Bhaktha, B.S.Jose, N.Vamshik, M.Pitchaimani, J.Gangadharan, K.V.2026-02-042024IEEE Transactions on Transportation Electrification, 2024, 10, 4, pp. 9959-9974https://doi.org/10.1109/TTE.2024.3370401https://idr.nitk.ac.in/handle/123456789/21446This article deals with the design and optimization of a 2 kW switched reluctance motor (SRM) for an electric rickshaw (E-rickshaw). Previously published research on SRM optimization has mostly focused on the optimization of their design and control variables only at the rated conditions. In electric vehicle (EV) applications, the load operating points (LOPs) of a traction motor are dynamic and spread widely across the torque speed envelope. To enhance their overall performance, it is vital to include them in the design optimization process; therefore, in this article, a novel procedure for implementing the multiobjective design optimization (MODO) of an SRM based on a driving cycle has been demonstrated. Higher starting torque and torque density with reduced electromagnetic losses throughout the driving cycle are established as the design objectives, subject to practical restrictions on current density and slot fill factor. The design objectives have been accurately evaluated through transient finite element analysis (FEA) and a computationally efficient SRM drive model (developed in MATLAB/Simulink) with consideration of the excitation control parameters. Kriging models have been constructed to reduce the computation cost of FEA during the optimization process. Then, a nondominated sorting genetic algorithm II (NSGA II) based multiobjective optimization coupled with the constructed Kriging models is conducted to generate a Pareto front. An optimal design that offers the best balance between the design objectives is selected from the Pareto-optimal set, and the dimensions of corresponding design variables are used to build a prototype. Finally, the static and dynamic performance of the SRM prototype are experimentally evaluated and validated with the FEA simulations. © 2024 IEEE.Electric loadsElectric tractionFinite element methodGenetic algorithmsInterpolationMATLABMultiobjective optimizationPareto principleTorqueTraction motors<italic xmlns:ali="> -mean algorithmComputational modellingDesign optimizationDriving cycleDriving cycle-based design optimizationElectric rickshawFinite element analyseOptimisationsPrototypePrototype machineSwitched reluctance motorSwitched Reluctance Motor - SRMXmlns:mml="Xmlns:xlink="Xmlns:xsi="Reluctance motors