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This paper presents system modeling, analysis, and simulation of an electric vehicle (EV) with two independent rear wheel drives. The traction control system is designed to guarantee the EV dynamics and stability when there are no differential gears. Using two in-wheel electric motors makes it possible to have torque and speed control in each wheel. This control level improves EV stability and safety. The proposed traction control system uses the vehicle speed, which is different from wheel speed characterized by a slip in the driving mode, as an input. In this case, a generalized neural network algorithm is proposed to estimate the vehicle speed. The analysis and simulations lead to the conclusion that the proposed system is feasible. Simulation results on a test vehicle propelled by two 37-kW induction motors showed that the proposed control approach operates satisfactorily. Index Terms--Electric vehicle (EV), induction motor, neural networks, speed estimation, traction control.

This paper addresses the problem of controlling power generation in variable-speed wind energy conversion systems (VS-WECS). These systems have two operation regions depending on the wind turbine tip-speed ratio. They are distinguished by minimum phase behavior in one of these regions and a nonminimum phase in the other one. A sliding mode control strategy is then proposed to ensure stability in both operation regions and to impose the ideal feedback control solution despite model uncertainties. The proposed sliding mode control strategy presents attractive features such as robustness to parametric uncertainties of the turbine and the generator as well as to electric grid disturbances. The proposed sliding mode control approach has been simulated on a 1.5-MW three-blade wind turbine to evaluate its consistency and performance. The next step was the validation using the National Renewable Energy Laboratory (NREL) wind turbine simulator called the fatigue, aerodynamics, structures, and turbulence code (FAST). Both simulation and validation results show that the proposed control strategy is effective in terms of power regulation. Moreover, the sliding mode approach is arranged so as to produce no chattering in the generated torque that could lead to increased mechanical stress because of strong torque variations. Index Terms--Power generation control, sliding mode control, wind energy conversion system.

This paper investigates the use of fuzzy logic for fault detection and diagnosis in a pulsewidth modulation voltage source inverter (PWM-VSI) induction motor drive. The proposed fuzzy technique requires the measurement of the output inverter currents to detect intermittent loss of firing pulses in the inverter power switches. For diagnosis purposes, a localization domain made with seven patterns is built with the stator Concordia current vector. One is dedicated to the healthy domain and the six others to each inverter power switch. The fuzzy bases of the proposed technique are extracted from the current analysis of the fault modes in the PWM-VSI. Experimental results on a 1.5-kW induction motor drive are presented to demonstrate the effectiveness of the proposed fuzzy approach. Index Terms--Concordia transform, diagnosis, fault detection, fuzzy logic, induction motor, voltage source inverter (VSI).

This paper describes active fault-tolerant control systems for a high-performance induction-motor drive that propels an electrical vehicle (EV) or a hybrid one (HEV). The proposed systems adaptively reorganize themselves in the event of sensor loss or sensor recovery to sustain the best control performance, given the complement of remaining sensors. Moreover, the developed systems take into account the controller-transition smoothness, in terms of speed and torque transients. The two proposed fault-tolerant control strategies have been simulated on a 4-kW induction-motor drive, and speed and torque responses have been carried to evaluate the consistency and the performance of the proposed approaches. Simulation results, in terms of speed and torque responses, show the global effectiveness of the proposed approaches, particularly the one based on modern and intelligent control techniques in terms of speed and torque smoothness. Index Terms--Automotive application, fault-tolerant control, induction-motor drive.

This paper proposes a strategy to minimize the losses of an induction motor propelling an electric vehicle (EV). The proposed control strategy, which is based on a direct flux and torque control scheme, utilizes the stator flux as a control variable, and the flux level is selected in accordance with the torque demand of the EV to achieve the efficiency-optimized drive performance. Moreover, among EV's motor electric propulsion features, the energy efficiency is a basic characteristic that is influenced by vehicle dynamics and system architecture. For this reason, the EV dynamics are taken into account. Simulation tests have been carried out on a 1.1-kW EV induction motor drive to evaluate the consistency and the performance of the proposed control approach. Index Terms--Direct torque control (DTC), electric vehicle (EV), induction motor, loss minimization.

