State of Health Estimation of On-Board Lithium-Ion Batteries Using Temporal Convolutional Network Optimized by Improved Self-Adaptive Honey Badger Algorithm

doi:10.12096/j.2096-4528.pgt.24068

Abstract

Abstract:

Objectives Lithium-ion batteries, as an important power source for new energy vehicles, require accurate state of health (SOH) estimation to design safe and reliable battery management systems. Traditional methods often overlook issues such as capacity recovery and insufficient feature effectiveness, which significantly affects estimation accuracy. To address these issues, a novel SOH estimation method for lithium-ion batteries that considers battery capacity recovery is proposed. Methods By combining the median absolute deviation with the Savitzky-Golay filter in the data preprocessing stage, the model effectively removes outliers and noise to improve the effectiveness of the features. Subsequently, feature decomposition is performed to remove redundant information and alleviate the computational load of the model. Highly correlated features are then selected as inputs for the temporal convolutional network model, reducing data dimensionality and simplifying the computational complexity of the neural network. Furthermore, an improved self-adaptive honey badger algorithm is proposed to optimize the hyperparameters of the network, accelerating model convergence and enhancing network performance. Results The proposed method has a high level of accuracy, with both the root mean squared error and the mean absolute error being lower than 0.007. Conclusions The proposed method exhibits high robustness, enabling effective SOH estimation of on-board lithium-ion batteries and meeting requirements of practical application.

Key words: energy storage, new energy, electric vehicles, lithium-ion battery, state of health estimation, improved honey badger algorithm, temporal convolutional network, data-driven, state of charge

CLC Number:

TK 02

Xiaowei ZHANG, Zhenxiao YI, Kai WANG. State of Health Estimation of On-Board Lithium-Ion Batteries Using Temporal Convolutional Network Optimized by Improved Self-Adaptive Honey Badger Algorithm[J]. Power Generation Technology, 2025, 46(6): 1154-1163.

Figures/Tables 14

Fig. 1 Capacity decay curves of each battery

Fig. 2 Processing results of B05 battery using different filtering methods

Fig. 3 Decomposition results of B05 battery capacity

Fig. 4 Capacity increment curves

Fig. 5 Variation curves of B05 battery temperature, current and voltage

Tab. 1 Correlation between charging time at constant voltage difference and battery capacity

起始电压/V	截止电压/V
起始电压/V	3.6	3.7	3.8	3.9	4.0	4.1	4.2
3.5	0.616 4	0.646 3	0.775 8	0.902 6	0.932 9	0.936 6	0.937 5
3.6	—	0.686 9	0.796 6	0.908 7	0.933 6	0.936 8	0.937 4
3.7	—	—	0.835 6	0.918 8	0.934 5	0.936 7	0.937 0
3.8	—	—	—	0.928 4	0.935 2	0.936 1	0.935 8
3.9	—	—	—	—	0.934 1	0.933 2	0.931 6
4.0	—	—	—	—	—	0.917 8	0.908 8
4.1	—	—	—	—	—	—	0.880 6

Fig. 6 Correlation between area under different voltage intervals and battery capacity

Tab. 2 Correlation analysis of different features

滤波方法	F₁	F₂	F₃	F₄	F₅	F₆
未处理	0.674 3	0.673 2	-0.609 7	-0.597 4	0.665 8	0.683 7
MAD	0.805 1	0.829 8	-0.800 2	-0.787 2	0.781 7	0.735 6
SG	0.882 3	0.891 9	-0.864 8	-0.857 9	0.868 2	0.876 3
MAD-SG	0.937 5	0.936 8	-0.942 5	-0.941 5	0.946 6	0.927 2

Fig. 7 Diagram of temporal convolutional network principle

Fig. 8 Schematic diagram of SA-HBA optimization process

Fig. 9 SOH estimation results of different batteries

Tab. 3 SOH estimation error analysis of different batteries

电池型号	RMSE	MAE
B34	0.006 2	0.005 1
B35	0.006 4	0.005 2
B36	0.006 9	0.006 2
B38	0.005 6	0.004 8

Fig. 10 SOH estimation results and errors of different batteries under different operating conditions

Tab. 4 SOH estimation error analysis of different batteries under different operating conditions

电池型号	模型	RMSE	MAE
B05	SA-HBA-TCN	0.006 2	0.005 1
	TCN	0.013 1	0.010 1
	LSTM	0.031 4	0.026 5
B46	SA-HBA-TCN	0.006 4	0.005 3
	TCN	0.013 8	0.011 3
	LSTM	0.039 1	0.028 6
B29	SA-HBA-TCN	0.006 7	0.005 8
	TCN	0.012 9	0.010 9
	LSTM	0.042 2	0.035 9

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