基于加权多新息改进Sage-Husa AEKF的锂离子电池SOC估计研究

宋维华; 刘冉冉; 金晓娜

doi:doi:10.19912/j.0254-0096.tynxb.2025-0719

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

基于加权多新息改进Sage-Husa AEKF的锂离子电池SOC估计研究

更新时间：2026-02-06

- 基于加权多新息改进Sage-Husa AEKF的锂离子电池SOC估计研究
- Vol. 47, Issue 1, Pages: 797-806(2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2025-0719
  CLC： TM912
- Online First：05 February 2026，
  
  Published：05 February 2026
- 稿件说明：
移动端阅览
宋维华, 刘冉冉, 金晓娜. 基于加权多新息改进Sage-Husa AEKF的锂离子电池SOC估计研究[J]. 2026, 47(1): 797-806.
DOI：

宋维华, 刘冉冉, 金晓娜. 基于加权多新息改进Sage-Husa AEKF的锂离子电池SOC估计研究[J]. 2026, 47(1): 797-806. DOI： doi:10.19912/j.0254-0096.tynxb.2025-0719.

摘要

针对扩展卡尔曼滤波（EKF）在锂离子电池荷电状态（SOC）估计中的精确性问题

以二阶RC等效电路模型为基础

采用多新息最小二乘算法（MIRLS）实现锂离子电池模型参数的在线辨识

在此基础上提出加权多新息改进Sage-Husa自适应扩展卡尔曼滤波（WMISAEKF）算法

解决噪声协方差更新致使滤波发散的问题并提出一种新的权重计算方法

充分利用历史新息

合理分配新息权重

可实现SOC的准确估计。仿真实例对提出的改进SOC估计算法进行性能验证

结果表明改进算法在更新过程中收敛性强、鲁棒性好

在关键误差指标上取得突破性进展

相比于EKF算法

平均绝对误差（MAE）和均方根误差（RMSE）分别降低了89.01%和79.06%

充分证实了所构建方法在电池状态估计精度上的显著提升

可为延长电池的使用寿命提供新的实践支撑。

Abstract

To address the accuracy issue of Extended Kalman Filter （EKF）in State of Charge （SOC） estimation for lithium-ion batteries

this study is based on a second-order RC equivalent circuit model and uses the Multi-Innovation Recursive Least Squares （MIRLS） algorithm for online identification of lithium-ion battery model parameters. On this foundation

we propose a Weighted Multi-innovation Improved Sage-Husa Adaptive Extended Kalman Filter（WMISAEKF） algorithm to resolve the issue of filter divergence caused by noise covariance updates. Furthermore

we introduce a novel weight calculation method that fully utilizes historical innovations and rationally allocates innovation weights

thereby achieving accurate SOC estimation. Simulation examples are used to validate the performance of the proposed improved SOC estimation algorithm. The results demonstrate that the improved algorithm exhibits strong convergence and robustness during the update process

achieving significant advancements in key error metrics. Compared to the EKF algorithm

the Mean Absolute Error （MAE） and Root Mean Square Error （RMSE） are decreased by 89.01% and 79.06%

respectively. This clearly confirms the substantial enhancement in SOC estimation accuracy for lithium-ion batteries provided by the proposed method

providing new practical support for extending battery lifespan.

关键词

Keywords

references

谭必蓉, 杜建华, 叶祥虎, 等. 基于模型的锂离子电池SOC估计方法综述[J]. 储能科学与技术, 2023, 12(6): 1995-2010.TAN B R, DU J H, YE X H, et al. Overview of SOC estimation methods for lithium-ion batteries based on models[J]. Energy storage science and technology, 2023, 12(6): 1995-2010.

UL HASSAN M, SAHA S, HAQUE M E, et al.A comprehensive review of battery state of charge estimation techniques[J]. Sustainable energy technologies and assessments, 2022, 54: 102801.

ALI M U, ZAFAR A, NENGROO S H, et al.Towards a smarter battery management system for electric vehicle applications: a critical review of lithium-ion battery state of charge estimation[J]. Energies, 2019, 12(3): 446.

高俊东, 马志强, 刘广忱, 等. 融合MHSA的锂电池SOC估计[J]. 太阳能学报, 2025, 46(3): 16-24.GAO J D, MA Z Q, LIU G C, et al. SOC estimation of lithium battery based on MHSA[J]. Acta energiae solaris sinica, 2025, 46(3): 16-24.

李谦, 姜帆, 韩乔妮, 等. 基于一阶ECM-IGPR的锂离子电池SOC及SOH联合估计框架[J]. 太阳能学报, 2024, 45(5): 240-250.LI Q, JIANG F, HAN Q N, et al. Joint first order SOC and SOH estimation framework for LI-ION BAttery based on ECM-IGPR[J]. Acta energiae solaris sinica, 2024, 45(5): 240-250.

