基于改进Rint模型锂离子电池SOC估计  

SOC estimation of Lithium-ion battery based on Rint model

作　　者：黄莉莉[1] 任星星 苗博博 孔繁昌 HUANG Lili;REN Xingxing;MIAO Bobo;KONG Fanchang(College of Lanzhou Institute Technology,Gansu,Lanzhou Province,730030,China)

机构地区：[1]兰州工业学院,甘肃兰州730050

出　　处：《电池工业》2022年第4期177-180,共4页Chinese Battery Industry

基　　金：甘肃省高等学校创新基金项目资助(2021B-309);兰州工业学院大学生创新创业训练计划项目:202DC2111807CX690。

摘　　要：通过对Rint模型进行改进,将电池内阻分为欧姆内阻R_(0)和极化内阻R,由于EKF算法可以很好地将非线性的问题解决,且计算精度高。本文采用扩展卡尔曼滤波EKF算法,对比改进前与改进后的Rint模型,在Matlab/Simulink软件中搭建基于两种电池模型的EKF算法模型,对该电池的荷电状态SOC进行估计。通过调参计算改进后改进后最小误差约为1.8%,最大误差在5%以内满足工程实际应用要求。By improving the Rint model,the internal resistance of the battery is divided into ohmic internal resistance and polarization internal resistance.Because the EKF(Extended Kalman Filter)algorithm can solve the nonlinear problem well,and the calculation accuracy is high,This paper uses the EKF algorithm to compare the Rint model before and after the improvement,and builds the EKF algorithm model based on the two battery models in Matlab/Simulink software to estimate the SOC(State of Charge)of the battery.The minimum error is 1.8%after the improvement through the parameter adjustment calculation,and the maximum error is within 5%to meet the requirements of practical engineering applications.

关 键 词：Rint模型 锂离子电池 SOC估计 

分 类 号：TM912[电气工程—电力电子与电力传动]