期刊论文

中文

电源技术

1002-087X

2020

44

10

1483-1487

10.3969/j.issn.1002-087X.2020.10.021

国网湖南省电力有限公司科技项目(5216A220000F) 国家自然科学基金资助项目(51977014)。

本校为第一机构

提升锂离子电池荷电状态(SOC)估计精度并准确估计健康状态(SOH)对于设计高性能和安全的电池管理系统(BMS)至关重要。以二阶RC锂离子电池电路模型为研究对象,提出了一种由扩展卡尔曼滤波(EKF)算法和平滑可变结构滤波(SVSF)算法组成的混合滤波算法(EKF-SVSF),其中扩展卡尔曼滤波算法用于锂离子电池参数辨识,平滑可变结构滤波算法用于SOC估计,并采用改进的粒子群优化算法(PSO)对混合滤波算法迭代过程中的噪声协方差矩阵进行修正,电池容量实时在线估计的结果用于SOH参数的预测。实验和仿真结果表明,所提出的EKF-SVSF算法对电池的欧姆电阻和容量有一个较好的估计,进一步提升了电池状态的估计精度。

It is very important to improve the accuracy of state of charge(SOC)estimation and estimate the state of health(SOH)accurately for the design of high-performance and safe battery management system(BMS).A hybrid filtering algorithm(EKF-SVSF)was proposed,which was composed of the extended Kalman filtering algorithm and the sliding variable structure filtering algorithm.The EKF algorithm was used to identify the parameters of the lithium-ion battery,and the smooth variable structure filtering algorithm(SVSF)was used to estimate the state of charge(SOC).The noise covariance matrix in the iterative...