Design Optimization of Switched Reluctance Surface Motor using Finite Element Analysis

This paper presents a procedure for design optimization of 8/6 switched reluctance motor (SRM) using Genetic Algorithm (GA) based random-search optimization. GA is advantageous since it is a highly parallel, randomized and adaptive algorithm with effective global search capability. Selection of dimensions of the motor is based on a constrained multi-objective optimization to achieve the desired performance having higher efficiency with minimum material. Moreover, design of SRM is considered as a highly nonlinear complex problem and is implemented using a 3-dimentional Finite Element Analysis (FEA).