基于循环神经网络的电动汽车智能双向充电策略

Susamani Francis Livinraj, Muralidhar Nayak Bhukya, D. Rani
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引用次数: 0

摘要

由于电力电子元件的适应,产生了一定程度的噪声,技术上称为电磁干扰(EMI),随着系统容量的增加,EMI含量也成比例地提高。因此,为了减轻电磁干扰的影响,在系统中引入了基于低通滤波器的电磁干扰滤波器,从而抑制了噪声水平。双向充电系统(Bidirectional Charging System, BCS)是一种新兴的电动汽车自主供电技术,主要以车对网(V2G)、网对车(G2V)和车对负荷(V2L)的形式实现。为了了解BCS的行为,采用了递归神经网络(RNN)控制器,并与人工神经网络双向充电模型进行了比较。采用RNN控制器的BCS充电系统在通过DC/DC变换器和AC/DC变换器交换电能时,与ANN控制器相比具有更好的动态响应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intelligent Bidirectional Charging Strategy based on Recurrent Neural Network (RNN) for EV
Due to adaption of power electronic components some extent of noise is generated, technically treated as Electromagnetic Interference (EMI), as system capacity increases the EMI content also improved proportionately. Therefore, in order to mitigate the EMI effect the low pass filter based EMI filter is introduced in the system such that the noise level is suppressed. Bidirectional Charging System (BCS) is one of the emerging technologies in EV to obtain autonomous power supply system in the form of Vehicle to Grid (V2G), Grid to Vehicle (G2V), and Vehicle to Load (V2L). To know the behavior of BCS the proposed Recurrent Neural Network (RNN) controller is employed and is compared with ANN bidirectional charging model. BCS charging system with RNN controller has better dynamic response to exchange the power via DC/DC converter and AC/DC converter as compared to ANN controller.
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