Computational Investigation of Modeling Coupled Optimization Techniques for MCSRM Driving EV

An electromagnetic model of switched reluctance motor (SRM) is required for design process and its analysis. It is desirable to achieve required specification within limited application constraints. Finite element method (FEM) provides an accurate model at the expense of higher computational cost. Magnetic equivalent circuit (MEC) model is a faster technique and requires less computation, but results are less accurate. This paper presents modeling and performance assessment of a mutually coupled switched reluctance motor (MCSRM) for a 2-wheeler electric vehicle (2W-EV) application using a coupled-hybrid modeling technique. Here, in this work, MEC is utilized for initial sizing of motor as a solver. The MEC model is inclusive of magnetic saturation properties and fringing effects in motor parts, and response is taken with relative motion of rotor to enhance precision of non-linear model. The 2-D finite element analysis (FEA) model is executed, and its response is utilized as a reference to Taguchi's assisted MEC model as an optimizer of SRM structure. Computation analysis of hybrid model is done and compared with conventional parametric FEA model and surrogate-assisted FEA model.