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    State of Charge Estimation for Electric Bus Batteries Based on Deep Learning Model with Parameters Fine-Tuning Method

    Source: Journal of Transportation Engineering, Part A: Systems:;2025:;Volume ( 151 ):;issue: 010::page 04025077-1
    Author:
    Zhao, Dengfeng
    ,
    Li, Haiyang
    ,
    Fu, Zhijun
    ,
    Liu, Zhaohui
    ,
    Zhou, Fang
    ,
    Zhong, Yudong
    ,
    He, Wenbin
    DOI: 10.1061/JTEPBS.TEENG-9086
    Publisher: American Society of Civil Engineers
    Abstract: AbstractAccurately estimating the state of charge (SOC) of electric bus batteries can effectively improve driving safety and mileage. Due to the strong nonlinearity and time-varying characteristics of batteries, it is difficult to estimate SOC through a ...
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      State of Charge Estimation for Electric Bus Batteries Based on Deep Learning Model with Parameters Fine-Tuning Method

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    https://yetl.yabesh.ir/yetl1/handle/yetl/4313655
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    • Journal of Transportation Engineering, Part A: Systems

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    contributor authorZhao, Dengfeng
    contributor authorLi, Haiyang
    contributor authorFu, Zhijun
    contributor authorLiu, Zhaohui
    contributor authorZhou, Fang
    contributor authorZhong, Yudong
    contributor authorHe, Wenbin
    date accessioned2026-08-20T20:54:35Z
    date available2026-08-20T20:54:35Z
    date copyright2025/07/30
    date issued2025
    identifier otherJTEPBS.TEENG-9086.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4313655
    description abstractAbstractAccurately estimating the state of charge (SOC) of electric bus batteries can effectively improve driving safety and mileage. Due to the strong nonlinearity and time-varying characteristics of batteries, it is difficult to estimate SOC through a ...
    publisherAmerican Society of Civil Engineers
    titleState of Charge Estimation for Electric Bus Batteries Based on Deep Learning Model with Parameters Fine-Tuning Method
    typeJournal Article
    journal volume151
    journal issue10
    journal titleJournal of Transportation Engineering, Part A: Systems
    identifier doi10.1061/JTEPBS.TEENG-9086
    journal fristpage04025077-1
    journal lastpage04025077-12
    page12
    treeJournal of Transportation Engineering, Part A: Systems:;2025:;Volume ( 151 ):;issue: 010
    contenttypeFulltext
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