基于改进EKF算法的锂离子电池SOC在线估计OA
Online State of Charge Estimation of Lithium-ion Battery Based on Improved Extended Kalman Filter Algorithm
锂离子电池因其能量密度高、自放电率低、污染小等优势,已经在储能电站领域得到广泛应用.针对锂离子电池各项状态预测,首先搭建二阶RC等效电路模型,然后采用带遗忘因子的递推最小二乘法(FFRLS)对模型参数进行辨识,提出一种基于自适应扩展卡尔曼滤波(AEKF)算法的SOC-SOH联合估计方法.在不同电池工况下进行对比验证,结果表明,与扩展卡尔曼滤波(EKF)和无迹卡尔曼滤波(UKF)相比,所提方法预测SOC和SOH的精确度和计算效率均有所提高,具有一定的实用价值.
Lithium-ion batteries have been widely used in the field of energy storage power stations due to their high energy density,low self-discharge rate and low pollution.To solve the accurate prediction of various states of lithium-ion batteries,a second-order RC equivalent circuit model is first built,and then the parameters of the model are identified by using the forgetting factor recursive least squares(FFRLS)method.A joint SOC-SOH estimation method based on adaptive extended Kalman filtering(AEKF)algorithm is proposed,and the method is compared and verified under different battery conditions.Experimental results show that compared with the extended Kalman filter(EKF)and the unscented Kalman filter(UKF),the proposed method can improve the accuracy and computational efficiency of SOC and SOH prediction,and has certain practical value.
崔晓丹;吴家龙;邓馗;王彦品;冯佳期;李亚杰
国电南瑞科技股份有限公司,江苏 南京 211106
动力与电气工程
锂离子电池二阶RC模型参数辨识SOH估计SOC估计AEKF算法
lithium batterysecond-order RC modelparameter identificationSOH estimationSOC estimationAEKF algorithm
《电器与能效管理技术》 2024 (006)
49-58 / 10
国网南瑞科技股份有限公司科技项目《锂电池储能系统电磁暂态模型构建技术研究》
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