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Multi-Objective Design Optimization of a Three Phase Squirrel Cage Induction Motor for Electric Propulsion System using Genetic Algorithm

机译:基于遗传算法的电动推进系统三相灰鼠笼式电动机的多目标设计优化

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Electric motor is one of the most important components of an Electric Vehicle (EV). The induction motor is mostly used for electric propulsion systems due to its various advantages such as low cost, robustness, etc. This paper discusses the Genetic Algorithm (GA) based multi-objective design optimization of an induction motor. The aim is to increase efficiency and to reduce the weight of an induction motor simultaneously. Different design variables and design constraints are imposed on the optimization problem within a range for design variables using Global Optimization Toolbox in MATLAB. A 7.5 kW, 4 pole, 50 Hz squirrel cage induction motor is designed conventionally and optimized using the GA. The designs obtained were validated and performance curves were obtained using their equivalent circuit parameters. Performance comparison was made between a conventionally designed motor and an optimally designed motor. The optimized design has 1.02% efficiency improvement and 1.11 kg weight reduction over the conventional design without violating the design constraints, whereas the conventional design violated the design constraints.
机译:电动机是电动车辆(EV)最重要的部件之一。由于其各种优点如低成本,鲁棒性等,因此,感应电机主要用于电动推进系统。本文讨论了基于遗传算法(GA)一种感应电动机的多目标设计优化。目的是提高效率并同时降低感应电机的重量。在Matlab中使用全局优化工具箱的设计变量范围内的优化问题施加了不同的设计变量和设计约束。一台7.5千瓦,4杆,50 Hz鼠笼式感应电机是传统和使用GA的优化设计。获得的设计被验证,并且使用其等效电路参数获得性能曲线。在传统设计的电机和最佳设计的电动机之间进行性能比较。优化设计具有1.02%的效率提升,传统设计减少1.11千克,而无需违反设计限制,而传统设计违反了设计约束。

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