A parallel multi cale-featured fusion model for state-of-health estimation of lithium-ion batteries based on relaxation voltage

doi:10.19799/j.cnki.2095-4239.2024.0808

Abstract

Abstract:

The estimation of state-of-health (SOH) in lithium-ion batteries is crucial for ensuring the reliability and safety of energy storage systems. However, existing SOH estimation methods are limited by single-feature extraction and dependency on fixed charge-discharge conditions, which hinders adaptability to dynamic operational environments. To address these challenges, we propose an SOH estimation approach based on relaxation voltage, integrating a parallel multiscale feature fusion convolution model (MSFFCM) with XGBoost. The MSFFCM model leverages multilayer stacked convolutional modules to extract deep features from relaxation voltage data while utilizing a parallel multiscale attention mechanism to enhance the capture of multiscale features. These features are then fused with statistical features to improve the model's feature extraction and integration capabilities. Bayesian optimization is applied to the XGBoost model for parameter tuning, enabling high-accuracy SOH estimation based on multi-source fused features. Validation experiments were conducted on datasets from two commercial 18650 lithium-ion battery types under varied temperature and charge-discharge strategies. Results indicate that the proposed method achieves a root mean square error and mean absolute error of less than 0.5%, significantly outperforming conventional methods. This study provides an effective estimation tool for lithium-ion battery health management without reliance on specific charge-discharge conditions, demonstrating promising potential for complex real-world applications.

Key words: lithium-ion battery, state-of-health estimation, relaxation voltage, parallel multi-scale features, feature fusion

CLC Number:

TM 912

Hairui WANG, Changyu XU, Guifu ZHU, Xiaojian HOU. A parallel multi cale-featured fusion model for state-of-health estimation of lithium-ion batteries based on relaxation voltage[J]. Energy Storage Science and Technology, 2025, 14(2): 799-811.

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数据集	电池类型	测试温度/℃	充电率/放电率/C	电池数
数据集1	NCA电池	25	0.25/1	7
	型号18650	25	0.5/1	19
	截止电压2.65~4.2 V	25	1/1	9
	额定容量3.5 Ah	35	0.5/1	3
	额定容量3.5 Ah	45	0.5/1	28

数据集2	NCM电池	25	0.5/1	23
	型号18650	35	0.5/1	4
	截止电压2.5~4.2 V	45	0.5/1	28
	额定容量3.5 Ah

参数	数值	参数	数值
Learning_rate	0.08594	Subsample	0.9434
Max_depth	10	Alpha	0.1811
n_estimators	225	Lambda	57.3173

数据集	方法	RMSE/%	MAPE/%	R²
数据集1	未经过优化	0.7315	0.6589	0.9758
数据集1	贝叶斯优化	0.4183	0.368	0.9950

数据集2	未经过优化	0.9274	0.8484	0.9661
数据集2	贝叶斯优化	0.6096	0.5514	0.9885

数据集	方法	RMSE/%	MAPE/%	R²
数据集1	M1	0.8817	0.7835	0.9778
	M2	0.8876	0.8116	0.9775
	M3	0.8273	0.7449	0.9804
	M4	2.016	1.699	0.8839
	M5	0.4183	0.368	0.9950

数据集2	M1	1.007	0.9173	0.9688
	M2	0.9681	0.8829	0.9712
	M3	0.9454	0.8726	0.9725
	M4	1.290	1.131	0.9488
	M5	0.6096	0.5514	0.9885

CNN-BOA	LSTM	Transformer	MSFFCM-XGB
Learning rate:0.0001	Learning rate:2×10^-3	Feature_size:14	Learning rate:0.08
Layer:4	Layer:2	Num_heads:2	Max_depth:10
Dropout:0.001	N_hidden:32	Num_layer:2	N_estimators:225
Batch size:448	Batch size:448	Dropout:0.1	Alphas:0.18
N_epochs:150	N_epochs:200	N_epochs:140	Subsample:0.94