基于气液动力学电池模型和CKF算法的SOC估测  

SOC estimation based on gas-liquid dynamics model and CKF algorithm

作　　者：曲智伟 孙化阳 竺玉强 栗欢欢[1] QU Zhiwei;SUN Huayang;ZHU Yuqiang;LI Huanhuan(Automotive Engineering Research Institute,Jiangsu University,Zhenjiang Jiangsu 212013,China;Kaibo Energy Technology Co.,Ltd.,Changzhou Jiangsu 213000,China)

机构地区：[1]江苏大学汽车工程研究院,江苏镇江212013 [2]凯博能源科技有限公司,江苏常州213000

出　　处：《电源技术》2022年第12期1412-1416,共5页Chinese Journal of Power Sources

基　　金：江苏省自然科学基金面上项目(BK20201426,BK20181163);江苏省科技支撑计划重点项目(BE2019010)。

摘　　要：提出了一种基于容积卡尔曼滤波(CKF)的SOC估计方法。首先建立了气液动力学电池模型。通过混合脉冲功率特性(HPPC)实验获得了OCV-SOC曲线,并采用遗传算法对模型参数进行了辨识,在恒流与DST工况下验证了模型的可靠性。然后结合容积卡尔曼滤波算法对模型进行优化改进并验证。结果表明,CKF的最大估算误差为2.44%,与原始算法4.43%最大估算误差相比,CKF对原始算法改进显著。This paper proposed an SOC estimation method based on cubature Kalman filter(CKF).Firstly,a gasliquid dynamic battery model was established.The OCV-SOC curve was obtained through the hybrid pulse power characteristic(HPPC)experiment.The model parameters were identified by genetic algorithm,and the reliability of the model was verified under constant current and DST conditions.Then the cubature Kalman filter algorithm was combined with to optimize and improve the model,and the estimation method with CKF was verified under multiple working conditions.The results show that the maximum estimation error of CKF is 2.44%compared with the original algorithm(4.34%),which means CKF improves the original algorithm significantly.

关 键 词：锂离子电池 气液动力学电池模型 容积卡尔曼滤波 SOC估算 

分 类 号：TM912.9[电气工程—电力电子与电力传动]