储能电池实时荷电状态联合估计方法  

A joint real-time SOC estimation method for energy storage batteries

作　　者：李先锋 胡晨刚 卜莉敏 陈攀 苗文捷 黄文哲 LI Xianfeng;HU Chengang;BU Liming;CHEN Pan;MIAO Wenjie;HUANG Wenzhe(Hangzhou Electric Power Design Institute Co.,Ltd.,Hangzhou 311500,China)

机构地区：[1]杭州电力设计院有限公司,杭州311500

出　　处：《浙江电力》2024年第5期73-82,共10页Zhejiang Electric Power

基　　金：浙江大有集团有限公司科技项目(DY2022-01)。

摘　　要：准确估计储能电池的SOC(荷电状态),对于实现电池的均衡充放电,减少因电池过充过放引起的容量下降具有重要意义。针对储能电池的复杂化学状态和SOC非线性时变特性,提出一种基于VFFRLS(变遗忘因子递归最小二乘)和UKF(无迹卡尔曼滤波)算法的锂离子电池SOC联合估计方法。采用VFFRLS在线辨识电池模型的电阻、电容参数,根据辨识结果,利用UKF算法实时估计电池SOC。实验结果表明,该联合算法具有较高的准确性和稳定性。Accurately estimating the state of charge(SOC)of energy storage batteries is of paramount importance for achieving balanced charging and discharging,and mitigating capacity degradation caused by overcharging and overdischarging.In view of the complex chemical states and nonlinear time-varying characteristics of SOC in energy storage batteries,this paper proposes a joint SOC estimation method for lithium-ion batteries based on the variable forgetting factor recursive least squares(VFFRLS)and unscented Kalman filter(UKF)algorithms.The VFFRLS algorithm is employed for online identification of battery model parameters such as resistance and capacitance,and based on the identification results,the UKF algorithm is utilized for real-time SOC estimation.Experimental results demonstrate that the proposed joint method exhibits high accuracy and stability.

关 键 词：荷电状态估计 变遗忘因子递归最小二乘 无迹卡尔曼滤波 

分 类 号：TM91[电气工程—电力电子与电力传动]