联合改进滑模观测器的自适应卡尔曼滤波荷电状态估计  

State of Charge Estimation Using Adaptive Kalman Filter with Improved Sliding Mode Observer

作　　者：钱伟[1,2] 王浩宇 郭向伟 李万 Qian Wei;Wang Haoyu;Guo Xiangwei;Li Wan(School of Electrical Engineering and Automation Henan Polytechnic University,Jiaozuo 454003 China;Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment Henan Polytechnic University,Jiaozuo 454003 China)

机构地区：[1]河南理工大学电气工程与自动化学院,焦作454003 [2]河南理工大学河南省煤矿装备智能检测与控制重点实验室,焦作454003

出　　处：《电工技术学报》2025年第6期1984-1994,共11页Transactions of China Electrotechnical Society

基　　金：国家自然科学基金项目(62373137);河南省高校基本科研业务费项目(NSFRF210332,NSFRF230604)资助。

摘　　要：锂电池荷电状态(SOC)的精确估计对于提高电池能量利用率、保障电池安全运行具有重要意义。针对模型不确定性导致基于卡尔曼滤波(KF)的SOC估计方法精度低的问题,提出一种联合改进型滑模观测器(ISMO)的自适应扩展卡尔曼滤波(AEKF)算法,以实现SOC高精度估计。首先,基于双极化(DP)等效电路模型建立融合饱和函数的ISMO,以降低传统滑模观测器的抖振。其次,设计一种新型自适应衰减因子,以降低过往陈旧测量数据对扩展卡尔曼滤波估计结果的影响,并基于融合饱和函数的ISMO,实现联合ISMO的AEKF估计方法设计。最后,基于自主实验平台获取实测模拟工况数据搭建仿真模型,验证了所提ISMO_AEKF算法在不同工况下,相比于AEKF、ISMO_EKF和其他同类型联合算法,具有更高的估计精度及鲁棒性。Accurate estimation of a lithium battery’s state of charge(SOC)is of great significance for improving energy utilization efficiency and ensuring a safe operation.This paper addresses the study of SOC estimation based on the Kalman filter(KF).The traditional KF algorithms rely on all past measurements to estimate the state variables at the next moment,ignoring the weight of current measurements,resulting in weak tracking ability and low estimation accuracy under steady-state conditions of KF.This paper proposes an adaptive extended Kalman filter(AEKF)algorithm with a jointly improved sliding mode observer(ISMO)to improve SOC estimation accuracy and robustness.First,an improved sliding mode observer incorporating the saturation function is built based on a dual-polarization(DP)equivalent circuit model that can balance estimation accuracy and computational complexity.The saturation function can switch the control outside the boundary layer in real-time and implement the linearized feedback control inside the boundary layer,significantly reducing the chattering of the traditional sliding mode observer.Second,a novel adaptive fading factor is introduced based on the extended Kalman filter(EKF),which strengthens the role of the present observation data while weakening the unfavorable influence of the stale measurements.Thus,the EKF algorithm’s tracking performance and estimation accuracy are enhanced.Finally,the robustness of the improved sliding mode observer system is utilized to predict the state vectors of the system and alleviate the problem of filter dispersion caused by modeling inaccuracies.Accordingly,an adaptive extended Kalman filter with the improved sliding mode observer(ISMO_AEKF)algorithm is established,which combines the advantages of AEKF and ISMO.Based on the self-built experimental platform,the Sanyo NCR18650GA 3.5 A·h lithium ternary battery is taken as the experimental object to obtain the measured simulated working condition data.The estimation accuracy and robustness of the joint algorithm a

关 键 词：荷电状态 饱和函数 滑模观测器 自适应衰减因子 卡尔曼滤波 

分 类 号：TM912[电气工程—电力电子与电力传动]