摘要
锂离子电池的荷电状态(state of charge,SOC)估计是电池管理系统的重要组成部分。更加精确的SOC估计结果,有利于储能电站的并网和控制。该文提出一种基于Vi-RNN的储能电池SOC估计算法,该算法将储能电池端口电压和电压增量作为输入,荷电状态作为输出,RNN算法作为框架,实现在线更高精度的SOC估计。采用储能锂离子电池在0.2C和0.3C充放过程中的测量数据进行仿真分析。结果显示:相较于MEA-BP算法,该方法估计结果的均方误差和相对误差更低,均方误差降低约20%。
State of charge(SOC)estimation of lithium-ion battery is an important part of battery management system.More accurate SOC estimation results are conducive to the grid connection control of energy storage power station.In this paper,an energy storage battery SOC estimation algorithm based on Vi-RNN was proposed.The energy storage battery port voltage and voltage increment were taken as the input,and SOC estimation result was taken as the output,and the RNN neural network algorithm was used as the framework to realize the high-precision SOC estimation.In this paper,the measured data of energy storage lithium-ion battery during charging and discharging at 0.2C and 0.3C were used for simulation analysis.The results show that,compared with MEA-BP,the mean square error and relative error of our method are lower,and the mean square error is reduced by about 20%.
作者
文茹馨
刘惠颖
梁言贺
汪江昭
林文娟
王宗晶
李琦
WEN Ruxin;LIU Huiying;LIANG Yanhe;WANG Jiangzhao;LIN Wenjuan;WANG Zongjing;LI Qi(State Grid Heilongjiang Power Supply Service Management Center,Harbin 150070 China;College of Electrical and Information Engineering,Hunan University,Changsha 410082,China)
出处
《中国测试》
CAS
北大核心
2023年第5期117-122,共6页
China Measurement & Test
关键词
锂电池
荷电状态
循环神经网络
电压增量
均方误差
相对误差
lithium battery
state of charge
recurrent neural network
voltage increment
mean square error
relative error
