Khwaja M. Rahman

Advantages of switched reluctance motor applications to EV and HEV: design and control issues

The purpose of this paper is to investigate the capabilities of switched reluctance motors (SRMs) for EV and HEV applications. This investigation is carried out in two steps. The first step involves the machine design and the finite element analysis to obtain the static characteristic of the motor. In the second step, the finite element field solutions are used in the development of a nonlinear model to investigate the dynamic performance of the designed motor. Several 8-6 and 6-4 SRM geometries are investigated. The effects of different stator and rotor pole widths and pole heights on the steady state as well as on the dynamic performance of the motor are studied. Simulation results of the designed SRM are presented for vehicle acceleration. To demonstrate the SRMs capability in producing an extended constant power range, experimental results are presented; however, for a reduced size motor available commercially.

Online Minimum-Copper-Loss Control of an Interior Permanent-Magnet Synchronous Machine for Automotive Applications

This paper presents an algorithm to calculate the current references on line, considering the inherent nonlinear nature of the saturation effect in an interior permanent-magnet synchronous machine for the minimum-copper-loss control under the current and voltage limit of the drive system. This paper basically approaches this issue as a nonlinearly constrained optimization problem where the torque command imposes the nonlinear equality constraint and the voltage limit imposes the nonlinear inequality constraint. Depending on the operating region, it solves the corresponding set of nonlinear equations in real time derived from the Lagrange multiplier method. Newtons method among various techniques is adopted to implement the numerical solution. This scheme gives accurate results not only in motoring but also in generating operation of the machine, since the voltage drop of the stator resistance is taken into account, which is hardly applicable to a two-dimensional lookup table where the inputs are the torque command and the maximum flux amplitude, and the output is each axis current reference in the rotor reference frame. The simulation and experimental results show the feasibility and performance of the proposed technique

Optimized torque control of switched reluctance motor at all operational regimes using neural network

Torque in a switched reluctance motor (SRM) at low speed is controlled by PWM chopping of current. The controller provides the appropriate reference current, the phase turn-on, and the turn-off angles based on the torque demand and the motor speed. While the torque at high speed is controlled by the single pulse operation of the current. The controllable parameters at high speeds are the phase turn-on and the turn-off angles. The controller is responsible for providing the appropriate turn-on and the turn-off angles. The complexity in the control of SRM arises from the fact that the SRM operation is highly nonlinear. By design SRM operates in the saturation region in almost all operational points. This results in the nonlinearity of the SRM magnetic field. To find the appropriate control parameters at low as well as at high speeds, an accurate nonlinear model of the SRM is, therefore, needed. In this paper such a dynamic model of SRM is developed. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed control scheme.

Mitigation of acoustic noise and vibration in switched reluctance motor drive using neural network based current profiling

In order to optimize the performance of switched reluctance motor (SRM) drives, geometrical design of the motor as well as control strategies for its power converter should be adjusted. High levels of torque ripple and acoustic noise are the main drawbacks of SRM drives in many applications. This paper mainly focuses on control strategies to be used for minimizing the acoustic noise and vibration in SRM drives. Further, the major sources of acoustic noise and vibration have been addressed. In addition, the significance of various control variables (control angles and current profile) has been studied in detail. Furthermore, based on this analysis, a current shape optimization method has been introduced. Extensive simulations are given to support the proposed control strategies. Moreover, the impact of the noise reduction algorithm on the efficiency of the drive has been explored. In addition, artificial neural networks have been employed to generate optimized current profiles at various operating points. Finally, experimental results for an 8/6, 0.3 kW SRM drive are given.

Inductance based position encoding for sensorless SRM drives

In this paper, a sensorless control scheme for switched reluctance motor (SRM) drives based on an inductance model is presented. The proposed sensorless method requires only active phase terminal measurements. It does not require external hardware and hence the reliability of the scheme is expected to be good while not adding to the system cost. Preliminary investigation shows that the proposed sensorless scheme can give position information with high resolution for speeds from standstill to several times the base speed of the motor. In this paper, the theoretical aspects of the proposed sensorless scheme are first described. Detailed simulation of the sensorless scheme using Matlab-Simulink is carried out and a comprehensive set of simulation results are presented in order to show the feasibility of the proposed method. Hardware aspects along with the implementation details of the sensorless scheme are discussed briefly.

