面向新型电力系统储能电站的锂电池荷电状态评估方法研究  

Research on SOC evaluation method of lithium battery for energy storage station in novel power system

作　　者：杨涛 文贤馗 谈竹奎 曾鹏 胡明辉 YANG Tao;WEN Xiankui;TAN Zhukui;ZENG Peng;HU Minghui(Electric Power Research Institute of Guizhou Power Grid Co.,Ltd.,Guiyang 550002,China;EPTC(Beijing)Electric Power Research Institute,Beijing 100000,China)

机构地区：[1]贵州电网有限责任公司电力科学研究院,贵阳550002 [2]中能国研(北京)电力科学研究院,北京100000

出　　处：《电测与仪表》2025年第3期30-37,共8页Electrical Measurement & Instrumentation

基　　金：南方电网有限责任公司科技项目(GZKJXM20210373)。

摘　　要：锂电池作为新型电力系统储能电站的重要组成部分,在其工作过程中需要对电池电量即荷电状态实时估计,避免储能锂电池出现过充电与过放电等故障,为储能系统带来安全隐患。为了准确估计电池荷电状态(state of charge,SOC),文中基于电化学阻抗谱(electrochemical impedance spectroscopy,EIS)对宽温度范围内不同荷电状态的锂电池进行测试和研究,提出了一种基于电化学阻抗谱的宽温度范围下的锂电池SOC估计方法。实验结果表明,在正常工作温度(15℃~45℃)范围内,10 Hz~1000 Hz频带内阻抗幅值在不同温度下可以有效表征电池SOC,而阻抗相位基本不随电池SOC变化而变化。通过对比不同温度与不同SOC下的阻抗幅值与相位,文中选取了10 Hz、100 Hz、1000 Hz作为特征频率,将三个频率下阻抗幅值及环境温度作为输入参量,结合深度神经网络(deep neural network,DNN)算法实现了锂电池的SOC估计。结果表明,文中建立的SOC估计模型能够将估计误差控制在4%以内,可有效为锂电池SOC估计提供参考,提高新型电力系统储能电站的设备管理水平。As an important part of the energy storage power station of novel power system,the lithium battery needs to estimate the battery power,that is,the state of charge in real time during its operation,so as to avoid the over-charge and over discharge failures of the energy storage lithium battery,which brings security risks to the energy storage system.In order to accurately estimate the battery state of charge(SOC),this paper tests and studies lithi-um batteries with different SOCs in a wide temperature range based on electrochemical impedance spectroscopy(EIS),and proposes a SOC evaluation method of lithium battery under wide temperature range based on EIS.The experimental results show that in the normal operating temperature range(15~45℃),the impedance amplitude in the 10~1000 Hz frequency band can effectively characterize the battery SOC at different temperatures,while the impedance phase basically does not change with the change of the battery SOC.By comparing the impedance ampli-tude and phase at different temperatures and different SOCs,this paper selects 10 Hz,100 Hz,and 1000 Hz as the characteristic frequencies,and uses the impedance amplitude and ambient temperature at the three frequencies as input parameters.Combined with the deep neural network algorithm(DNN),the SOC estimation of lithium battery is realized.The results show that the SOC estimation model established in this paper can control the estimation error within 4%,which can effectively provide a reference for lithium battery SOC estimation and improve the equipment management level of the energy storage power station in novel power system.

关 键 词：新型电力系统 锂电池 荷电状态估计 电化学阻抗谱 

分 类 号：TM911[电气工程—电力电子与电力传动]