This paper describes a comparative study allowing the selection of the most appropriate electric-propulsion system for a parallel hybrid electric vehicle (HEV). This paper is based on an exhaustive review of the state of the art and on an effective comparison of the performances of the four main electric-propulsion systems, namely the dc motor, the induction motor (IM), the permanent magnet synchronous motor, and the switched reluctance motor. The main conclusion drawn by the proposed comparative study is that it is the cage IM that better fulfills the major requirements of the HEV electric propulsion. Index Terms--Comparison, electric propulsion, hybrid electric vehicle (HEV).

The aim of this paper is to study the feasibility of fault detection and diagnosis in a three-phase inverter feeding an induction motor. The proposed approach is a sensor-based technique using the mains current measurement. A localization domain made with seven patterns is built with the stator Concordia mean current vector. One is dedicated to the healthy domain and the last six are to each inverter switch. A probabilistic approach for the definition of the boundaries increases the robustness of the method against the uncertainties due to measurements and to the PWM. In high-power equipment where it is crucial to detect and diagnose the inverter faulty switch, a simple algorithm compares the patterns and generates a Boolean indicating the faulty device. In low-power applications (less than 1 kW) where only fault detection is required, a radial basis function (RBF) evolving architecture neural network is used to build the healthy operation area. Simulated experimental results on 0.3- and 1.5-kW induction motor drives show the feasibility of the proposed approach. Index Terms--Concordia transform, fault detection and diagnosis, induction motor, inverter, pattern recognition.

Recently, the permanent-magnet synchronous motor (PMSM) has found widespread utilization in modern adjustable ac drives. This is achieved by using current-controlled voltage source inverter (VSI) systems. Because of its ease implementation, fast current control response and inherent peak current-limiting capability, hysteresis current control is considered as the simplest technique used to control the motor currents for an ac machine. On the other hand, the ramp comparator controller has some advantages, such as limiting maximum inverter switching frequency to the carrier triangular waveform frequency and producing well-defined harmonics. In order to take advantage of the position features of both these two controllers, this paper presents the design and software implementation of a hybrid current controller. The proposed intelligent controller is a simultaneous combination and contribution of the hysteresis current controller and the ramp comparator. Comparisons using simulations on a 0.9-kW PMSM confirm that the proposed hybrid current controller gives better performance and has the advantage of conceptual simplicity. In particular, harmonic spectra of the stator current, obtained using a fast Fourier transform (FFT), are used for comparison purposes. Index Terms--Hybrid controller, hysteresis controller, permanent-magnet synchronous motor, ramp comparator, robustness.

In recent years, marked improvement has been achieved in the design and manufacture of stator winding. However, motors driven by solid-state inverters undergo severe voltage stresses due to rapid switch-on and switch-off of semiconductor switches. Also, induction motors are required to operate in highly corrosive and dusty environments. Requirements such as these have spurred the development of vastly improved insulation material and treatment processes. But cage rotor design has undergone little change. As a result, rotor failures now account for a larger percentage of total induction motor failures. Broken cage bars and bearing deterioration are now the main cause of rotor failures. Moreover, with advances in digital technology over the last years, adequate data processing capability is now available on cost-effective hardware platforms, to monitor motors for a variety of abnormalities on a real time basis in addition to the normal motor protection functions. Such multifunction monitors are now starting to displace the multiplicity of electromechanical devices commonly applied for many years. For such reasons, this paper is devoted to a comparison of signal processing-based techniques for the detection of broken bars and bearing deterioration in induction motors. Features of these techniques which are relevant to fault detection are presented. These features are then analyzed and compared to deduce the most appropriate technique for induction motor rotor fault detection. Index Terms--Induction motor, rotor fault diagnosis, stator current.

The reliability of power electronics systems is of paramount importance in industrial, commercial, aerospace, and military applications. The knowledge about fault mode behavior of an induction motor drive system is extremely important from the standpoint of improved system design, protection, and fault-tolerant control. This paper addresses the application of motor current spectral analysis for the detection and localization of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of induction motors. Intensive research effort has been for some time focused on the motor current signature analysis. This technique utilizes the results of spectral analysis of the stator current. Reliable interpretation of the spectra is difficult since distortions of the current waveform caused by the abnormalities in the induction motor are usually minute. This paper takes the initial step to investigate the efficiency of current monitoring for diagnostic purposes. The effects of stator current spectrum are described and the related frequencies determined. In the present investigation, the frequency signature of some asymmetrical motor faults are well identified using advanced signal processing techniques, such as high-resolution spectral analysis. This technique leads to a better interpretation of the motor current spectra. In fact, experimental results clearly illustrate that stator current high-resolution spectral analysis is very sensitive to induction motor faults modifying main spectral components, such as voltage unbalance and single-phasing effects. Index Terms - Broken bars, damaged bearings, fault detection, induction motors, rotor eccentricity, shaft speed oscillation, single-phasing effects, spectral analysis, stator current, unbalanced voltage.

