摘要
电动车电池管理系统的核心任务是对电池荷电状态(SOC)进行预测.在分析了MH/Ni电池充放电反应机理的基础上,应用径向基函数(RBF)神经网络建立了预测MH/Ni电池荷电状态的模型,并且应用该模型对电池放电过程中某一状态下的荷电状态进行预测.该模型预测速度快,并且预测值与试验值吻合.人工神经网络建模技术简单直观,是预测MH/Ni电池SOC有力工具.
Prediction of the state of charge （SOC） of MH/Ni battery is very important for battery management system of electric vehicles. Through discussing the electrochemistry of MH/Ni system, a model based on radial basis function （RBF） neural network was employed to predict the state of charge of MH/Ni battery. The model was used to predict the state of charge at a certain state of discharging process. The proposed model had high prediction speed. The predicted SOC closely resembled the measured value. Artificial neural network technique is simple and understandable. It is a powerful tool to estimate the SOC of MH/Ni battery.
出处
《化工学报》
EI
CAS
CSCD
北大核心
2006年第9期2162-2166,共5页
CIESC Journal
关键词
荷电状态
径向基函数
神经网络
MH/NI电池
state of charge
radial basis function
artificial neural network
MH/Ni battery
