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Energy management of dual-source propelled electric vehicle using fuzzy controller optimized via genetic algorithm

机译:采用遗传算法优化的模糊控制器的双源推进电动汽车能量管理

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Energy and power distribution between multiple energy sources of electric vehicles (EVs) is the main challenge to achieve optimum performance from EV. Fuzzy inference systems are powerful tools due to nonlinearity and uncertainties of EV system. Design of fuzzy controllers for energy management of EV relies too much on the expert experience and it may lead to sub-optimal performance. This paper develops an optimized fuzzy controller using genetic algorithm (GA) for an electric vehicle equipped with two power bank including battery and super-capacitor. The model of EV and optimized fuzzy controller are simulated in ADVISOR software. Developed method has been implemented on standard driving cycles and simulation results show the decrease on consumed power by developed controller compared with standard fuzzy controller.
机译:电动车辆(EVS)之间的能量和功率分配是从EV实现最佳性能的主要挑战。由于EV系统的非线性和不确定性,模糊推理系统是强大的工具。 EV的能源管理模糊控制器的设计依赖于专家体验,可能导致次优性能。本文开发了一种使用遗传算法(GA)的优化模糊控制器,该电动车辆配备有两个电源组,包括电池和超级电容。顾问软件模拟了EV和优化模糊控制器的模型。开发的方法已经在标准驱动周期上实施,仿真结果表明,与标准模糊控制器相比,开发控制器的消耗功率降低。

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