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

doi:10.11930/j.issn.1004-9649.202401111

Abstract

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

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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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202401111

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

Figures/Tables 13

Fig.1 Second order RC equivalent circuit model

Fig.2 Flow of DBO-KF algorithm

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

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

Fig.4 Experimental subjects and platform

Fig.5 Discharge current and terminal voltage variation curve

Fig.6 Discharge curves under four types dynamic operating conditions

Fig.7 Fitness curve of DBO-KF

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

Fig.9 Identification results of various parameters in battery model

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

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

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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