改进BP神经网络的磷酸铁锂电池SOC估算  

SOC Estimation of Lithium Iron Phosphate Battery Based on Improved BP Neural Network

作　　者：黄妙华[1,2] 严永刚[1] 朱立明[1] 

机构地区：[1]武汉理工大学汽车工程学院,湖北武汉430070 [2]现代汽车零部件技术湖北省重点实验室,湖北武汉430070

出　　处：《武汉理工大学学报（信息与管理工程版）》2014年第6期790-793,共4页Journal of Wuhan University of Technology：Information & Management Engineering

基　　金：湖北省自然科学基金资助项目(2011CDB255)

摘　　要：以磷酸铁锂电池为研究对象,根据电池的充放电特性,在Matlab上建立合适的神经网络模型,提出组合训练法,通过大量试验,在比较了10多种训练函数的基础上,得出效果比较好的4种训练函数,兼顾估算精度和训练时间,找出了网络隐含层较优节点数为20,隐含层和输出层的传递函数分别为trainsig和purelin。训练结果表明,所建立的BP神经网络模型估算精度高,普适性好。The lithium iron phosphate battery was studied.The charge-discharge characteristics of the battery were investi-gated.Then the appropriate neural network was built in MATLAB.A combined training method was proposed.After comparison of more than ten train functions, four functions＆#39;performance were turn out to be outstanding.When both estimation accuracy and training time were considered, the appropriate number of hidden nodes was 20.Two transfer functions are tansig and purelin.The network trained by sample data was verified to be universal, and high estimation was achieved.

关 键 词：磷酸铁锂电池 SOC估算 BP神经网络 

分 类 号：TM912[电气工程—电力电子与电力传动] U469[机械工程—车辆工程]