Research of SOC Estimation of Lithium-Ion Battery Based on GWO-SVD-MIUKF Hybrid Algorithm

黄梓涵; 曾进辉; 刘颉; 李梓谦; 宁佳伟

doi:10.3969/j.issn.1008-0198.2025.05.004

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Research of SOC Estimation of Lithium-Ion Battery Based on GWO-SVD-MIUKF Hybrid Algorithm

更新时间：2026-01-10

- Research of SOC Estimation of Lithium-Ion Battery Based on GWO-SVD-MIUKF Hybrid Algorithm
- Vol. 45, Issue 5, Pages: 25-32(2025)
- 作者机构：
  
  湖南工业大学交通与电气工程学院,湖南,株洲,412007
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1008-0198.2025.05.004
  CLC： TM911
- Published Online：11 November 2025，
  
  Published：11 November 2025
- 稿件说明：
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黄梓涵, 曾进辉, 刘颉, 李梓谦, 宁佳伟. 基于GWO的改进型多新息无迹卡尔曼滤波方法的锂离子电池SOC预测研究[J]. 湖南电力, 2025, 45(5): 25-32.

黄梓涵, 曾进辉, 刘颉, et al. Research of SOC Estimation of Lithium-Ion Battery Based on GWO-SVD-MIUKF Hybrid Algorithm[J]. 2025, 45(5): 25-32.
黄梓涵, 曾进辉, 刘颉, 李梓谦, 宁佳伟. 基于GWO的改进型多新息无迹卡尔曼滤波方法的锂离子电池SOC预测研究[J]. 湖南电力, 2025, 45(5): 25-32. DOI： 10.3969/j.issn.1008-0198.2025.05.004.

黄梓涵, 曾进辉, 刘颉, et al. Research of SOC Estimation of Lithium-Ion Battery Based on GWO-SVD-MIUKF Hybrid Algorithm[J]. 2025, 45(5): 25-32. DOI： 10.3969/j.issn.1008-0198.2025.05.004.

摘要

基于荷电状态对电池管理系统的重要性

提出一种基于灰狼优化算法的改进型多新息无迹卡尔曼滤波的荷电状态估计方法。该方法融合灰狼优化算法（grey wolf optimizer

GWO）与奇异值分解改进的多新息无迹卡尔曼滤波方法(SVD-based multi-innovation unscented kalman filter

SVD-MIUKF)

在参数辨识与滤波结构上均进行了优化：SVD用于重构多新息无迹卡尔曼滤波中的协方差矩阵以提升数值稳定性

GWO用于辨识模型参数并动态调整SVD-MIUKF中的估计窗口

提高算法自适应性与收敛速度。基于马里兰大学公开的INR 18650-20R数据集

在多种典型工况下开展实验对比。结果表明

该算法在荷电状态估计中的误差可控制在0.20%左右

具有较高的估计精度和良好的收敛性能。

Abstract

Based on the importance of state of charge(SOC) to the battery management systems(BMS)

a Grey Wolf Optimizer enhanced SVD-based Multi-Innovation Unscented Kalman Filter(GWO-SVD-MIUKF) for SOC estimation is proposed. The method combines Grey Wolf Optimizer(GWO) with a singular value decomposition-based MIUKF(SVD-MIUKF). Both the parameter identification and the filter structure are optimized. SVD is used to reconstruct the covariance matrix in MIUKF to improve nu?merical stability

while GWO is used to identify model parameters and dynamically adjust the es?timation window

enhancing adaptability and convergence. Experiments are conducted on the public INR18650-20R dataset from the University of Maryland under various typical conditions. Results show that the proposed method achieves high estimation accuracy

with SOC error controlled within ap?proximately 0.20%

and demonstrates good convergence performance.

关键词

Keywords

references

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