Predicting the remaining service life of lithium batteries based on the SDAE-transformer-ECA network

doi:10.19799/j.cnki.2095-4239.2023.0369

Abstract

Abstract:

The accurate prediction of the remaining useful life (RUL) of lithium-ion batteries plays a crucial role in improving the battery life and reducing the probability of accidents. This study combines the advantages of a stacked denoising autoencoder (SDAE) and a transformer to propose a lithium-ion battery RUL prediction network that combines the SDAE, transformer, and efficient channel attention (ECA). Considering the noise pollution brought about by the capacity regeneration phenomenon and the dataset acquisition error during battery usage, the SDAE is used to reconstruct and denoise the input data and extract the features. The sequence information of the reconstructed data is then captured through the transformer network. Finally, the cross-channel integration and interaction of the captured information are performed in combination with the ECA network to realize the RUL prediction of the lithium-ion batteries. This study uses the battery capacity dataset provided by the Center for Advanced Life Cycle Engineering at the University of Maryland. The experimental results show that the proposed algorithm has low error and high accuracy. Compared with that for the suboptimal bi-directional long short-term memory (Bi-LSTM) algorithm, the average RE, mean absolute error (MAE), and root-mean-squared error (RMSE) for the proposed algorithm are relatively reduced by 62.67%, 40.68%, and 34.33%, respectively. Using the B0007 battery capacity dataset provided by the National Aeronautics and Space Administration for generalization verification, the experimental results of the RE, MAE, and RMSE were found to be 1.98%, 3.12%, and 4.16%, respectively. With that being said, the prediction accuracy of the proposed algorithm is higher than that of existing algorithms, such as recurrent neural networks, LSTM, gated recurrent units, and Bi-LSTM. Thus, the generalization of the model is proven.

Key words: lithium-ion battery, SDAE, transformer, attention mechanism, remaining useful life prediction

CLC Number:

TM 912

Xinghai SONG, Xiaoqian ZHANG, Huishi LIANG, Zinan SHI, Miangang LI, Kui ZHOU, Xiaoxu GONG. Predicting the remaining service life of lithium batteries based on the SDAE-transformer-ECA network[J]. Energy Storage Science and Technology, 2023, 12(10): 3181-3190.

Figures/Tables 9

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参数名	参数设置
采样窗口s	64
模型学习速率lr	0.005
Transformer网络深度d	1
Transformer网络的隐藏层大小h	32
学习正则化α	10^-6
优化函数	Adam
损失函数loss	MSE

算法	RE/%	MAE/%	RMSE/%
RNN	6.51	9.10	11.39
LSTM	3.94	3.77	4.78
GRU	5.54	4.28	5.11
Bi-LSTM	3.08	4.13	5.01
Transformer	79.92	60.88	70.80
SDAE-Transformer	6.33	3.13	4.27
SDAE-Transformer-ECA	1.15	2.45	3.29

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