LI Dong-yu, WANG Rui and FENG Yi-min. Impairment degree forecast for valve regulated lead acid battery based on BP neural network[J]. Journal of Light Industry, 2012, 27(4): 12-15. doi: 10.3969/j.issn.1004-1478.2012.04.004
Citation:
LI Dong-yu, WANG Rui and FENG Yi-min. Impairment degree forecast for valve regulated lead acid battery based on BP neural network[J]. Journal of Light Industry, 2012, 27(4): 12-15.
doi:
10.3969/j.issn.1004-1478.2012.04.004
Impairment degree forecast for valve regulated lead acid battery based on BP neural network
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Taikang County Electric Power Administration, Taikang 461400, China;
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FanCounty Power Supply Bureau, FanCounty 457500, China;
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Human Resources and Social Security of Luoyang City, Luoyang 471003, China
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Received Date:
2012-03-19
Available Online:
2012-07-15
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Abstract
In order to improve forecast accurancy of impairment degree for valve regulated lead acid battery,a forecast model based on neural network with autonomic learning function was structured.The BP neural network was trained and learned using 192 different discharge degree data,then the real time collection data were forecasted and analyzed using trained BP neural network.The forecast accurancy is above 93%,which proves the forecast model's validity.
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References
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[1]
Rand D A J,Moseley P T,Garche J,et al.Valve-regulated Lead-acid Batteries[M].London:Elsevier,2004:8-12.
-
[2]
朱松然.铅酸蓄电池实用手册[M].北京:机械工业出版社,1992:22-46.
-
[3]
刘百芬,程海林.一种新型的蓄电池内阻测量方法的研究及实现[J].仪表技术与传感器,2004,40(5):49.
-
[4]
韩团军.基于神经网络的铅酸蓄电池剩余容量预测[J].陕西理工学院学报,2008,24(4):26.
-
[5]
舒服华.基于最小二乘支持向量机的电池剩余电量预测[J].电源技术,2008,32(7):452.
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Proportional views
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