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    State of Health Estimation for Lithium-Ion Batteries Using Multiple Indirect Feature Extraction and Bayesian Optimization–Transformer Model

    Source: Journal of Energy Engineering:;2026:;Volume ( 152 ):;issue: 002::page 04025118-1
    Author:
    Li, Cong
    ,
    Pan, Rui
    ,
    Si, Ruibin
    ,
    Liu, Quanfeng
    ,
    Zhou, Juan
    DOI: 10.1061/JLEED9.EYENG-6176
    Publisher: American Society of Civil Engineers
    Abstract: AbstractAccurate estimation of the state of health (SOH) of lithium-ion batteries is a critical prerequisite for ensuring their safe and stable operation. However, the complex operating environments in practical applications often result in raw data ...
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      State of Health Estimation for Lithium-Ion Batteries Using Multiple Indirect Feature Extraction and Bayesian Optimization–Transformer Model

    URI
    https://yetl.yabesh.ir/yetl1/handle/yetl/4311997
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    • Journal of Energy Engineering

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    contributor authorLi, Cong
    contributor authorPan, Rui
    contributor authorSi, Ruibin
    contributor authorLiu, Quanfeng
    contributor authorZhou, Juan
    date accessioned2026-08-20T11:18:09Z
    date available2026-08-20T11:18:09Z
    date copyright2025/12/26
    date issued2026
    identifier otherJLEED9.EYENG-6176.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4311997
    description abstractAbstractAccurate estimation of the state of health (SOH) of lithium-ion batteries is a critical prerequisite for ensuring their safe and stable operation. However, the complex operating environments in practical applications often result in raw data ...
    publisherAmerican Society of Civil Engineers
    titleState of Health Estimation for Lithium-Ion Batteries Using Multiple Indirect Feature Extraction and Bayesian Optimization–Transformer Model
    typeJournal Article
    journal volume152
    journal issue2
    journal titleJournal of Energy Engineering
    identifier doi10.1061/JLEED9.EYENG-6176
    journal fristpage04025118-1
    journal lastpage04025118-11
    page11
    treeJournal of Energy Engineering:;2026:;Volume ( 152 ):;issue: 002
    contenttypeFulltext
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