State-of-Charge Estimation of Lithium-Ion Batteries Using Convolutional Neural Network With Self-Attention Mechanism

Autor:	Jianlong Chen, Chenghao Zhang, Cong Chen, Chenlei Lu, Dongji Xuan
Rok vydání:	2022
Předmět:	Mechanics of Materials Renewable Energy Sustainability and the Environment Mechanical Engineering Energy Engineering and Power Technology Electronic Optical and Magnetic Materials
Zdroj:	Journal of Electrochemical Energy Conversion and Storage. 20
ISSN:	2381-6910 2381-6872
Popis:	State of charge (SOC) of lithium-ion batteries is an indispensable performance indicator in a battery management system (BMS), which is essential to ensure the safe operation of the battery and avoid potential hazards. However, SOC cannot be directly measured by sensors or tools. In order to accurately estimate the SOC, this paper proposes a convolutional neural network based on self-attention mechanism. First, the one-dimensional convolution is introduced to extract features from battery voltage, current, and temperature data. Then, the self-attention mechanism can reduce the dependence on external information and well capture the internal correlation of features extracted by the convolutional layer. Finally, the proposed method is validated on four dynamic driving conditions at five temperatures and compared with the other two deep learning methods. The experimental results show that the proposed method has good accuracy and robustness.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::9cbce32a3b47d42c54d08b1919119cfa https://doi.org/10.1115/1.4055985 Zobrazit plný text záznamu