Finite element modelling of giant magnetostrictive materials (Terfenol-D rods, Tb0.3 Dy0.7 Fe2), is an interesting but difficult task. Not only the coupling between electromagnetic and mechanical phenomena is essential, it is also a full magnetomechanical nonlinear problem. Confronted with the need to come closer to the reality, nonlinear modelling is necessary. For this purpose a variational formulation is proposed to analyze, by using the Finite Element Method (FEM), the giant magnetostriction phenomena, particularly the Joule effect in the magnetostatic case. It is proved, using Euler's and Neumann's theorems, that the proposed model corresponds to the mathematical setting of the magnetomechanical coupled problem. It should be noted that the model is strongly based on static registrations of the magnetic and mechanical properties which are characteristically complex and nonlinear. A solution is then suggested to fit this type of experimental data through the thin sheets bending principle which is more efficient when used for finite element modelling.

This paper is intended as a tutorial overview of induction motors signature analysis as a medium for fault detection. The purpose is to introduce in a concise manner the fundamental theory, main results, and practical applications of motor signature analysis for the detection and the localization of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of induction motors. The paper is focused on the so-called motor current signature analysis which utilizes the results of spectral analysis of the stator current. The paper is purposefully written without 'state-of-the-art' terminology for the benefit of practicing engineers in facilities today who may not be familiar with signal processing. Index Terms--Fault detection, induction motor, motor current signature analysis.

Various applications of artificial neural networks (ANN's) presented in the literature prove that such technique is well suited to cope with online faults diagnosis in induction motors. The aim of this paper is to present a methodology by which induction motors electrical faults can be diagnosed. The proposed methodology is based on the so-called Park's vector approach. In fact, stator current Park's vector patterns are first learned, using ANN's, and then used to discern between 'healthy' and 'faulty' induction motors. The diagnosis process was tested on both classical and decentralized approaches. The purpose of a decentralized architecture is to facilitate a satisfactory distributed implementation of new types of faults to the initial NN monitoring system. The generality of the proposed methodology has been experimentally tested on a 4-kW squirrel-cage induction motor. The obtained results provide a satisfactory level of accuracy, indicating a promising industrial application of the hybrid Park's vector-neural networks approach. Index Terms--Artificial neural networks, diagnosis, induction motor, open phase, Park's vector approach, voltage unbalance.

This paper deals with the broken bars detection in induction motors. The hypothesis on which detection is based is that the apparent rotor resistance of an induction motor will increase when a rotor bar breaks. To detect broken bars, measurements of stator voltages and currents are processed by an extended Kalman filter for the speed and rotor resistance simultaneous estimation. In particular, rotor resistance is estimated and compared with its nominal value to detect broken bars. In the proposed extended Kalman Filter approach, the state covariance matrix is adequacy weighted leading to a better states estimation dynamic. Its main advantage is the correct rotor resistance estimation even for an unloaded induction motor. As part of this estimation process, it is necessary to compensate for the thermal variation in the rotor resistance. Computer simulations, carried out for a 4-kW four-pole squirrel cage induction motor, provide an encouraging validation of the proposed sensorless broken bars detection technique. Index Terms--Broken bars, extended Kalman filter, induction motor, rotor resistance, simultaneous estimation.

In this paper, an effective on-line method for induction motor parameter identification, especially rotor parameters, based on the H-G diagram is presented for motor thermal monitoring purpose. The H-G diagram is established from the analysis of the induction motor measurement of active and reactive power consumption for each operating point. Computer simulations and experimental tests, carried out for a 4 kW four-pole squirrel cage induction motor, provide an encouraging validation of the proposed thermal monitoring technique. The process should be refined for a possible industrial application. Index Terms--H-G diagram, identification, induction motor, rotor parameters, thermal monitoring.