铅酸蓄电池工况监控优化算法

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摘 要 : 本文将铅酸电池监控信息数据进行分类,以用户为导向,展现监控所得信息数据的层级关系;对剩余电量与荷电状态SOC的定义进行了区分;分析了智能算法中的神经网络算法与卡尔曼滤波算法在剩余电量估算中的应用与发展趋势.

Abstract: The information of lead-acid battery monitor was classified in different categories. The hierarchy of battery monitoring information considering users need was shown in the paper. The differences between remaining capacity and SOC were analyzed. The new intelligence algorithms such as Neural Networks and Kalman filter he were disscussed.


关 键 词 : 铅酸蓄电池;剩余电量;神经网络;卡尔曼滤波

Key words: Lead-acid battery;Remaining capacity;Neural Networks;Kalman filter

中图分类号:TM912 文献标识码:A 文章编号:1006-4311(2013)11-0295-02

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基金项目:广东省自然科学基金(S2012010009675)资助.

作者简介:张小慧(1988-),女,湖北十堰人,华南理工大学自动化科学与工程学院硕士研究生,主要研究方向为复杂系统控制、优化与管理.