阀控铅酸蓄电池劣化程度预测研究  

Prediction of health of VRLA battery

作　　者：龙顺游[1] 强锡富[1] 

机构地区：[1]哈尔滨工业大学电气工程及自动化学院,黑龙江哈尔滨150001

出　　处：《哈尔滨工业大学学报》2003年第1期118-121,共4页Journal of Harbin Institute of Technology

摘　　要：阀控铅酸蓄电池的老化失效机理复杂 ,失效模式受诸多因素影响 ,单纯通过浮充状态数据分析难于准确估计电池的劣化程度 .在对蓄电池放电特性分析的基础上 ,研究了采用模糊神经网络方法建立蓄电池的劣化程度预测模型 ,根据部分放电的测量数据进行劣化程度的预测 。The mechanism of VRLA battery deterioration is very complex, and the deterioration models are affected by many factors. It is difficult to evaluate the health of VRLA battery only through the data analysis in a floating state. From the study on battery characteristics during discharging, a fuzzy neural network is adapted to build the model for SOH prediction. The SOH has been calculated by using part of discharge testing data. The satisfactory accuracy has been demonstrated by test data.

关 键 词：阀控铅酸蓄电池 劣化程度 预测 模糊神经网络 模型 

分 类 号：TM912[电气工程—电力电子与电力传动]