基于蜣螂算法优化卡尔曼滤波的锂离子电池模型参数辨识

doi:10.11930/j.issn.1004-9649.202401111

中国电力 ›› 2025, Vol. 58 ›› Issue (1): 196-204.DOI: 10.11930/j.issn.1004-9649.202401111

• 新能源与储能 • 上一篇

基于蜣螂算法优化卡尔曼滤波的锂离子电池模型参数辨识

夏天¹(), 刘代飞²(), 岳家辉¹, 陈来恩¹, 李亦梁³

1. 长沙理工大学电气与信息工程学院，湖南长沙　410114
2. 长沙理工大学能源与动力工程学院，湖南长沙　410114
3. 长高电新科技股份公司，湖南长沙　410219

收稿日期:2024-01-25 出版日期:2025-01-28 发布日期:2025-01-23
作者简介:夏天（1993—），男，通信作者，硕士研究生，从事储能安全与控制技术研究，E-mail：862551450@qq.com
刘代飞（1975—），男，副教授，从事电储能安全与控制技术研究，E-mail：dfcanfly@126.com
基金资助:
国家自然科学基金资助项目（柔性直流输电交流侧故障下换流器多桥臂主动应对的能量调控机理及穿越控制研究，51977014）。

Estimation of Model Parameters of Lithium Batteries Based on Kalman Filtering Optimized by Dung Beetle Algorithm

Tian XIA¹(), Daifei LIU²(), Jiahui YUE¹, Laien CHEN¹, Yiliang Li³

1. School of Electrical and Information Engineering, ChangSha University of Science and Technology, Changsha 410114, China
2. School of Energy and Power Engineering, Changsha University of Science and Technology, Changsha 410114, China
3. ChangGao Dianxin Science and Technology Co., Ltd., Changsha 410219, China

Received:2024-01-25 Online:2025-01-28 Published:2025-01-23
Supported by:
This work is supported by National Natural Science Foundation of China (Research on Energy Regulation Mechanism and Crossing Control of Multi-bridge Arm Active Response of Converter under AC side Fault in flexible DC Transmission, No.51977014).

摘要/Abstract

摘要：

锂离子电池参数辨识结果是电池状态预测的重要基础，提出了一种基于蜣螂算法（dung beetle optimizer，DBO）优化卡尔曼滤波（Kalman filtering，KF）的方法，用以在线辨识电池模型参数。该方法利用DBO快速全局寻找最优解特点，在KF算法中优化过程噪声和观测噪声的协方差矩阵，提高了识别电池模型参数的准确性。仿真实验数据表明，相较于未优化的KF参数辨识的结果，所提方法辨识误差有明显减少，预测的参数值更加接近真实值。

关键词: 锂离子电池, 参数辨识, 卡尔曼滤波器, 蜣螂算法, 协方差矩阵

Abstract:

The identification of parameters for lithium batteries is an important basis for battery state prediction. An improved kalman filtering (KF) based on dung beetle optimizer (DBO) is proposed for online identification of battery model parameters. This method utilizes the rapid global search for optimal solutions characteristic of DBO to optimize the covariance matrices of process noise and observation noise in KF, thereby improving the accuracy of identifying battery model parameters. Simulation experiment data shows that compared to the parameter identification results based on unoptimized KF, the variance that the identification results of this method compared to the true values are significantly reduced, resulting in the predicted parameter values are closer to the true values.

Key words: lithium battery, parameter identification, kalman filter, dung beetle optimizer, covariance matrix

夏天, 刘代飞, 岳家辉, 陈来恩, 李亦梁. 基于蜣螂算法优化卡尔曼滤波的锂离子电池模型参数辨识[J]. 中国电力, 2025, 58(1): 196-204.

Tian XIA, Daifei LIU, Jiahui YUE, Laien CHEN, Yiliang Li. Estimation of Model Parameters of Lithium Batteries Based on Kalman Filtering Optimized by Dung Beetle Algorithm[J]. Electric Power, 2025, 58(1): 196-204.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202401111

https://www.electricpower.com.cn/CN/Y2025/V58/I1/196

图/表 13

图 1 2阶RC等效电路模型

Fig.1 Second order RC equivalent circuit model

图 2 DBO-FK算法流程

Fig.2 Flow of DBO-KF algorithm

图 3 基于蜣螂算法优化卡尔曼滤波的示意

Fig.3 Schematic of improved kalman filter based on dung beetle optimizer

表 1 18650号电池基础参数

Table 1 Basic parameters of No. 18650 battery

参数	数值	参数	数值
额定容量/(mA·h)	2980	重量/g	50.0
额定电压/V	3.6	充电温度/℃	+10~+45
放大放电电流/A	10	放电温度/℃	–20~+60
最大充电电流/A	4	循环使用寿命/次	1000
能量密度/(W·h·g^–1)	218	尺寸/mm	65.1×18.25

图 4 实验对象与实验平台

Fig.4 Experimental subjects and platform

图 5 放电电流与端电压变化曲线

Fig.5 Discharge current and terminal voltage variation curve

图 6 4种动态工况下的放电曲线

Fig.6 Discharge curves under four types dynamic operating conditions

图 7 蜣螂算法适应度曲线

Fig.7 Fitness curve of DBO-KF

图 8 蜣螂算法优化前后端电压对比

Fig.8 Comparison terminal voltage before and after DBO-KF optimization

图 9 电池模型中各参数的辨识结果

Fig.9 Identification results of various parameters in battery model

表 2 不同SOC值下参数辨识结果

Table 2 Parameter identification results under different SOC

SOC/%	R₀/Ω	R₁/Ω	R₂/Ω	C₁/F	C₂/F
50	0.0090	0.1920	0.0943	9.8453	4.8505
40	0.0559	0.0394	0.0360	151.2994	493.9040
30	0.0561	0.0677	0.0240	130.8150	342.5920
20	0.0568	0.0568	0.0034	135.6240	310.9530
10	0.0592	0.0286	0.0042	908.3162	191.9880
0	0.0692	0.0624	-0.9259	405.8970	138.5590

图 10 4种动态工况下DBO-KF算法辨识曲线

Fig.10 Identification curves using DBO-KF under four types dynamic operating conditions

表 3 端电压误差值可信度指标对比

Table 3 Comparison of the credibility indexs of terminal voltage error

工况	E_MS	E_RMS	E_MA
DST	0.02990	0.000894	0.0134
FUDS	0.00077	0.027800	0.0149
US60	0.00120	0.035300	0.0163
BJDST	0.01700	0.020800	0.0169

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基于蜣螂算法优化卡尔曼滤波的锂离子电池模型参数辨识

Estimation of Model Parameters of Lithium Batteries Based on Kalman Filtering Optimized by Dung Beetle Algorithm

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基于蜣螂算法优化卡尔曼滤波的锂离子电池模型参数辨识

Estimation of Model Parameters of Lithium Batteries Based on Kalman Filtering Optimized by Dung Beetle Algorithm

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