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

dc.contributor.authorZhao, Dengfeng
dc.contributor.authorLi, Haiyang
dc.contributor.authorFu, Zhijun
dc.contributor.authorLiu, Zhaohui
dc.contributor.authorZhou, Fang
dc.contributor.authorZhong, Yudong
dc.contributor.authorHe, Wenbin
dc.date.accessioned2026-08-20T20:54:35Z
dc.date.available2026-08-20T20:54:35Z
dc.date.copyright2025/07/30
dc.date.issued2025
dc.identifier.otherJTEPBS.TEENG-9086.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4313655
dc.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 ...
dc.publisherAmerican Society of Civil Engineers
dc.titleState of Charge Estimation for Electric Bus Batteries Based on Deep Learning Model with Parameters Fine-Tuning Method
dc.typeJournal Article
dc.journal.volume151
dc.journal.issue10
dc.journal.titleJournal of Transportation Engineering, Part A: Systems
dc.identifier.doi10.1061/JTEPBS.TEENG-9086
dc.journal.fristpage04025077-1
dc.journal.lastpage04025077-12
dc.page12
dc.treeJournal of Transportation Engineering, Part A: Systems:;2025:;Volume ( 151 ):;issue: 010
dc.contenttypeFulltext


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