Estimating lithium-ion battery health using automatic feature extraction and channel attention mechanisms for multi-timescale modeling

doi:10.19799/j.cnki.2095-4239.2024.0627

Abstract

Abstract:

Accurate estimation of the state of health (SOH) in lithium-ion batteries (LIB) is crucial for the safe and stable operation of energy storage systems. Current data-driven approaches often rely on manual feature extraction or fall short in single-scale feature representation. To address these issues, this paper introduces a novel SOH estimation model that leverages automatic feature extraction and channel attention mechanisms for multi-timescale modeling. The approach begins with inputting charging process data into multiple parallel dilation convolution modules (DCM), which automatically extract features across various time scales, creating a rich and comprehensive feature representation. These multi-scale features are then integrated and processed by a gated recurrent unit (GRU) to capture long-term dependencies in the time series data. Furthermore, the model incorporates the efficient channel attention (ECA) mechanism, which dynamically adjusts the importance of historical information and emphasizes critical features. The proposed method's effectiveness is validated through experiments two public datasets, showcasing a significant improvement over common deep learning models. Results demonstrate that the model proposed in this study exhibits high precision in SOH estimation and robust transferability. The model achieves low Root Mean Square Errors (RMSE) of 0.0110 and 0.0095 on these datasets, respectively, and maintains an RMSE of only 0.0092 in cross-dataset transfer experiments. These findings underscore the efficacy and adaptability of the proposed model in executing SOH predictions across different datasets.

Key words: lithium-ion battery, state of health, convolutional neural network, attention mechanism, time series

CLC Number:

TM 912

Xue KE, Huawei HONG, Peng ZHENG, Zhicheng LI, Peixiao FAN, Jun YANG, Yuzheng GUO, Chunguang KUAI. Estimating lithium-ion battery health using automatic feature extraction and channel attention mechanisms for multi-timescale modeling[J]. Energy Storage Science and Technology, 2024, 13(9): 3059-3071.

Figures/Tables 12

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方法	B05		B06		B07		B18		平均值
方法	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE
CNN	0.0314	0.0326	0.0266	0.0326	0.0169	0.0184	0.0171	0.0195	0.0230	0.0258
GRU	0.0183	0.0193	0.0284	0.0311	0.0116	0.0129	0.0187	0.0217	0.0192	0.0212
文献[31]	0.0080	0.0092	0.0111	0.0209	0.0105	0.0118	0.0094	0.0104	0.0097	0.0131
文献[30]	0.0109	—	0.0117	—	0.0154	—	0.0169	—	0.0137	—
DGE	0.0141	0.0152	0.0201	0.0235	0.0091	0.0107	0.0116	0.0144	0.0137	0.0160
MDGE	0.0058	0.0082	0.0134	0.0173	0.0063	0.0073	0.0096	0.0113	0.0088	0.0110

方法	CS2_35		CS2_36		CS2_37		CS2_38		平均值
方法	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE
CNN	0.0219	0.0229	0.0328	0.0346	0.0257	0.0261	0.0272	0.0302	0.0269	0.0284
GRU	0.0080	0.0110	0.0164	0.0181	0.0135	0.0152	0.0186	0.0221	0.0141	0.0166
文献[31]	0.0118	0.0184	0.0142	0.0229	0.0104	0.0117	0.0107	0.0162	0.0118	0.0173
文献[32]	0.0135	0.0146	0.0237	0.0263	0.0164	0.0182	0.0746	0.0900	0.0320	0.0373
DGE	0.0074	0.0097	0.0116	0.0139	0.0084	0.0108	0.0116	0.0152	0.0097	0.0124
MDGE	0.0055	0.0077	0.0067	0.0096	0.0062	0.0079	0.0092	0.0131	0.0069	0.0095
T1-MDGE	0.0093	0.0106	0.0095	0.0112	0.0091	0.0102	0.0109	0.0145	0.0097	0.0116
T2-MDGE	0.0050	0.0069	0.0068	0.0099	0.0055	0.0073	0.0097	0.0126	0.0068	0.0092

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