Editorial guest editorial introduction to the focused section on real-time monitoring, diagnosis, and prognosis and health management for electric vehicles

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dc.creatorZhiwei Gao
dc.creatorVictor Huang
dc.creatorLifeng Wu
dc.creatorMohammad Al Janaideh
dc.creatorReinaldo Martinez Palhares
dc.date.accessioned2025-05-28T14:57:27Z
dc.date.accessioned2025-09-08T23:16:44Z
dc.date.available2025-05-28T14:57:27Z
dc.date.issued2023
dc.identifier.doi10.1109/TMECH.2023.3234361
dc.identifier.issn10834435
dc.identifier.urihttps://hdl.handle.net/1843/82558
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofIEEE/ASME Transactions on Mechatronics
dc.rightsAcesso Restrito
dc.subjectVeículos Elétricos
dc.subject.otherPrognóstico baseado em Dados
dc.subject.otherSpecial issues and sections , Lithium-ion batteries , Temperature measurement , Electric vehicles , State of charge , Real-time systems , Prognostics and health management , Estimation , Safety , Reliability
dc.subject.otherDiagnóstico de Falhas
dc.subject.otherHealth Management , Electric Vehicles , Real-time Management , Real-time Diagnosis , Health Status , Actuator , Power System , State Of Charge , Expectation Maximization , Fault-tolerant , Power Network , Operational Performance , Monitoring Techniques , Fault Diagnosis , Safety-critical , Extended Kalman Filter , Internal Defects , Fault-tolerant Control , Electric Vehicles Battery , Battery Degradation , Battery Temperature
dc.subject.otherIndústria 4.0
dc.subject.otherInteligência Artificial
dc.titleEditorial guest editorial introduction to the focused section on real-time monitoring, diagnosis, and prognosis and health management for electric vehicles
dc.typeArtigo de periódico
local.citation.epage610
local.citation.issue2
local.citation.spage607
local.citation.volume28
local.description.resumoNowadays, industrial automation systems are becoming more complex and expensive, and having less tolerance for performance degradation, productivity decrease, and reliability and safety threats, which greatly necessitates to detect and identify any kinds of abnormalities as early as possible, predict the remaining useful life of a component or system, implement real-time resilient operation and health management to minimize performance degradation, improve reliability and safety, and reduce operation and maintenance costs during the life cycles in automation systems.
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA
local.publisher.initialsUFMG
local.url.externahttps://ieeexplore.ieee.org/document/10026628

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