SOH estimation of lithium-ion batteries based on LSTM&GRU-Attention multijoint model

doi:10.19799/j.cnki.2095-4239.2023.0514

Abstract

Abstract:

The accurate estimation of the state-of-health (SOH) of lithium-ion batteries (LiBs) plays a critical role in ensuring the stable and efficient operation of energy storage systems. This study proposes a fusion model based on cross-validation-trained linear regression weighting to enhance the precision of data-driven methods for SOH estimation. First, health features are extracted from the discharge voltage curve as well the as charging and discharging temperature curves. Second, Pearson correlation coefficients are used to analyze the selected features, determining the health-indicator parameters for the network model inputs. Finally, attention mechanisms were incorporated into the long short-term memory (LSTM) and gated recurrent unit (GRU) to establish the LSTM-Attention and GRU-Attention models, respectively. These models are trained using the first 50% of data from NASA's battery aging datasets, B0005, B0006, B0007, and B0018, with the remaining 50% used for validation. The LSTM- and GRU-Attention models produce SOH estimates of $y^L - A$ and $y^G - A$ , respectively. Then, the fusion model proposed in this study performs linear regression weighting on these two estimates, yielding a maximum root mean square error (RMSE) and mean absolute error (MAE) of 0.00291 and 0.00200, respectively. Furthermore, the robustness of the proposed model is demonstrated by subjecting the health factors input to various proportions of Gaussian white noise. The results indicate that the fusion model exhibits strong resistance to interference, with a maximum RMSE and MAE of only 0.03562 and 0.02889, respectively.

Key words: lithium-ion battery, state of health, health indicator, LSTM-Attention, GRU-Attention, weighted linear regression

CLC Number:

TM 911

Baihai MAO, Wu QIN, Xianbin XIAO, Zongming ZHENG. SOH estimation of lithium-ion batteries based on LSTM&GRU-Attention multijoint model[J]. Energy Storage Science and Technology, 2023, 12(11): 3519-3527.

Figures/Tables 13

Table 1

Fig. 1

Fig. 2

Fig. 3

Table 2

Evaluation table of Pearson correlation coefficients"

项目	$F 1$	$F 2$	$F 3$	$F 4$	$F 5$	$F 6$
B0005	-0.8345	0.9962	-0.4871	0.9999	-0.4755	0.9998
B0006	-0.9573	0.9949	-0.4897	0.9999	0.3501	0.9999
B0007	-0.8362	0.9978	-0.3147	0.9997	0.0407	0.9992
B0018	-0.8997	0.9978	0.1176	0.9998	-0.1413	0.9994

Table 2

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Table 3

Fig. 10

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项目	充电			放电		其他
项目	电流/A	电压/V	截止电流/mA	恒流/A	截止电压/V	初始容量/Ah
B0005	1.5	4.2	20	2	2.7	1.86
B0006	1.5	4.2	20	2	2.5	2.04
B0007	1.5	4.2	20	2	2.2	1.89
B0018	1.5	4.2	20	2	2.5	1.86

评价指标	模型	电池
评价指标	模型	B0005	B0006	B0007	B0018
MAE	LSTM	0.02526	0.01629	0.01468	0.01550
	GRU	0.02143	0.01305	0.00947	0.01462
	LSTM-Attention	0.01019	0.00807	0.00723	0.01142
	GRU-Attention	0.00757	0.00672	0.00609	0.00840
	线性回归加权法	0.00079	0.00200	0.00138	0.00184
RMSE	LSTM	0.02911	0.02012	0.01545	0.01669
	GRU	0.02368	0.01349	0.01047	0.01523
	LSTM-Attention	0.01026	0.00903	0.00892	0.01360
	GRU-Attention	0.00880	0.00772	0.00777	0.00998
	线性回归加权法	0.00146	0.00291	0.00232	0.00218

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