基于修正安时积分法的磷酸铁锂电池荷电状态估计-《郑州大学学报(工学版)》

文章信息/Info

Title:: State of Charge Estimation of LiFePO4 Battery Based on Modified Amper-hour Integral Method

作者:: 宋磊; 陆春光; 刘琳; 刘世芳; 王要强; 1. 国网浙江省电力有限公司,浙江杭州 310014;2. 国网浙江杭州市萧山区供电有限公司,浙江杭州 311200;3. 郑州大学电气与信息工程学院,河南郑州 450001;4. 郑州大学河南省电力电子与电力系统工程技术研究中心,河南郑州 450001

Author(s):: SONG Lei; LU Chunguang; LIU Lin; LIU Shifang; WANG Yaoqiang; 1. State Grid Zhejiang Electric Power Co. , Ltd. , Hangzhou 310014, China; 2. State Grid Zhejiang Hangzhou Xiaoshan District Power Supply Co. , Ltd. , Hangzhou 311200, China; 3. School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; 4. Henan Engineering Research Center of Power Electronics and Energy Systems, Zhengzhou University, Zhengzhou 450001, China

Keywords:: mobile energy storage system; LiFePO4 battery; amper-hour integral method; adaptive unscented Kalman filter

摘要:: 鉴于磷酸铁锂电池特性曲线存在电压平台区,以及电压、电流存在测量误差,导致估计其荷电状态十分困难。为了提高磷酸铁锂电池在电压平台区荷电状态估计的准确性,提出了一种基于安时积分法的改进卡尔曼滤波算法。首先,分别采用安时积分法和 AUKF 算法估计磷酸铁锂电池 SOC。其次,计算 2 种算法估计值的增量,利用 2 种估计算法的特性,通过对比增量关系以确定最优估计值,对 AUKF 算法的估计结果进行修正。最后,所提方法的有效性在磷酸铁锂电池多种运行工况下得到了验证。实验结果表明:所提方法在存在电压偏差的情况下,能保持 SOC 估计误差小于 0. 02,实现了 SOC 的准确估计。

Abstract:: It was difficult to estimate the state of charge of LiFePO4 battery due to a large voltage platform area and voltage and current measurement errors. In order to improve the accuracy of estimation of the state of charge of lithium iron phosphate batteries in voltage platform area, an improved Kalman filter algorithm based on amper-hour integral method was proposed. Firstly, amper-hour integration method and AUKF algorithm were used to estimate SOC of lithium iron phosphate batteries. Secondly, the increment of the estimated value of the two algorithms was calculated. The characteristics of the two estimation algorithms were used to determine the optimal estimated value by comparing the increment relationship, and the estimated result of the AUKF algorithm was corrected. Finally, the effectiveness of the proposed method had been verified in various operating conditions of lithium iron phosphate batteries. Experimental results showed that the proposed method could keep the SOC estimation error less than 0. 02 under the condition of voltage deviation, and achieve accurate SOC estimation.