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Energy Management Strategy for Plug-In Hybrid Electric Vehicles Based on Genetic-Fuzzy Control Strategy

机译:基于遗传-模糊控制策略的插电式混合动力汽车能源管理策略

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Plug-in hybrid electric vehicles (PHEVs) provide us with a good way to solve the problems involving powertrain efficiency and pollution. The energy managements (EMs) of PHEVs is a main point which attracts a lot of research interests. In this paper, the genetic algorithm is used to optimize the performance of fuzzy logic control of EMs. The study mainly focuses on the control of several factors about the torque distribution and SOC maintaining to reduce emission of harmful gases like CO, NO, and HC. In the simulation, the control system model is built based on Matlab/Simulink platform, and the result shows that the 100-Kilometer Fuel Consumption and the CO emission are reduced by 5.07% and 6.31% respectively after utilizing the genetic algorithm to optimize the fuzzy logic control strategy.
机译:插电式混合动力汽车(PHEV)为我们提供了解决动力总成效率和污染问题的好方法。 PHEV的能源管理(EM)是吸引许多研究兴趣的重点。本文采用遗传算法来优化EM的模糊逻辑控制性能。研究主要集中在控制扭矩分配和SOC保持的几个因素上,以减少诸如CO,NO和HC等有害气体的排放。在仿真中,基于Matlab / Simulink平台建立了控制系统模型,结果表明,利用遗传算法对模糊控制进行优化后,百公里油耗和CO排放量分别降低了5.07%和6.31%。逻辑控制策略。

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