Multi-Population based Hippopotamus Optimization Algorithm for Design of Switched Reluctance Motor for Enhanced Thermal Performance

Light Electric Vehicles (LEVs), such as e-rickshaws and e-autos, are critical for reducing urban air pollution but face thermal management challenges due to frequent stops and starts and the urban heat island effect. This paper analyzes temperature rise in Switched Reluctance Motors (SRMs), which are suitable for LEVs due to their better heat dissipation capabilities. Hippopotamus optimization (HO) algorithm is presented first and then coupled with multi-population strategy. The proposed multi-population based hippopotamus optimization (MPHO) algorithm is compared with HO algorithm on CEC test suit and the convergence curves on selected functions are presented. MPHO algorithm is applied to the design of SRM for enhanced thermal performance. First local sensitivity analysis is carried out and the decision variables are identified to which temperature rise is most sensitive. A response surface model (RSM) is developed in conjugation with latin hypercube sampling (LHS) to evaluate objective function (temperature rise) and constraints (output power and efficiency). Finally, HO and MPHO algorithms are applied to the design of SRM and optimal design points are obtained. It is observed that MPHO performs better than HO algorithm and gives better results.