M. Ehsani

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.

A novel position sensor elimination technique for the interior permanent-magnet synchronous motor drive

An approach to the position sensor elimination of an interior permanent-magnet (IPM) synchronous motor drive is proposed. Analytical equations are developed for the calculation of the phase inductance of an IPM motor driven by a current controlled pulse-width modulation (PWM) converter with a hysteresis controller. The calculated phase inductance is then used to estimate the position of the rotor using a set of stored data relating the phase inductance and the rotor position. In order to obtain an unambiguous relation between the phase inductance and the rotor position, the phase inductance of the three phases is calculated during different segments of each electrical cycle. A direct approach to inductance calculation is also proposed which is computationally less intensive and gives uniformly good results. Simulation results are presented and it is shown that the position error is very small for speed control applications. A current regulator using constant frequency switching is also analyzed.<<ETX>>

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.

Overview of reduced parts converter topologies for AC motor drives

In this paper, various reduced parts converter topologies and control strategies for power factor correction and motor control are reviewed and systematic design methodology is developed. From this investigation, the converter topologies could be mainly categorized into cascade type and unified type. The detailed operational principles are examined and the performance comparison Is derived to illustrate merits and limitations of the converters. Simulation results are provided to help the better understanding of the theoretical description and several experimental results are presented on prototype induction motor and brushless DC (BLDC) motor drives, along with cascade and unified type converters.

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.

Design and control characterization of switched reluctance generator for maximum output power

Maximization of power throughput of switched reluctance generator has been addressed in this paper. Phase self-inductance, phase voltage switching angles, DC bus voltage level and shaft speed of the generator have been identified as design parameters and control variables affecting output power. Depending on the application each one can be defined as either design parameter or control variable. Due to highly nonlinear characteristics of switched reluctance generator, there is no analytical equation for output power in terms of design parameters and control variables. So, iterative simulation of the generator model on the range of design parameters and control variables has been used for finding output power profile. Since it is a multidimensional search space, the number of iterations is very big. Therefore, we have used theoretical knowledge about the generator and practical limitations to narrow the search space and reduce simulation time. Output power profile of a 1hp generator has been presented from simulation results. These results have been verified by experimental measurements on a test-bed in the lab. The results give the performance of switched reluctance generator for the range of machine parameters and control variables. They show the existence of optimal output power and give guidance for design of the generator and its controller.

Advanced BLDC motor drive for low cost and high performance propulsion system in electric and hybrid vehicles

In this paper, the authors propose an advanced brushless DC motor (BLDCM) drive for low cost and high performance electric propulsion system in electric vehicles (EVs) and hybrid electric vehicles (HEVs). It includes reduced parts power converter topologies and an optimal PWM control strategy to produce the desired dynamic and static speed and torque characteristics. The theoretical explanation and operational principle are described in detail, and the performance of the proposed low cost BLDCM drive is compared with its conventional counterpart by informative simulation results. An IGBT inverter with high speed DSP (TI TMS320 F243) is also built to provide experimental results.

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.

Dynamic modeling of nonlinear SRM drive with Pspice

In this paper, a comprehensive nonlinear dynamic model for a switched reluctance motor (SRM) drive using Pspice is presented. The SRM is represented by its nonlinear dynamic equations and the magnetic model of SRM includes the representation of inductance-current-position characteristics which closely match those obtained experimentally. The variation of the phase inductance with rotor position is expressed by a limited number of Fourier series terms. The coefficients of the Fourier series are determined by the values of the inductance at the aligned position, unaligned position and a position midway between the two. The nonlinear relationship between the phase inductance and the current is represented by polynomial functions whose coefficients are derived by static characteristics obtained from finite element analysis or experimental results. Since Pspice is a circuit oriented package, any type of converter and control scheme can be modeled with ease and hence the component ratings can be selected based on simulation results. Further, any type of converter/motor faults as well as phase asymmetries can also be simulated. It is also possible to simulate various control strategies for low and higher speed operations and obtain optimum control angles. The details of the developed model and the simulation results obtained for a 300 W, 12 V, 8/6 SRM drive in various operating regions are presented in this paper. To validate the model, the simulation results are compared with experimental and finite element analysis results.