基于粗大误差补偿和粒子滤波的锂电池SOC估计方法  

SOC Estimation Method of Lithium Battery Based on Gross Error Compensation and Particle Filter

作　　者：贺亦甲 张正江[1] 胡桂廷 洪智慧 HE Yijia;ZHANG Zhengjiang;HU Guiting;HONG Zhihui(National-local Joint Engineering Research Center of Electrical Digital Design Technology,Wenzhou University,Wenzhou 325035,China;State Grid Yongjia Electric Power Supply Company,Wenzhou 325100,China)

机构地区：[1]温州大学电气数字化设计技术国家地方联合工程研究中心,浙江温州325035 [2]国网浙江省电力有限公司永嘉县供电公司,浙江温州325100

出　　处：《控制工程》2024年第7期1155-1162,共8页Control Engineering of China

基　　金：国家自然科学基金资助项目(61703309);温州市科技计划项目(ZG2023049,H20220006)。

摘　　要：针对锂电池的荷电状态(state of charge,SOC)估计过程中测量值存在粗大误差的问题,以Thevenin等效电路模型为基础,提出了一种粗大误差补偿粒子滤波(particle filter,PF)算法。该算法可以实时地检测锂电池测量数据中可能存在的粗大误差,并对其进行分类与估计,通过补偿机制对异常测量值进行修正,提高算法的抗干扰能力。将该算法应用于锂电池SOC估计过程进行仿真验证,仿真结果表明,该算法具有较强的鲁棒性,在进行锂电池SOC估计时可以有效地抑制粗大误差的影响。To solve the problem of gross errors in the measurement values from the state of charge(SOC)estimation of lithium battery,a particle filter(PF)algorithm with gross error compensation is proposed based on Thevenin equivalent circuit model.The proposed algorithm can detect the possible gross errors in the measurement data of lithium battery in real time,classify them and estimate their magnitudes,and correct the abnormal measurement values through the compensation mechanism,thus greatly improving the anti-interference ability of the algorithm.The performance of the proposed algorithm is verified by simulation for the SOC estimation of lithium battery.The results show that the proposed algorithm has strong robustness and can effectively restrain the influence of gross errors on the SOC estimation of lithium battery.

关 键 词：锂电池 荷电状态估计 粒子滤波 粗大误差 

分 类 号：TM912[电气工程—电力电子与电力传动] TP273[自动化与计算机技术—检测技术与自动化装置]