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

doi:10.3969/j.issn.1008-0198.2025.05.004

湖南电力 ›› 2025, Vol. 45 ›› Issue (5): 25-32.doi: 10.3969/j.issn.1008-0198.2025.05.004

• 专家专栏：配网灵活储能技术 • 上一篇下一篇

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

黄梓涵, 曾进辉, 刘颉, 李梓谦, 宁佳伟

湖南工业大学交通与电气工程学院, 湖南株洲 412007

收稿日期:2025-07-07 修回日期:2025-07-22 发布日期:2025-11-11
通信作者: 黄梓涵(2000),男,硕士研究生,研究方向为电池管理系统。
作者简介:曾进辉（1981）,男,教授,博士生导师,主要研究方向为电力电子变换与控制刘颉（1989）,男,博士,硕士生导师,主要研究方向为负荷预测、电能质量检测和多能互补调度。
基金资助:
国家自然科学基金面上项目（52377185）

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

HUANG Zihan, ZENG Jinhui, LIU Jie, LI Ziqian, NING Jiawei

School of Traffic and Electrical Engineering, Hunan University of Technology, Zhuzhou 412007, China

Received:2025-07-07 Revised:2025-07-22 Published:2025-11-11

摘要/Abstract

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。

关键词: 锂离子电池, 荷电状态估计, 改进型多新息无迹卡尔曼滤波（MIUKF）, GWO-SVD-MIUKF

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.

Key words: lithium-ion battery, state of charge(SOC) estimation, improved multi-innovation unscented Kalman filter(MIUKF), GWO-SVD-MIUKF

中图分类号:

TM911

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.

HUANG Zihan, ZENG Jinhui, LIU Jie, LI Ziqian, NING Jiawei. Research of SOC Estimation of Lithium-Ion Battery Based on GWO-SVD-MIUKF Hybrid Algorithm[J]. Hunan Electric Power, 2025, 45(5): 25-32.

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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

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

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