摘要
针对锂电池荷电状态(SOC)的非线性特性,提出GRA-MEA-BP神经网络算法,用于锂电池运行的实时SOC估计。所提方法通过灰度关联分析(GRA)筛选SOC估计的主要影响因子,将得到主要影响因子作为输入,当前时刻的SOC作为输出,训练MEA-BP神经网络。最后,以实际使用的锂电池作为对象,对所提出的方法进行数据训练和验证。结果表明了所提方法的准确性。
In view of the nonlinear characteristics of lithium battery state of charge(SOC),the grey relational analysis,mind evolutionary algorithm,back propagation(GRA-MEA-BP)neural network algorithm is proposed,which is used for the SOC estimation of lithium battery at the current moment.Several main impact factors of SOC estimation is selected by GRA.The MEA-BP neural network is trained by taking the main influencing factors as inputs and the current SOC as output.Finally,taking the actual lithium battery as the object the proposed method is trained and verified by real data.The verification results indicate the accuracy of the proposed method.
作者
许守平
胡娟
付珊珊
陈娟
XU Shouping;HU Juan;FU Shanshan;CHEN Juan(China Electric Power Research Institute,Beijing 100192,China;State Key Laboratory of Operation and Control of Renewable Energy&Storage Systems,Beijing 100192,China)
出处
《电器与能效管理技术》
2023年第9期55-61,共7页
Electrical & Energy Management Technology
基金
国家电网公司科技项目(5442DG190013)。
关键词
锂电池
荷电状态
神经网络
灰度关联分析
lithium battery
state of charge
neural network
gray correlation analysis
