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Neural Network Controller to Manage the Power Flow of a Hybrid Source for Electric Vehicles

机译:神经网络控制器,用于管理电动汽车的混合动力源

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This paper proposes a neural network controller for a DC/DC converter used to manage the power flow of an active hybrid energy source for Electric Vehicles. The energy source is composed of a battery, an ultracapacitor and a Dual Active Bridge DC/DC converter used to optimize the power distribution between the energy sources involved. The neural network control method applied to this bidirectional converter has many advantages: reduction of the computational time, decrease in the amount of data stored and improvement in the transient response. This method is simulated in an Electric Vehicle application using a normalized driving cycle. The simulation results will show the improvements made by this control method compared to the conventional PI controller, in terms of improving the power management, reducing stress on the sources and increasing robustness due to reference changes.
机译:本文提出了一种用于DC / DC转换器的神经网络控制器,用于管理电动车辆的主动混合能量源的电力流量。能源由电池,超容器和双功动桥DC / DC转换器组成,用于优化所涉及的能源之间的功率分布。应用于该双向转换器的神经网络控制方法具有许多优点:减少计算时间,减少存储的数据量和瞬态响应的改进。使用归一化驾驶循环在电动车辆应用中模拟该方法。仿真结果将显示与传统PI控制器相比,通过改善电源管理,降低源的应力并导致鲁棒性引起的稳定性,对传统PI控制器进行了改进。

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