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contributor authorChen, Jianlong
contributor authorZhang, Chenghao
contributor authorChen, Cong
contributor authorLu, Chenlei
contributor authorXuan, Dongji
date accessioned2023-11-29T19:02:05Z
date available2023-11-29T19:02:05Z
date copyright11/11/2022 12:00:00 AM
date issued11/11/2022 12:00:00 AM
date issued2022-11-11
identifier issn2381-6872
identifier otherjeecs_20_3_031010.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4294530
description abstractState 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.
publisherThe American Society of Mechanical Engineers (ASME)
titleState-of-Charge Estimation of Lithium-Ion Batteries Using Convolutional Neural Network With Self-Attention Mechanism
typeJournal Paper
journal volume20
journal issue3
journal titleJournal of Electrochemical Energy Conversion and Storage
identifier doi10.1115/1.4055985
journal fristpage31010-1
journal lastpage31010-9
page9
treeJournal of Electrochemical Energy Conversion and Storage:;2022:;volume( 020 ):;issue: 003
contenttypeFulltext


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