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

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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
- 基于加权多新息改进Sage-Husa AEKF的锂离子电池SOC估计研究
- 太阳能学报 2026年47卷第1期页码：797-806
- 作者机构：
- 作者简介：
- 基金信息：
  
  国家自然科学基金（62003150;52105260）;江苏省高等学校基础科学（自然科学）面上项目（21KJB120002）;江苏理工学院中吴青年创新人才支持计划
- DOI：doi:10.19912/j.0254-0096.tynxb.2025-0719
  中图分类号： TM912
- 网络首发：2026-02-05，
  
  纸质出版：2026-02-05
- 稿件说明：
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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]. 太阳能学报, 2026,47(1):797-806.

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.
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]. 太阳能学报, 2026,47(1):797-806. DOI： doi:10.19912/j.0254-0096.tynxb.2025-0719.

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

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