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Genetic Algorithms optimized Multi-objective Controller for an Induction Machine based Electrified Powertrain

机译:基于电气化动力系的遗传算法优化多目标控制器

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In electrified powertrain control, meeting the torque demands and ensuring efficient Electrical Machine (EM) operations are two essential but conflicting demands. A multi-objective Linear Parameters Varying (LPV) controller is proposed to address the problem of these conflicting objectives. The synthesis of multi-objective controller is based on the selection of optimal weighting functions optimized by Genetic Algorithm (GA). The effectiveness of the proposed controller is tested and evaluated for an electrified powertrain operating in a standard urban driving cycles. The stability of the proposed Multi-Objective Controller (MOC) is established. The nonlinear simulation of the proposed controller delivers the robust performance and better efficiency of an EV Induction Machine (IM) based electric drive over the entire driving cycle.
机译:在电气化的动力总成控制中,满足扭矩要求和确保有效的电机(EM)操作是两个必不可少但相互矛盾的需求。提出了一种多目标线性参数改变(LPV)控制器来解决这些冲突目标的问题。多目标控制器的合成基于通过遗传算法(GA)优化的最佳加权功能的选择。测试并评估所提出的控制器的有效性,并评估在标准城市驾驶循环中操作的电气化动力系。建立了所提出的多目标控制器(MOC)的稳定性。所提出的控制器的非线性仿真在整个驾驶循环上提供了基于EV感应机(IM)的电动驱动的强大性能和更好的效率。

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