基于修正EKF算法的锂离子电池SOC估算  

SOC Estimation of Li-ion Battery based on Modified EKF Algorithm

作　　者：黄正军[1] 施卢丹[1] HUANG Zhengjun;SHI Ludan(Jinhua Polytechnic,Jinhua 321007,China)

机构地区：[1]金华职业技术学院,浙江金华321007

出　　处：《金华职业技术学院学报》2021年第3期37-41,共5页Journal of Jinhua Polytechnic

摘　　要：电池荷电状态(SOC)是电池重要的性能指标之一,为电池管理系统实现管理控制提供了重要依据。针对磷酸铁锂电池的特性,综合考虑模型精度和运算量,选用PNGV等效电路模型,并对该模型进行改进。以扩展卡尔曼滤波算法(EKF)为基础,同时考虑电池温度、放电电流对SOC估算的影响,引入电池容量补偿系数和放电效率模型,实现对SOC估算进行修正,并通过Matlab仿真对SOC估算进行了验证。结果表明,该算法能够较好地表征电池的动态特性,具有估算精度较高、抗干扰能力较强的优点,可以满足应用要求。The battery state of charge(SOC)is one of the important performance indicators of battery,which provides an important basis for the management and control of Battery Management System(BMS).In view of the characteristics of lithium iron phosphate battery,considering the model accuracy and calculation amount,the equivalent circuit model of PNGV was selected and improved.Based on Extended Kalman Filter(EKF),considering the influence of battery temperature and discharge current on SOC estimation,the battery capacity compensation coefficient and discharge efficiency model were added to realize the correction of SOC estimation.At the end of this paper,the SOC estimation method was validated by MATLAB simulation.The results show that the algorithm can better display the dynamic characteristics of battery.It has the advantages of high estimation accuracy and strong antiinterference ability,and can meet the application requirements.

关 键 词：荷电状态 PNGV模型 扩展卡尔曼滤波 修正 锂离子电池 

分 类 号：TM912[电气工程—电力电子与电力传动]