State-of-Charge Estimation Based on Immune Evolutionary Networks

被引:0
|
作者
Cheng Bo [1 ,2 ]
Lin Liqiao [3 ]
Cao Houli [1 ]
Zhang Jiexin [1 ]
Cao Binggang [2 ]
机构
[1] Changan Univ, Sch Construct Machinery, Xian 710064, Shaanxi, Peoples R China
[2] Xi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Peoples R China
[3] Qingdao Hismile Coll, Dept Informat & Technol, Qingdao 266100, Peoples R China
关键词
state of charge; immune algorithm; neural network; evolutionary strategy;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Based on Clonal Selection Theory, an adaptive Parallel Immune Evolutionary Strategy (PIES) is presented. Compared with conventional evolutionary strategy algorithm (CESA) and immune monoclonal strategy algorithm (IMSA), experimental results show that PIES is of high efficiency and can effectively prevent premature convergence. A three-layer feed-forward neural network is presented to predict state of charge (SOC) of Ni-MH batteries. Initially, partial least square regression (PLSR) is used to select input variables. Then, five variables, battery terminal voltage, voltage derivative, voltage second derivative, discharge current and battery temperature, are selected as the inputs of NN. In order to overcome the weakness of BP algorithm, PIES is adopted to train weights. Finally, under the state of dynamic power cycle, the predicted SOC and the actual SOC are compared to verify the proposed neural network with acceptable accuracy (5%).
引用
收藏
页码:3502 / +
页数:2
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