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

机译:遗传算法优化的模糊控制器在双源推进电动汽车能源管理中的应用

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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:电动汽车(EV)的多种能源之间的能量和功率分配是实现EV最佳性能的主要挑战。由于电动汽车系统的非线性和不确定性,模糊推理系统是强大的工具。用于电动汽车能量管理的模糊控制器的设计过于依赖专家经验,可能会导致性能欠佳。本文开发了一种基于遗传算法(GA)的优化模糊控制器,用于装备有电池和超级电容器两个移动电源的电动汽车。在ADVISOR软件中对EV模型和优化的模糊控制器进行了仿真。在标准行驶周期上实现了所开发的方法,仿真结果表明,与标准模糊控制器相比,所开发的控制器所消耗的功率有所减少。

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