张涛,陈东明,侯鹏鹏,等.基于变遗忘因子的改进卡尔曼滤波锂电池荷电状态估算研究[J].河南理工大学学报（自然科学版）,doi:10.16186/j.cnki.1673-9787.2022030035

ZHANG T, CHEN D M, HOU P P, et al.Estimation the state of charge of lithium battery based on variable forgetting factor and improved extend Kalman filter[J].Journal of Henan Polytechnic University(Natural Science) ,doi:10.16186/j.cnki.1673-9787.2022030035

基于变遗忘因子的改进卡尔曼滤波锂电池荷电状态估算研究(网络首发)

张涛,陈东明,侯鹏鹏,王尧彬

河南理工大学 电气工程与自动化学院，河南 焦作 454000

摘要:为了解决锂电池荷电状态（SOC）估算不精准的问题，本文提出变遗忘因子最小二乘（VFF- RLS）与自适应平方根无迹卡尔曼滤波（ASRUKF）算法联合估算SOC。首先使用VFF-RLS对模 型参数辨识，根据估算误差实时改变新旧数据权重，防止波动过程辨识精度下降；然后使用 ASRUKF算法估算SOC，该算法结合平方根滤波，将自适应算法和平方根无迹卡尔曼滤波结 合，确保协方差矩阵非负定，并且能够实时更新噪声；接着搭建实验平台，查找电池手册选择 合适的工况实验，利用平台对选择的电池进行实验，将获得的电流电压代入算法进行计算，并 与其他算法进行比较。结果表明，在复杂的试验工况下，跟传统算法比较，改进算法的参数辨 识速度明显加快，在估算SOC阶段，最大波动误差为0.413%，波动范围明显变小，在实际值 误差较大的情况下，依然能够迅速收敛，证明算法的改进是切实可行的，可用于实际电池研究。

关键词:锂电池;变遗忘因子;荷电状态;自适应滤波;平方根滤波

中图分类号：TM912

doi:10.16186/j.cnki.1673-9787.2022030035

基金项目：国家自然科学基金资助项目(U1804143)；河南省科技攻关项目(202102210295)；河南省高校基本科研业务费专项项目（NSFRF210424）；河南省科技创新团队基金资助项目（CXTD2017085）；河南理工大学青年骨干教师资助项目（2019XQG-17)

收稿日期:2022-03-14

修回日期:2022-12-23

网络首发日期:2023-11-30

Estimation the state of charge of lithium battery based on variable forgetting factor and improved extend Kalman filter（Online）

ZHANG Tao, CHEN Dongming, HOU Pengpeng, WANG Yaobin

School of Electrical Engineering and Automation， Henan Polytechnic University， Jiaozuo 454000， Henan， China

Abstract:To figure out the problem of imprecise estimation of state of charge(SOC) of lithium-ion battery, this paper put forward estimate the SOC by the combination of the variable forgetting factor-recursive least squares(VFF-RLS) and the multi-information extended Kalman filter(MIEKF) algorithm. Primarily, VFF-RLS was used to identify model parameters, the ratio of the old and new data was varied in real-time on the basis of the estimation error. After, MIEKF algorithm was used to estimate SOC. integrated with multi-information identification theory, this method works by reusing error information to reduce the influence of individual necrosis data on the experiment. MIEKF algorithm was used to estimate SOC, and the error information was reused to reduce the influence of individual necrosis data on the experiment. Then setting up the experimental platform, selecting the appropriate working condition experiment according to the battery manual to, the platform was used to conduct experiments on the selected battery and calculate according to the set input current, compared with other algorithms. Test results show that under the complex conditions, compare with traditional algorithm, the improved algorithm of the apparent acceleration parameter identification, SOC estimation stage, the maximum error of the volatility is 0.533%, range decreased significantly, and the actual value error is bigger, still can fast convergence, improved algorithm is practical and feasible, and can be used in the actual battery research.

Key words:Lithium-ion battery;variable forgetting factor;state of charge;adaptive filtering;square root filtering

CLC：TM912