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    Data-Driven Online Prediction of Discharge Capacity and End-of-Discharge of Lithium-Ion Batteries

    Source: Journal of Computing and Information Science in Engineering:;2024:;volume( 024 ):;issue: 009::page 90901-1
    Author:
    Shi, Junchuan
    ,
    Wei, Yupeng
    ,
    Wu, Dazhong
    DOI: 10.1115/1.4063985
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Monitoring the health condition as well as predicting the performance of lithium-ion batteries is crucial to the reliability and safety of electrical systems such as electric vehicles. However, estimating the discharge capacity and end-of-discharge (EOD) of a battery in real-time remains a challenge. Few works have been reported on the relationship between the capacity degradation of a battery and EOD. We introduce a new data-driven method that combines convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) models to predict the discharge capacity and the EOD using online condition monitoring data. The CNN model extracts long-term correlations among voltage, current, and temperature measurements and then estimates the discharge capacity. The BiLSTM model extracts short-term dependencies in condition monitoring data and predicts the EOD for each discharge cycle while utilizing the capacity predicted by the CNN as an additional input. By considering the discharge capacity, the BiLSTM model is able to use the long-term health condition of a battery to improve the prediction accuracy of its short-term performance. We demonstrated that the proposed method can achieve online discharge capacity estimation and EOD prediction efficiently and accurately.
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      Data-Driven Online Prediction of Discharge Capacity and End-of-Discharge of Lithium-Ion Batteries

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4303224
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    contributor authorShi, Junchuan
    contributor authorWei, Yupeng
    contributor authorWu, Dazhong
    date accessioned2024-12-24T19:03:52Z
    date available2024-12-24T19:03:52Z
    date copyright6/7/2024 12:00:00 AM
    date issued2024
    identifier issn1530-9827
    identifier otherjcise_24_9_090901.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4303224
    description abstractMonitoring the health condition as well as predicting the performance of lithium-ion batteries is crucial to the reliability and safety of electrical systems such as electric vehicles. However, estimating the discharge capacity and end-of-discharge (EOD) of a battery in real-time remains a challenge. Few works have been reported on the relationship between the capacity degradation of a battery and EOD. We introduce a new data-driven method that combines convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) models to predict the discharge capacity and the EOD using online condition monitoring data. The CNN model extracts long-term correlations among voltage, current, and temperature measurements and then estimates the discharge capacity. The BiLSTM model extracts short-term dependencies in condition monitoring data and predicts the EOD for each discharge cycle while utilizing the capacity predicted by the CNN as an additional input. By considering the discharge capacity, the BiLSTM model is able to use the long-term health condition of a battery to improve the prediction accuracy of its short-term performance. We demonstrated that the proposed method can achieve online discharge capacity estimation and EOD prediction efficiently and accurately.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleData-Driven Online Prediction of Discharge Capacity and End-of-Discharge of Lithium-Ion Batteries
    typeJournal Paper
    journal volume24
    journal issue9
    journal titleJournal of Computing and Information Science in Engineering
    identifier doi10.1115/1.4063985
    journal fristpage90901-1
    journal lastpage90901-10
    page10
    treeJournal of Computing and Information Science in Engineering:;2024:;volume( 024 ):;issue: 009
    contenttypeFulltext
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