期刊
JOURNAL OF POWER SOURCES
卷 556, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.jpowsour.2022.232466
关键词
Lithium -ion battery; State of health estimation; Automatic feature extraction; Convolutional autoencoder; Self-attention mechanism
This paper proposes an automatic feature extraction method combining convolutional autoencoder and self-attention mechanism for battery state of health (SOH) estimation. The method replaces manual feature engineering with automatic feature extraction using a convolutional autoencoder and achieves high accuracy on both the dataset used in the study and the NASA public dataset. Experimental results and comparisons with existing methods are provided.
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 ap-proaches 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 engi-neering 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.
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