Low adhesion detection and identification in a railway vehicle system using traction motor behaviour

It is important to monitor the wheel-rail friction coefficient in railway vehicles to improve their traction and braking performance as well as to reduce the number of incidents caused by low friction. Model based fault detection and identification (FDI) methods, especially state observers have been commonly used in previous research to monitor the wheel-rail friction. However, the previous methods cannot provide an accurate value of the friction coefficient and few of them have been validated using experiments. A Kalman filter based estimator is proposed in this research project. The developed estimator uses signals from the traction motor and provides a new and more efficient approach to monitoring the condition of the wheel-rail contact condition. A 1/5 scaled test rig has been built to evaluate the developed method. This rig comprises 2 axle-hung induction motors driving both the wheelsets of the bogie through 2 pairs of spur gears. 2 DC generators are used to provide traction load to the rollers through timing pulleys. The motors are independently controlled by 2 inverters. Motor parameters such as voltage, current and speed are measured by the inverters. The speed of the wheel and roller and the output of the DC generator are measured by incremental encoders and Hall-effect current clamps. A LabVIEW code has been designed to process all the collected data and send control commands to the inverters. The communication between the PC and the inverters are realized using the Profibus (Process Field Bus) and the OPC (Object Linking and Embedding (OLE) for Process Control) protocol. 3 different estimators were first developed using computer simulations. Kalman filter and its two nonlinear developments: extended Kalman filter (EKF) and unscented Kalman filter (UKF) have been used in these 3 methods. The results show that the UKF based estimator can provide the best performance in this case. The requirement for measuring the roller speed and the traction load are also studied using the UKF. The results show that it is essential to measure the roller speed but the absence of the traction load measurement does not have significant impact on the estimation accuracy. A re-adhesion control algorithm, which reduces excessive creepage between the wheel and rail, is developed based on the UKF estimator. Accurate monitoring of the friction coefficient helps the traction motor work at its optimum point. As the largest creep force is generated, the braking and accelerating time and distance can be reduced to their minimum values. This controller can also avoid excessive creepage and hence potentially reduce the wear of the wheel and rail. The UKF based estimator development has been evaluated by experiments conducted on the roller rig. Three different friction conditions were tested: base condition without contamination, water contamination and oil contamination. The traction load was varied to cover a large range of creepage. The importance of measuring the roller speed and the traction load was also studied. The UKF based estimator was shown to provide reliable estimation in most of the tested conditions. The experiments also confirm that it is not necessary to measure the traction load and give good agreement with the simulation results. With both the simulation and experiment work, the UKF based estimator has shown its capability of monitoring the wheel-rail friction coefficient

Functional observers for motion control systems

This paper presents a novel functional observer for motion control systems to provide higher accuracy and less noise in comparison to existing observers. The observer uses the input current and position information along with the nominal parameters of the plant and can observe the velocity, acceleration and disturbance information of the system. The novelty of the observer is based on its functional structure that can intrinsically estimate and compensate the un-measured inputs (like disturbance acting on the system) using the measured input current. The experimental results of the proposed estimator verifies its success in estimating the velocity, acceleration and disturbance with better precision than other second order observers

An implementation of rotor speed observer for sensorless induction motor drive in case of machine parameter uncertainty

The paper describes observers using model reference adaptive system for sensorless induction motor drive with the pulse width modulator and the direct torque control under the circumstances of incorrect information of induction motor parameters. An approximation based on the definition of the Laplace transformation is used to obtain initial values of the parameters. These values are utilized to simulate sensorless control structures of the induction motor drive in Matlab-Simulink environment. Performance comparison of two typical observers is carried out at different speed areas and in presence of parameter uncertainty. A laboratory stand with the induction motor drive and load unit is set up to verify the properties of observers. Experimental results confirm the expected dynamic properties of selected observer

Development of Urban Electric Bus Drivetrain

The development of the drivetrain for a new series of urban electric buses is presented in the paper. The traction and design properties of several drive variants are compared. The efficiency of the drive was tested using simulation calculations of the vehicle rides based on data from real bus lines in Prague. The results of the design work and simulation calculations are presented in the paper

A new functional observer to estimate velocity, acceleration and disturbance for motion control systems

This paper presents a novel functional observer which can observe the velocity, acceleration and disturbance information of a motion control system with higher accuracy and less noise in comparison to classical observers. The observer uses the input current and position information and the nominal parameters of the plant. The novelty of the observer is based on its functional structure that can intrinsically estimate and compensate the un-measured inputs (like disturbance acting on the system) using the measured input current. The experimental results of the proposed estimator verifies its success in estimating the velocity, acceleration and disturbance with better precision than classical observers

Flux observer algorithms for direct torque control of brushless doubly-fed reluctance machines

Direct Torque Control (DTC) has been extensively researched and applied to most AC machines during the last two decades. Its first application to the Brushless Doubly-Fed Reluctance Machine (BDFRM), a promising cost-effective candidate for drive and generator systems with limited variable speed ranges (such as large pumps or wind turbines), has only been reported a few years ago. However, the original DTC scheme has experienced flux estimation problems and compromised performance under the maximum torque per inverter ampere (MTPIA) conditions. This deficiency at low current and torque levels may be overcome and much higher accuracy achieved by alternative estimation approaches discussed in this paper using Kalman Filter (KF) and/or Sliding Mode Observer (SMO). Computer simulations accounting for real-time constraints (e.g. measurement noise, transducer DC offset etc.) have produced realistic results similar to those one would expect from an experimental setup

A Novel PMSM Hybrid Sensorless Control Strategy for EV Applications Based on PLL and HFI

In this paper, a novel hybrid sensorless control strategy for Permanent Magnet Synchronous Machine (PMSM) drives applied to Electric Vehicles (EV) is presented. This sensorless strategy covers the EV full speed range and also has speed reversal capability. It combines a High Frequency Injection (HFI) technique for low and zero speeds, and a Phase-Locked Loop (PLL) for the medium and high speed regions. A solution to achieve smooth transitions between the PLL and the HFI strategies is also proposed, allowing to correctly detect the rotor position polarity when HFI takes part. Wide speed and torque four-quadrant simulation results are provided, which validate the proposed sensorless strategy for being further implemented in EV.Peer ReviewedPostprint (author's final draft

On extended Kalman filters with augmented state vectors for the stator flux estimation in SPMSMs

The demand for highly dynamic electrical drives, characterized by high quality torque control, in a wide variety of applications has grown tremendously during the past decades. Direct torque control (DTC) for permanent magnet synchronous motors (PMSM) can provide this accurate and fast torque control. When applying DTC the change of the stator flux linkage vector is controlled, based on torque and flux errors. As such the estimation of the stator flux linkage is essential. In the literature several possible solutions for the estimation of the stator flux linkage are proposed. In order to overcome problems associated with the integration of the back-emf, the use of state observers has been advocated in the literature. Several types of state observers have been conceived and implemented for PMSMs, especially the Extended Kalman Filter (EKF) has received much attention. In most reported applications however the EKF is only used to estimate the speed and rotor position of the PMSM in order to realize field oriented current control in a rotor reference frame. Far fewer publications mention the use of an EKF to estimate the stator flux linkage vector in order to apply DTC. Still the performance of the EKF in the estimation of the stator flux linkage vector has not yet been thoroughly investigated. In this paper the performance of the EKF for stator flux linkage is studied and simulated. The possibilities to improve the estimation by augmenting the state vector and the consequences of these alterations are explored. Important practical aspects for FPGA implementation are discussed