost significant role in the development of electrified transport industry. Application of Induction Motor (IM) drive is not only limited to the domestic and industrial applications but also has an ubiquitous influence in the modern electrified transport sector. IM is characterized by a simple and rugged structure, operational reliability, low maintenance, low cost, ability to operate in a hostile environment and high dynamic performance. However, IM is one of the widely accepted choices by Electric Vehicles (EVs) manufacturer. At present, Variable speed IM drive is almost replacing the traditional DC motor drive in a wide range of applications including EVs where a fast dynamic response is required. It became possible after the technological advancement and development in the field of power switching devices, digital signal processing and recently intelligent control systems have led to great improvements in the dynamic performance of traction drives. Speed Sensorless control strategies offer better system’s reliability and robustness and reduce the drive cost, size and maintenance requirements. Sensorless IM drives have been applied on medium and high speed applications successfully. However, instability at low speed and under different load disturbance conditions are still a critical problem in this research field and has not been robustly achieved. Some application such as traction drives and cranes are required to maintain the desired level of torque down to low speed levels with uncertain load torque disturbance conditions. Speed and torque control is more important particularly in motor-in-wheel traction drive train configuration EVs where vehicle wheel rim is directly connected to the motor shaft to control the speed and torque. The main purpose of this research is to improve the dynamic performance of conventional proportional-integral controller based model reference adaptive system (PI-MRAS) speed observer by using several speed profiles under different load torque disturbance conditions, which is uncertain during the whole vehicle operation apart from the vehicle own load. Since, vehicle has to face different road conditions and aerodynamic effects which continuously change the net load torque effect on the traction drive. This thesis proposes different novel methods based on the fuzzy logic control (FLC) and sliding mode control (SMC) with rotor flux MRAS. Numerous simulations and experimental tests designed with respect to the EV operation are carried out to investigate the speed estimation performance of the proposed schemes and compared with the PI-MRAS speed observer. For simulation and experimental purpose, Matlab-Simulink environment and dSPACE DS-1104 controller board are used respectively. The results presented in this thesis show great performance improvements of the proposed schemes in speed estimation & load disturbance rejection capability and provide a suitable choice of speed sensoless IM drive control for EVs with cost effectiveness

Speed -Sensorless Estimation And Position Control Of Induction Motors For Motion Control Applications

Thesis (Ph.D.) University of Alaska Fairbanks, 2006High performance sensorless position control of induction motors (IMs) calls for estimation and control schemes which offer solutions to parameter uncertainties as well as to difficulties involved with accurate flux and velocity estimation at very low and zero speed. In this thesis, novel control and estimation methods have been developed to address these challenges. The proposed estimation algorithms are designed to minimize estimation error in both transient and steady-state over a wide velocity range, including very low and persistent zero speed operation. To this aim, initially single Extended Kalman Filter (EKF) algorithms are designed to estimate the flux, load torque, and velocity, as well as the rotor, Rr' or stator, Rs resistances. The temperature and frequency related variations of these parameters are well-known challenges in the estimation and control of IMs, and are subject to ongoing research. To further improve estimation and control performance in this thesis, a novel EKF approach is also developed which can achieve the simultaneous estimation of R r' and Rs for the first time in the sensorless IM control literature. The so-called Switching and Braided EKF algorithms are tested through experiments conducted under challenging parameter variations over a wide speed range, including under persistent operation at zero speed. Finally, in this thesis, a sensorless position control method is also designed using a new sliding mode controller (SMC) with reduced chattering. The results obtained with the proposed control and estimation schemes appear to be very compatible and many times superior to existing literature results for sensorless control of IMs in the very low and zero speed range. The developed estimation and control schemes could also be used with a variety of the sensorless speed and position control applications, which are challenged by a high number of parameter uncertainties

Application of neural networks in the control of induction motor drives

Náplní disertační práce je problematika užití umělých neuronových sítí v oblasti řízení elektrických pohonů. Práce se konkrétně soustředí na použití neuronových sítí v systémech pro odhad stavových veličin asynchronního motoru. Byly realizovány čtyři systémy bezsenzorového vektorového řízení, které využívají offline trénovanou dopřednou neuronovou síť. První řešení využívá neuronovou síť přímo k výpočtu mechanické úhlové rychlosti, rotorový tok je následně určen pomocí proudového modelu. Druhé řešení je založeno na použití pozorovatele rychlosti RF-MRAS, kde je neuronová síť použita na místě referenčního modelu a nahrazuje tak napěťový model, tím je odstraněn problém s otevřenou integrací. Největší pozornost byla věnována pozorovateli CB-MRAS. Byly navrženy dvě nové modifikace CB-MRAS s neuronovou sítí na místě proudového estimátoru. Experimentální výsledky ukazují zlepšení přesnosti a stability CB-MRAS v generátorickém režimu. Ověření bylo provedeno měřením na experimentálním pohonu, který je vybaven 2,2 kW asynchronním motorem a řídicím systémem s digitálním signálovým kontrolérem TMS320F28335. Za účelem práce s neuronovými sítěmi byl řídicí systém rozšířen o komunikační rozhraní, které umožňuje sběr dat potřebných pro návrh a testování neuronových sítí. Pro všechny realizované metody experimentální výsledky ukazují vysokou míru přesnosti v oblasti nízkých otáček.This doctoral thesis deals with the use of artificial neural networks in the field of control of electric drives. In particular, the thesis focuses on the application of neural networks in systems intended for estimation of the state variables of an induction motor. Four sensorless vector control schemes have been implemented, in which an offline-trained feedforward neural network is utilized. The first solution uses a neural network which directly provides the estimated mechanical angular speed, the rotor flux is determined using the current model. The second solution is based on the use of the RF-MRAS speed observer, in this case, a neural network is used in the place of the reference model, it replaces the voltage model, thereby the problem of pure integration is eliminated. The main focus was on the CB-MRAS observer. Two new modifications of CB-MRAS with a neural network in the place of the current estimator have been proposed. The experimental results show an improvement in the accuracy and stability of CB-MRAS in the regenerating mode. The verification was performed employing an experimental drive equipped with a 2.2 kW induction motor and controlled by a control system which is based on the TMS320F28335 digital signal controller. In order to work with neural networks, the control system has been extended with a communication interface that allows the collection of data needed for designing and testing neural networks. For all implemented methods, the obtained results show a high level of accuracy in the low speed range.430 - Katedra elektronikyvyhově

Industrial Applications: New Solutions for the New Era

This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section