Rotor time constant updating scheme for a rotor flux oriented induction motor drive

This paper presents a simple online rotor time constant identification scheme of an indirect field-oriented induction motor for the purpose of improving the performance and robustness of the drive. The proposed technique neither requires any special test signal nor any complex computations. This scheme is based on a special switching technique of the current-regulated pulsewidth modulation (CRPWM) voltage-source inverter (VSI), which allows measuring the induced voltage across the stator phase. The rotor time constant is then identified directly from this measured voltage. This proposed technique provides six windows within one electric cycle to update the rotor time constant, which should be sufficient for all practical purposes. Simulated results followed by experiments are presented to validate the effectiveness of the proposed technique.

Optimized instantaneous torque control of switched reluctance motor by neural network

The switched reluctance motor (SRM), owing to its doubly salient pole structure and to its operation in the saturation region, has a highly nonlinear torque characteristics. Therefore, a linear model of a SRM can predict its performance characteristics only for a limited range of operation. An accurate and comprehensive nonlinear model of SRM, however, is extremely complicated and is computationally intensive to be implemented in real time for control purposes. To alleviate some of these difficulties, several simplified models are presented in the literature. These models, however, lack accuracy. Artificial neural networks (ANNs) have been used successfully in the control of nonlinear dynamic systems. This paper presents an ANN based torque control scheme of SRM which generates optimal current profiles to minimize torque pulsation. Theoretically, it is found that for each speed and torque below base speed there are several current profiles which produce the desired torque without any pulsation. This method finds the one which gives the maximum torque per ampere. Unlike the other ANN based nonlinear modeling of SRM presented in the literature which uses static magnetization data for training, the training data for the ANN in the proposed method is obtained from the simulation of a dynamic model of the SRM. The proposed control scheme, therefore, controls torque on an instantaneous basis, thus allowing torque control even during the dynamic operation of the motor. In order to include the effect of the nonlinearity, the dynamic model of the SRM uses static magnetization data generated experimentally. Operation of the SRM from zero speed to the base speed is considered. Simulation results of the optimal torque control scheme are presented. To validate the applicability of the proposed technique, the result of the ANNs will is compared with experimentally measured results.

Evaluation of soft switching for EV and HEV motor drives

Soft switching has the potential of reducing switch stresses and of lowering the switching losses as compared to hard switching. To understand the effectiveness of the soft-switching technique, when applied to electric vehicle (EV) and hybrid electric vehicle (HEV) systems, it may be necessary to first evaluate their system requirements and performance. This evaluation process would require knowledge of the vehicle dynamics. The vehicle load requires a special torque-speed profile from the drivetrain for minimum power ratings to meet the vehicles operational constraints, such as initial acceleration and gradability. The selection of motor and its control for EV and HEV applications are dictated mainly by this special torque-speed requirement. As a consequence, this requirement will have a strong influence on the converter operation. This paper makes an attempt to evaluate EV and HEV running in both standard Federal Test Procedure 1975 city driving and highway driving cycles. A simplified analysis is carried out for several of the most commonly used electric motors operating on the optimal torque-speed profile. Special attention is given to the converter conduction and switching losses, by analyzing the switching losses, and by assuming that an ideal soft-switching scheme will have zero switching losses, one can evaluate the improvement in the system efficiency if a soft-switching control is used. The relative significance of soft switching for EV and HEV systems is then established.

High performance digital PI current regulator for EV switched reluctance motor drives

This paper presents a design methodology for digital PI current regulators that may be used for the highly nonlinear switched reluctance motor control. The important nonlinear behavior of saturation, back EMF, and mutual coupling are accounted for to achieve consistent current regulator performance over the entire operating regime. Gain adaptation is used with respect to both position and current to ensure stability. An improved back EMF decoupling scheme is implemented to reduce bandwidth requirements. The proposed control is implemented on a high torque traction drive for EV applications. Simulation and experimental results demonstrate excellent performance over the entire operating regime.

Improvement of hysteresis control in switched reluctance motor drives

Hysteresis control is commonly used in switched reluctance motor (SRM) drives, operating in the low speed region, for limiting the current to the desired value. Not much attention has been paid in the literature to the effect of the size of the hysteresis current band on the performance of the SRM drive. This paper analyses the effects of the size of the hysteresis band on the machine performance and presents a new improved hysteresis control to improve the drive efficiency for low speed operation of SRM. The size of the hysteresis band has significant effect on the switching losses, conduction losses, core losses and torque ripple. Theoretical analysis, aided by computer simulations are employed in this study to determine the optimal current band size for different speeds in the low speed operation of SRM. It is expected that implementation of this algorithm in high power SRM drives, operating at low speeds, will improve the overall drive efficiency to a significant extent.