基于忆阻神经网络的锂电池SOC检测技术研究  

Research on SOC detection technique of lithium battery based on memristive neural networks

作　　者：王建军[1] 马树才[2] 张春梅[1] 

机构地区：[1]张家口职业技术学院,河北张家口075000 [2]河北北方学院信息科学与工程学院,河北张家口075000

出　　处：《电源技术》2014年第11期2044-2046,共3页Chinese Journal of Power Sources

摘　　要：锂电池荷电分布状态(SOC)是衡量电动汽车锂电池状态的工作标准之一。在分析SOC检测技术的基础之上,提出了利用BP神经网络技术来构建电池组SOC数据模型的研究思路。并结合实际的仿真数据对BP神经网络进行了有效的改造,从而形成了以忆阻器为神经元突触的神经网络SOC检测体系。仿真结果表明,该方法适应性好,对实际具有良好的指导作用。State of charge（SOC） was one of the working standard that the measure of the state of the electric vehicle lithium. On the basis of analysis the detection technology of the SOC, the research ideas that use of BP neural network technology to build a battery pack SOC data model was proposed. And it took the effective transformation on BP neural network combined with the actual simulation data, which formed a memristor synapses neural network SOC testing system. Simulation results show that this method is well adapted and meaningful for the actual.

关 键 词：荷电分布状态 电动汽车 神经网络 忆阻器 

分 类 号：TM912[电气工程—电力电子与电力传动]