基于改进BP神经网络的铅酸蓄电池放电程度评估  

Discharging Degree Evaluation to Lead Acid -Battery Based on Improved BP Network

作　　者：范红军[1] 王春健[2] 张晓杰[1] 

机构地区：[1]海军航空工程学院青岛分院,山东青岛266041 [2]海军潜艇学院,山东青岛266071

出　　处：《自动化应用》2011年第9期17-19,43,共4页Automation Application

摘　　要：针对传统方法评估蓄电池放电程度的缺点,引入人工神经网络算法,建立以蓄电池开路电压、内阻和工作温度为输入的蓄电池放电程度改进BP神经网络模型,实现对铅酸蓄电池放电程度的评估。结果表明,通过该网络模型可以方便快速地得到蓄电池的放电程度评估值,所得结果满足要求。To the shortage of traditional evaluation means of battery discharge percent, the artificial neural network is inducted. The improved BP neural network of battery discharge percent is established, whose inputs are opening voltage, dynamic resistance and working temperature. And the discharge percent of acid battery is realized. The result shows that the BP network can obtain the discharge percent expediently and fleetly, and the conclusion meets the demand commendably.

关 键 词：铅酸蓄电池 放电程度 评估方法 BP神经网络 

分 类 号：TM912.2[电气工程—电力电子与电力传动]