基于EKF算法的锂电池SOC估算研究  

Research on SOC Estimation of Lithium Battery Based on EKF Algorithm

作　　者：宋朋骏 蒋晓艳 高阳 秦民 SONG Peng-jun;JIANG Xiao-yan;GAO Yang;QIN Min(Agriculture and Animal Husbandry College of Xizang,Nyingchi 860000,China;不详)

机构地区：[1]西藏农牧学院,水利土木工程学院,西藏林芝860000 [2]西藏农牧学院,电气工程学院,西藏林芝860000 [3]国能(西藏)尼洋河发电有限公司,西藏林芝860000

出　　处：《电力电子技术》2025年第1期71-76,共6页Power Electronics

基　　金：西藏自治区自然科学基金重点项目(XZ202201-ZR0052G);高原特色资源创新利用与发展研究(2023-XYQ-006);高海拔高寒条件下光储逆变器关键技术研究(XZNMXYRCXM-2024-04)。

摘　　要：随着新能源储能充电站的不断增加,锂电池作为中间载体需要提供稳定的充放电电压,而电池的不稳定性、剩余电量和充放电电压波动的问题尚未得到充分解决,严重影响了其安全和可靠性。因此,围绕上述问题展开研究,提出扩展卡尔曼滤波器(EKF)进行锂电池的状态估计。首先,采用二阶RC等效电路模型对电池进行建模,然后进行电池恒流脉冲放电实验得到荷电状态(SOC)-OCV拟合曲线,利用离线参数辨识法对等效电路模型参数进行辨识。最后利用电池的混合脉冲功率特性(HPPC)实验数据验证模型的有效性,并在Matlab/Simulink软件中搭建SOC估算仿真模型验证EKF算法的可行性,结果表明EKF算法可以较好地将SOC的估算误差控制在3%以内。With the increasing number of new energy storage charging stations,lithium batteries as intermediate carri-ers need to provide stable charging and discharging voltages,and the problems of battery instability,remaining power and charging and discharging voltage fluctuations have not been fully solved,seriously affecting their safety and relia-bility.Therefore,the extended Kalman(EKF)filter is proposed to estimate the state of lithium batteries.Firstly,the se-cond-order RC equivalent circuit model is used to model the battery,and then the state of charge(SOC)-OCV fitting curve is obtained by the battery constant current pulse discharge experiment.The parameters of the equivalent circuit model are identified by off-line parameter identification method.Finally,the validity of the model is verified by using hybrid pulse power characterization(HPPC)experimental data of the battery,and the feasibility of the EKF algorithm is verified by building an SOC estimation simulation model in Matlab/Simulink software.The results show that the EKF algorithm can effectively control the SOC estimation error within 3%.

关 键 词：锂电池 荷电状态 参数辨识 

分 类 号：TM911[电气工程—电力电子与电力传动]