Novel Differential Evolutionary Optimization Approach for an Integrated Motor-Magnetic Gear Used for Propulsion Systems

Abstract

This paper addresses the proposition of an electrical multiport propulsion system. This traction system contains an electric motor to which a magnetic gear is attached; it is called an integrated motor-magnetic gear (IMMG) propulsion unit. This IMMG has two shafts and two levels of torque and speed. Thus, it is suited for the propulsion of an electric tractor, which has different diameters for the front and rear wheels. The IMMG design is optimized based on the differential evolution (DE) algorithm. For such an algorithm, offline tuning of its parameters may fail given the different characteristics of optimization problems. Therefore, a parameter adaptation that considers feedback from the search process can be an alternative. In this work, we propose a novel online strategy to control the DE algorithm’s parameters. This strategy is based on a simple adaptation mechanism to generate a promising mutation parameter value for each individual in the population. The design process was evaluated through experiments, and a good correlation was found between the designed performances and tested results.

Keywords

• Differential evolutionary optimization
• integrated motor-magnetic gear
• EV propulsion