双自适应CKF锂电池荷电状态估计  

Estimation of state of charge of lithium battery dual adaptive CKF

作　　者：杨宇飞 王高杰 郑艳萍[1] Yang Yufei;Wang Gaojie;Zheng Yanping(School of Mechanical and Power Engineering,Zhengzhou University,Zhengzhou 450001,China;Guangdong Foshan Lianchuang Engineering Graduate School,Foshan 528300,China)

机构地区：[1]郑州大学机械与动力工程学院,郑州450001 [2]广东佛山联创工程研究生院,佛山528300

出　　处：《电子测量技术》2024年第15期53-63,共11页Electronic Measurement Technology

基　　金：河南省重大科技专项(221100240200)资助。

摘　　要：锂电池荷电状态是锂电池安全运行最重要的状态参数,为了提高锂电池SOC的估算精度,本文提出了一种双自适应容积卡尔曼滤波算法。利用锂电池二阶DP等效电路模型做状态参数的离线辨识,使用精确度较高的容积卡尔曼滤波算法估测单个SOC,并且引入自适应因子去估测实时噪声,在获得SOC的基础对锂电池内阻实时估计,用双自适应容积卡尔曼滤波算法估测SOC。为了全面验证自己的结论符合实际工况要求,本文进行了动态压力测试、联邦城市驾驶、城市驾驶循环和城郊驾驶循环的模拟工况实验,通过算法获得前3种工况SOC的误差在0.5%以内,城郊驾驶循环工况的误差在1%以内,并且具有较强的鲁棒性,证明自己的算法成立。The state of charge of lithium batteries is the most important state parameter for the safe operation of lithium batteries.In order to improve the estimation accuracy of lithium battery SOC,this paper proposes a dual adaptive volumetric Kalman filter algorithm.The second-order DP equivalent circuit model of lithium batteries is used for offline identification of state parameters,and a high-precision volumetric Kalman filter algorithm is used to estimate a single SOC.An adaptive factor is introduced to estimate real-time noise.On the basis of obtaining SOC,the internal resistance of lithium batteries is estimated in real time,and the dual adaptive volumetric Kalman filter algorithm is used to estimate SOC.In order to fully verify that the conclusions of this paper meet the requirements of actual working conditions,this paper conducted dynamic stress testing,federal city driving,urban driving cycle and suburban driving cycle simulation experiments.The SOC errors of the first three working conditions obtained by the algorithm are within 0.5%,and the error of the suburban driving cycle working condition is within 1%,and it has strong robustness,proving that the algorithm is valid..

关 键 词：SOC 离线辨识 双自适应容积卡尔曼滤波算法 

分 类 号：TM912[电气工程—电力电子与电力传动] TN06[电子电信—物理电子学]