高铭琨, 徐海亮, 吴明铂. 基于等效电路模型的动力电池SOC估计方法综述[J]. 电气工程学报, 2021, 16(1): 90-102.GAO M K, XU H L, WU M B. Review of SOC estimation methods for power battery based on equivalent circuit model[J]. Journal of electrical engineering, 2021, 16(1): 90-102.

李洪达. 基于自适应滑模观测器的荷电状态估算方法[J]. 电气技术与经济, 2023(2): 36-39, 44.LI H D. State of charge estimation method based on adaptive sliding mode observer[J]. Electrical equipment and economy, 2023(2): 36-39, 44.

SUN D M, YU X L, WANG C M, et al.State of charge estimation for lithium-ion battery based on an Intelligent adaptive extended kalman filter with improved noise estimator[J]. Energy, 2021, 214: 119025.

LI X Y, WANG Z P, ZHANG L.Co-estimation of capacity and state-of-charge for lithium-ion batteries in electric vehicles[J]. Energy, 2019, 174: 33-44.

ROUT S, DAS S.A noise covariance regulated robust modified adaptive extended Kalman filter for state of charge estimation of lithium-ion battery[J]. IEEE access, 2024, 12: 78434-78448.

巫春玲, 付俊成, 徐先峰, 等. 基于多新息最小二乘和多新息扩展卡尔曼滤波算法的锂电池SOC估计[J]. 华南理工大学学报(自然科学版), 2024, 52(2): 74-83.WU C L, FU J C, XU X F, et al. Lithium battery SOC estimation based on multi innovation least square and multi innovation extended Kalman filter algorithm[J]. Journal of South China University of Technology(natural science edition), 2024, 52(2): 74-83.

董磊, 赖纪东, 苏建徽, 等. 基于IMAFFRLS-EKF的锂电池在线参数辨识和SOC估计方法[J]. 太阳能学报, 2024, 45(6): 66-74.DONG L, LAI J D, SU J H, et al. Online parameter identification and soc estimation of lithium battery based on IMAFFRLS-EKF[J]. Acta energiae solaris sinica, 2024, 45(6): 66-74.

WANG H, ZHENG Y P, YU Y.Lithium-ion battery SOC estimation based on adaptive forgetting factor least squares online identification and unscented Kalman filter[J]. Mathematics, 2021, 9(15): 1733.

罗玉涛, 吴志强. 基于DAEKF算法的锂离子电池主要状态在线联合估计[J]. 华南理工大学学报(自然科学版), 2023, 51(1): 84-94.LUO Y T, WU Z Q. Online joint estimation of main states of lithium-ion battery based on DAEKF algorithm[J]. Journal of South China University of Technology(natural science edition), 2023, 51(1): 84-94.

路金涛, 彭珍瑞. 自适应窗长改进Sage-Husa卡尔曼滤波的结构响应重构[J]. 计算力学学报, 2025, 42(2): 212-220.LU J T, PENG Z R. Structural response reconstruction of adaptive window length improved Sage-Husa Kalman filter[J]. Chinese journal of computational mechanics, 2025, 42(2): 212-220.

乔家璐, 王顺利, 于春梅, 等. 基于加权多新息AEKF的锂电池SOC在线估算[J]. 储能科学与技术, 2021, 10(6): 2318-2325.QIAO J L, WANG S L, YU C M, et al. Novel multiple weighted-AEKF method for online state-of-charge estimation of lithium-ion batteries[J]. Energy storage science and technology, 2021, 10(6): 2318-2325.

田晟, 吕清, 李亚飞. 基于STEKF的锂离子动力电池SOC估算[J]. 华南理工大学学报(自然科学版), 2020, 48(2): 69-75.TIAN S, LÜ Q, LI Y F. SOC estimation of Li-ion power battery based on STEKF[J]. Journal of South China University of Technology(natural science edition), 2020, 48(2): 69-75.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

基于MODE算法的光伏逆变器机电暂态模型LVRT控制方式与控制参数辨识研究

基于CEEMDAN和GA-BILSTM的锂离子电池剩余使用寿命预测

基于多尺度分解和多模型融合的锂电池剩余使用寿命预测

飞轮储能系统含参数辨识的双矢量交替顺序模型预测控制

混合型超级电容器实测分析与建模方法

Related Author

徐恒山

王思维

张旭军

李晨阳

黄永章

徐鹏

冉文文

黄媛

Related Institution

State Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University

College of Electric Power, Inner Mongolia University of Technology

Engineering Research Center of Large-scale Energy Storage Technology, Ministry of Education

College of Data Science and Application, Inner Mongolia University of Technology

School of Mechatronics and Vehicle Engineering, Chongqing Jiaotong University

AI问答

⁰