Abdul-Rahman A. Arkadan

Particle Swarm Design Optimization of ALA Rotor SynRM for Traction Applications

Particle swarm optimization (PSO) algorithm is applied to the design optimization problem of axially laminated anisotropic (ALA) rotor synchronous reluctance motor (SynRM) drive. The objective of the optimization is to maximize the developed torque while minimizing the torque ripple as well as the ohmic and core losses for traction applications. The number of flux paths, stator tooth width, and rotor flux path width define the 3-D search space for the optimization problem. An artificial intelligence modeling approach utilizing PSO and finite element-state space (FE-SS) models is used for the characterization and design optimization of a prototype ALA rotor SynRM drive for traction applications.

Computer-Aided Modeling of a Rectified DC Load-Permanent Magnet Generator System with Multiple Damper Windings in the Natural Abc Frame of Reference

In this paper, a computer-aided modeling method, by which one can analyze and predict the dynamic performance of electronically rectified load-permanent magnet generator systems with multiple damping circuits is presented. Perpetual electronic switching in such systems results in a continuous change in the machine system network topologies. Hence, network modeling of such systems was done here on an instantaneous basis in the time domain. The natural abc frame of reference was used throughout. An advantage of using this approach is that it enables one to directly use readily available abc machine parameters obtained from magnetic field solutions. Thus, the inherent nonlinearities and space harmonics in the flux linkages, inductances, as well as induced emfs are fully accounted for in this modeling and analysis approach. This method was applied to a two-pole, 75 kVA, 208 V, 24000 r/ min permanent magnet generator-3 phase full wave rectifier load system, Figure (1). The resulting model was used to study the generator-load system performance. Details of developing this model are given in the paper. Accordingly, by using generalized concepts of network graph theory in conjunction with hybrid matrix formulation of nonlinear networks, see references [5] and [9] in the paper, the state equations associated with this system were automatically formulated and continuously updated in a computer-aided network solution program.

Particle Swarm Optimization of coupled electromechanical systems

This work investigates the feasibility of utilizing an electromagnetic-team fuzzy logic (EM-TFL) robust identifier for use with the particle swarm optimization technique to increase the efficiency and fuel economy of a hybrid electric vehicle (HEV) powertrain system in series configuration. This optimization necessitates the characterization of the key electromechanical components of the hybrid electric powertrain system which includes a PM generator and an electric motor drive system. The basic objective of improving the fuel economy while maintaining the performance of the vehicle is met through the implementation of a particle swarm optimization algorithm.

Design Optimization of ALA Rotor SynRM Drives using T-AI-EM Environment

An integrated team-artificial intelligence-electromagnetic, T-AI-EM, environment is developed to accurately determine the performance characteristics of synchronous reluctance motors (SynRM) with axially laminated anisotropic (ALA) rotor configurations. This T-AI-EM is used to train a fuzzy logic system that predicts the optimal solution of the machine for any given input torque. The main objective of this optimization is to minimize the torque ripple corresponding to a given torque-load condition. The T-AI-EM is composed of two main blocks. The first consists of electromagnetic module utilizing indirectly coupled finite element state space (FE-SS) model. The second consists of an AI based model inspired from team member concept, that consists of several adaptive network fuzzy inference systems, ANFISs, supervised by a radial based network, RBN

Optimal Control Strategy for Hybrid Electric Vehicle Powertrain

An optimal control strategy is developed for the powertrain of hybrid electric vehicles (HEVs) to minimize the fuel consumption while maintaining good performance and drivability. A novel robust identifier using the generalized notion of power approach is developed and used to accurately predict the performance characteristics of the powertrain. The uniqueness of this model is its ability to represent both internal combustion engine (ICE) unit and electrical motor generator (EMG) unit of the powertrain system in one state space system of equations. Thus, it allows proper characterization of the interaction of both units considering the irreversibility of the entropy generation by the ICE unit, load small disturbances, and magnetic material nonlinearity as well as space and time harmonics of the EMG unit. The optimal control strategy is developed in two stages. The first uses a mathematical search algorithm based on the calculus of variation to minimize fuel consumption and develop needed torque to achieve a good performance. The second stage employs a fuzzy logic algorithm to ensure good drivability (comfort). The superiority of the optimization algorithm is demonstrated by applying it to a prototype HEV with split power configuration and by comparing simulation results with readily available benchmark data.

Modeling faulted switched reluctance motors using evolutionary neural networks

The work presented examines the feasibility of using artificial neural networks (ANNs) and evolutionary algorithms (EAs) to model fault free and faulted switched reluctance motor (SRM) drive systems. SRMs are capable of functioning despite the presence of faults. Faults impart transient changes to machine inductances in a manner that is difficult to model analytically. After this transient period, SRMs are capable of functioning at a reduced level of performance. ANNs are applied for their well known interpolation capabilities for highly nonlinear systems. EAs are employed for their ability to search a complex structural and parametric space as necessary to find good ANN solutions. In this paper, the ANN structure and training regimen are described for application to an example SRM drive system under normal and abnormal operating conditions.<<ETX>>

EM-TFL identification for Particle Swarm Optimization of HEV powertrain

The feasibility of developing a design optimization environment utilizing an Electromagnetic-Team Fuzzy Logic, EM-TFL, robust identifier for use with Particle Swarm Optimization, PSO, technique is investigated. The developed environment is applied in a case study to increase the efficiency and fuel economy of a prototype Hybrid Electric Vehicle, HEV, powertrain in series configuration. This optimization necessitates the characterization of the key electromechanical components of the HEV powertrain system which includes a generator, an electric motor drive system, and a battery pack in addition to an Internal Combustion Engine, ICE. The basic objective of improving the fuel economy while maintaining the performance of the vehicle is met through the implementation of a PSO algorithm.

Design Optimization of SynRM Drives for HEV Power Train Applications

This work investigates the feasibility of utilizing a team artificial intelligence-electromagnetic, TAI-EM, environment for the characterization and design optimization of synchronous reluctance motors, SynRM, with axially laminated anisotropic, ALA, rotor configurations. The main objective of this optimization is to minimize the torque ripple, as well as Ohmic and core losses at a given torque-speed condition. This environment is applied for the characterization and design optimization of a prototype 100 KW, 6000 rev/min ALA rotor SynRM drive system for traction applications. The TAI-EM environment resulted in an optimized machine design. The results are verified by comparing major performance indices of the predicted optimized design to those obtained from the prototype measurements.

Characterization of Stand Alone AC Generators During No-Break Power Transfer Using Radial Basis Networks

This paper describes the use of an artificial intelligence-electromagnetic modeling approach for the performance prediction of stand alone synchronous generators during no break power transfer (NBPT) operating conditions. This approach uses radial basis networks (RBNs), which have the advantage of not being locked into local minima as could do feedforward neural networks. The RBNs are simply linear function approximators that use radial basis functions which are powerful techniques for interpolation in multidimensional space. The RBN is used to evaluate the stresses accompanying this mode of operation which may result in the failure of the diodes in the rotating rectifier bridge of the generator brushless field exciter. The modeling approach is applied in a case study of two standalone synchronous generators system for aerospace applications. This study resulted in the prediction of the system performance characteristics including the peak currents and reverse voltages of the rotating diodes. The simulation results were validated by comparison to experimental data

EM-TFL environment for the design optimization of electromagnetic launchers

An integrated electromagnetic team fuzzy logic, (EM-TFL) environment is developed for the characterization and design optimization of EM launchers (EML). In addition, a novel technique that uses FL membership functions to model the conflicting objective functions in multiobjective design optimization problems is also presented. The results of a case study involving a multistage EML are presented.