基于UKF的锂电池SOC估计  

SOC Estimation of Lithium Battery Based on UKF

作　　者：郭浩宇 马明慧 张玉华[2] 张铭扬 GUO Haoyu;MA Minghui;ZHANG Yuhua;ZHANG Mingyang(School of Vehicle and Traffic Engineering,Zhengzhou University of Science and Technology,Zhengzhou 450064,China;School of Mechanical Engineering,Zhengzhou University of Science and Technology,Zhengzhou 450064,China)

机构地区：[1]郑州科技学院车辆与交通工程学院,河南郑州450064 [2]郑州科技学院机械工程学院,河南郑州450064

出　　处：《通信电源技术》2025年第5期104-106,共3页Telecom Power Technology

基　　金：河南省教育厅2024年大学生创新创业训练计划项目“车用动力锂电池组智能均衡充放电管理系统设计”(202412746031);郑州科技学院2024大学生创新创业训练计划项目“车用动力锂电池组智能均衡充放电管理系统设计”(DC202431)。

摘　　要：针对电动汽车用锂电池荷电状态(State Of Charge,SOC)估计精确度问题,提出一种基于无迹卡尔曼滤波(Unscented Kalman Filter,UKF)的SOC估计方法。通过搭建一阶电阻-电容(Resistance Capacitance,RC)等效电路模型并结合UKF估计SOC,基于MATLAB/Simulink搭建仿真模型验证算法的精确度与可行性。结果表明,该方法测试的估计SOC值能够快速收敛到真实SOC值,与实际荷电量SOC误差控制在0.5%以内,效率相对较高,能够有效延长电池的使用寿命。A SOC estimation method based on Unscented Kalman Filter(UKF)is proposed to address the accuracy issue of State Of Charge(SOC)estimation for lithium batteries used in electric vehicles.By building a first-order Resistance Capacity(RC)equivalent circuit model and combining it with UKF to estimate SOC,a simulation model was constructed based on MATLAB/Simulink to verify the accuracy and feasibility of the algorithm.The results show that the estimated SOC value tested by this method can quickly converge to the true SOC value,with an error control of less than 0.5%compared to the actual SOC value.The efficiency is relatively high,and it can effectively extend the service life of the battery.

关 键 词：锂电池 无迹卡尔曼滤波(UKF) 荷电状态(SOC) 

分 类 号：TM9[电气工程—电力电子与电力传动]