An improved vehicle to the grid method with battery longevity management in a microgrid application

An improved vehicle to the grid method with battery longevity management in a microgrid application
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一种改进的车辆到电网方法,在微电网应用中具有电池寿命管理

DOI:
10.1016/j.energy.2020.117374
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发表时间:
2020-05
期刊:
影响因子:
9
通讯作者:
He Hongwen
He Hongwen
中科院分区:
工程技术1区
文献类型:
--
作者:
Yang Qingqing;Li Jianwei;Cao Wanke;Li Shuangqi;Lin Jie;Huo Da;He Hongwen

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本文提出了一种改进的车辆到电网(V2G)频率控制调度方法,其优点是保护电池,从而在并网服务期间节省电池寿命。所提出的方法在两个方面进行了改进。首先,为了预测 V2G 服务控制时间步长内的可用电动汽车 (EV) 电池容量,开发了基于深度学习的预测。其次,本研究对之前的V2G方法进行了改进,在V2G优化中加入了电池循环寿命的定量分析。基于LSTM算法的可调度电池容量的准确预测在电力系统频率控制中表现出非常有效。此外,与之前没有电池寿命控制的方法相比,所提出的方法有利于减少充电/放电周期。
This paper proposed an improved vehicle-to-grid (V2G) scheduling approach for the frequency control with the advantage of protecting the batteries hence saving the battery lifetime during grid connected service. The proposed methodology is improved in two ways. Firstly, to give a prediction of the available electric vehicle (EV) battery capacity in the control time-step for the V2G service, a deep learning based prediction is developed. Secondly, this study advances the previous V2G method by adding the quantitative analysis of the battery cycle life into the V2G optimization. The accurate prediction of the schedulable battery capacity based on the LSTM algorithm is shown very effective in the power system frequency control. Also, compared with the previous method that without battery lifetime control, the proposed method benefits in the reduction of charge/discharge cycles.
微电网中插电式电动汽车和风力发电机的随机协调:一种模型预测控制方法
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