基于STF-AUKF算法的退役动力电池SOC估计研究  

Research on SOC Estimation of Retired Power Batteries Based on STF-AUKF Algorithm

作　　者：刘甲星 耿煜航 范佳玮 刘申昂 申亚楠 孙霞[1] LIU Jiaxing;GENG Yuhang;FAN Jiawei;LIU Shenang;SHEN Yanan;SUN Xia(School of Intelligent Equipment,East University of Science and Technology,Tai'an,Shandong 271000,China)

机构地区：[1]山东科技大学智能装备学院,山东泰安271000

出　　处：《移动信息》2025年第3期346-348,351,共4页Mobile Information

基　　金：2024年全国大学生创新创业训练项目(202410424096)。

摘　　要：针对电动汽车退役电池在梯次利用过程中荷电状态(SOC)估计精度不足的技术难题,文中创新性地开发了一种基于强跟踪无迹卡尔曼滤波(STF-AUKF)的SOC估算方法。首先,依据二阶RC等效电路模型对退役动力电池进行建模,随后通过混合动力脉冲(HPPC)测试对模型参数进行系统辨识与验证,确保了模型的可靠性。在此基础上,运用STF-AUKF算法实现了退役动力电池SOC的精确估算。实验数据表明,该算法在SOC估算过程中展现出优异的性能,其平均估算误差控制在1.23%以内,充分验证了该方法在工程应用中的精确性和实用价值。In response to the technical challenge of insufficient accuracy in estimating the State of Charge(SOC)of retired electric vehicle batteries during the cascading utilization process,this paper innovatively develops a SOC estimation method based on Strong Tracking Unscented Kalman Filter(STF-AUKF).Firstly,the retired power battery was modeled based on the second-order RC equivalent circuit model,and then the model parameters were systematically identified and verified through hybrid power pulse(HPPC)testing to ensure the reliability of the model.On this basis,the STF-AUKF algorithm was used to achieve accurate estimation of the SOC of retired power batteries.Experimental data shows that the algorithm exhibits excellent performance in SOC estimation,with an average estimation error controlled within 1.23%,fully verifying the accuracy and practical value of the method in engineering applications.

关 键 词：退役动力电池:SOC估计 参数辨识 STF-AUKF算法 

分 类 号：TM912[电气工程—电力电子与电力传动]