State of health estimation of lithium-ion battery with automatic feature extraction and self-attention learning mechanism

Accurate state of health (SOH) estimation is significantly important to ensure the safe and reliable operation of lithium-ion battery. Most existing data-driven estimation methods are based on feature engineering and rely heavily on expert experience and manual operation. However, manually extractin...

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Bibliographic Details
Published in:	Journal of power sources Vol. 556; p. 232466
Main Authors:	Jiang, Yiyue, Chen, Yuan, Yang, Fangfang, Peng, Weiwen
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.02.2023
Subjects:	Automatic feature extraction Convolutional autoencoder Lithium-ion battery Self-attention mechanism State of health estimation State of health estimation Lithium-ion battery Self-attention mechanism Automatic feature extraction Convolutional autoencoder
ISSN:	0378-7753
Online Access:	Get full text
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Summary:	Accurate state of health (SOH) estimation is significantly important to ensure the safe and reliable operation of lithium-ion battery. Most existing data-driven estimation methods are based on feature engineering and rely heavily on expert experience and manual operation. However, manually extracting qualified health features requires rich prior knowledge, and these highly-designed features for one specific application may not generalize well to other situations. In this work, an automatic feature extraction method combining convolutional autoencoder and self-attention mechanism is proposed for battery SOH estimation. With preprocessed data fed into the convolutional autoencoder, efficient features characterizing battery health are automatically extracted without human intervention. A self-mechanism module is then further employed to map these high-dimensional abstract health features into battery SOH. Finally, experimental study of battery aging is implemented to demonstrate the proposed method, and comparisons of the proposed method with existing data-driven approaches and the manual feature-based methods have also been presented. With the help of the convolutional autoencoder and self-attention module, the proposed method replaces the conventional manual feature engineering with automatic feature extraction, and reaches 0.0048 average test root-mean-squared error (RMSE) and 0.46% mean-absolute-percentage error (MAPE) on our dataset and 3.69% on the NASA public dataset. •An automatic health feature extraction method for LIBs without prior knowledge is proposed.•Convolutional autoencoder model is used to extract features automatically.•Self-attention mechanism is incorporated to obtain accurate SOH estimation results.•The performance is compared with the manual feature-based methods and other data-driven methods.
ISSN:	0378-7753
DOI:	10.1016/j.jpowsour.2022.232466