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

    Source: Journal of Electrochemical Energy Conversion and Storage:;2022:;volume( 020 ):;issue: 003::page 31010-1
    Author:
    Chen, Jianlong
    ,
    Zhang, Chenghao
    ,
    Chen, Cong
    ,
    Lu, Chenlei
    ,
    Xuan, Dongji
    DOI: 10.1115/1.4055985
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: 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.
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      State-of-Charge Estimation of Lithium-Ion Batteries Using Convolutional Neural Network With Self-Attention Mechanism

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4294530
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    • Journal of Electrochemical Energy Conversion and Storage

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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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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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