A novel approach for characterization and optimization of ALA rotor synchronous reluctance motor drives for traction applications

An integrated team artificial intelligence electromagnetic, T-AI-EM environment is developed to accurately determine the performance characteristics of synchronous reluctance machines (SynRM) with axially laminated anisotropic (ALA) rotor configurations. This T-AI-EM approach is also used to develop an optimum design of SynRM for traction applications. The main objective of this optimization is to minimize the torque ripple while maximizing the output torque to ensure high acceleration and smooth operation. Furthermore, this model will enable the SynRM motor to easily interact in real time with the control unit of the hybrid-electric vehicle, HEV. The T-AI-EM is composed of two main blocks. The first block 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.