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

doi:10.3969/j.issn.1008-0198.2025.05.004

欢迎来到能源电力期刊网！

English Version 读者登录期刊登录

您当前的位置：

首页 >

文章列表页 >

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

专家专栏：配网灵活储能技术 | 更新时间：2026-01-10

- 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
- 湖南电力 2025年45卷第5期页码：25-32
- 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
  中图分类号： TM911
- 网络出版：2025-11-11，
  
  纸质出版：2025-11-11
- 稿件说明：
移动端阅览
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

李豪磊,赵升,谢喜龙,等. 基于GWO-LSTM-TCN混合模型的锂电池荷电状态估计研究[J]. 电源技术,2024,48(11):2195-2200.

CHEN L P,BAO X Y,LOPES A M,et al.State of health estimation of lithium-ion batteries based on equivalent circuit model and data-driven method[J]. Journal of Energy Storage,2023,73:109195.

林正廉,卢玉斌,陈亮,等. 基于TVFRLS和SVD-UKF的锂离子电池SOC估算[J]. 电池,2023,53(6):634-638.

LIU D L,WANG S L,FAN Y C,et al.A novel fuzzy-extended Kalman filter-ampere-hour(F-EKF-Ah)algorithm based on improved second-order PNGV model to estimate state of charge of lithium-ion batteries[J]. International Journal of Circuit Theory and Applications,2022,50(11):3811-3826.

续远. 基于安时积分法与开路电压法估测电池SOC[J]. 新型工业化,2022,12(1):123-124,127.

KIM K H,OH K H,AHN H S,et al.Time-frequency domain deep convolutional neural network for Li-ion battery SoC estimation[J]. IEEE Transactions on Power Electronics,2024,39(1):125-134.

刘轩,吕炎,高杰. 基于柔性压电纤维阵列的锂离子电池荷电状态声学表征[J]. 湖南电力,2025,45(1):29-36.

张宇,李维嘉,吴铁洲. 基于BP-DCKF-LSTM的锂离子电池SOC估计[J]. 电源技术,2025,49(1):155-166.

窦元运,张成知,封居强,等. 多因素影响下融合RNN和AUKF的矿用锂离子电池SOC估计[J]. 电源技术,2025,49(4):764-771.

LI P C,JU S X,BAI S X,et al.State of charge estimation for lithium-ion btteries based on physics-embedded neural network[J]. Journal of Power Sources,2025,640:236785.

王建锋,张照震,李平. 基于加权自适应递推最小二乘法与EKF的锂离子电池SOC估计[J]. 汽车技术,2021(10):16-22.

AL-GABALAWY M,HOSNY N S,DAWSON J A,et al.State of charge estimation of a Li-ion battery based on extended Kalman filtering and sensor bias[J]. International Joural of Energy Research,2021,45(5):6708-6726.

JIANG C,WANG S L,WU B,et al.A state-of-charge estimation method of the power lithium-ion battery in complex conditions based on adaptive square root extended Kalman filter[J]. Energy,2021,219:119603.

LI W H,FAN Y,RINGBECK F,et al.Electrochemical model-based state estimation for lithium-ion batteries with adaptive unscented Kalman filter[J]. Journal of Power Sources,2020,476:228534.

MIRJALILI S,MIRJALILI S M,LEWIS A.Grey wolf optimizer[J]. Advances in Engineering Software,2014,69(3):46-61.

邢作霞,丑佳明,郭珊珊,等. 基于混合神经网络的风电场测风数据插补方法的研究[J]. 太阳能学报,2025,46(5):458-464.

王文,张晗,张擘,等. 基于GWO-RBF神经网络的车用燃料电池剩余使用寿命预测[J]. 科学技术与工程,2025,25(14):5897-5904.

邢丽坤,詹明睿,郭敏,等. 基于FFMILS-MIUKF算法的锂电池SOC估计[J]. 电子测量技术,2022,45(16):53-60.

WANG S L,ZHANG S J,WEN S F,et al.An accurate state-of-charge estimation of lithium-ion batteries based on improved particle swarm optimization-adaptive square root cubature Kalman filter[J]. Journal of Power Sources,2024,624:235594.

浏览量

142

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

锂离子电池组均衡电路设计与仿真

锂离子电池储能灭火防复燃技术研究

锂离子电池绝缘包覆材料研究进展与展望

基于机理模型的锂电池最佳放电策略研究

基于单一和多元特征的锂离子电池健康状态预测平台设计与实现