| 基于RBFNN的船用铅酸蓄电池SOC预测方法研究 |
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| 引用本文： | 王阔厅,孙俊忠,周智勇,张海鹏.基于RBFNN的船用铅酸蓄电池SOC预测方法研究[J].蓄电池,2012,49(2):76-80. |
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| 作者姓名： | 王阔厅 孙俊忠 周智勇 张海鹏 |
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| 作者单位： | 海军潜艇学院培训系机电研究所,山东青岛,266042 |
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| 摘 要： | 目前预测铅酸蓄电池荷电状态(SOC)的算法很多,这些算法各有特点。根据船用铅酸蓄电池的特点,本文比较分析了这些方法的预测效果,提出了利用径向基神经网络(RBFNN)算法预测船用铅酸蓄电池SOC的方法。并利用某型船用铅酸蓄电池的实验数据,对其SOC进行了预测。结果表明:利用该算法预测船用铅酸蓄电池的SOC,精度高,操作简便。
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| 关 键 词： | 船用铅酸蓄电池 荷电状态 径向基神经网络 剩余容量 预测 |
Research on forecasting the SOC of marine lead-acid batteries based on RBFNN |
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| WANG Kuo-ting
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SUN Jun-zhong
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ZHOU Zhi-yong
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ZHANG Hai-peng.Research on forecasting the SOC of marine lead-acid batteries based on RBFNN[J].Chinese Labat Man,2012,49(2):76-80. |
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| Authors: | WANG Kuo-ting SUN Jun-zhong ZHOU Zhi-yong ZHANG Hai-peng |
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| Affiliation: | (Navy Submarine Academy,Qingdao Shandong 266042,China) |
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| Abstract: | At present,there are many kinds of algorithms that forecast the state of charge（SOC） of the lead-acid batteries.These algorithms have different characteristics.This paper have compared and analyzed the forecasting effect of these algorithms.It put forward the best algorithm based on the RBFNN for forecasting the state of charge of the lead-acid batteries,and used RBFNN algorithm to forecast the state of charge of the lead-acid batteries based on the experimental data.The results indicated that RBFNN algorithm could accurately and easily forecast the state of charge ofmarine lead-acid batteries. |
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| Keywords: | marine lead-acid battery state of charge RBFNN residual capacity forecast |
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