Switched reluctance motor design using neural-network method with static finite-element simulation

The paper describes a neural network method for optimal design of a switched reluctance motor (SRM). The approach maximizes average torque while minimizing torque ripple, considering mainly the stator and rotor geometry parameters. Before optimization takes place, an experimental validation of the SRM model, based on the finite-element method, is performed. The validation predicts average torque and torque ripple characteristics for several motor configurations while stator and rotor pole arcs are varied. The numerical results are highly nonlinear, and a function approximation of the data is therefore difficult to implement. We therefore interpolate the data by using a neural network based on a generalized radial basis function. The computed results allow us to search for optimum motor parameters. The optimum design was confirmed by numerical field solutions.