Multi-Objective Optimization of the Switched Reluctance Motor for Improved Performance in a Heavy Hybrid Electric Vehicle Application

Sashankh Ravi, Purdue University


The goal of this research is to improve the performance of the switched reluctance motor for a heavy hybrid electric vehicle based application. In order to achieve this, the stator and rotor tooth shapes and the switching current waveforms are modified from their base values. A multi-objective optimization problem is formulated to minimize the square of the RMS current and the normalized torque ripple. The optimization is solved using a genetic algorithm and a Pareto-optimal front is obtained. Finally, a time-domain simulation is employed to study the performance of the optimal designs over a wide range of operating speeds.




Aliprantis, Purdue University.

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