Guest Editorial Introduction to the Focused Section on Real-Time Monitoring, Diagnosis, and Prognosis and Health Management for Electric Vehicles

Zhiwei Gao; Victor Huang; Lifeng Wu; Mohammad Al Janaideh; Reinaldo Martinez Palhares

doi:10.1109/tmech.2023.3234361

Guest Editorial Introduction to the Focused Section on Real-Time Monitoring, Diagnosis, and Prognosis and Health Management for Electric Vehicles

Zhiwei Gao, Victor Huang, Lifeng Wu, Mohammad Al Janaideh, Reinaldo Martinez Palhares

Research output: Contribution to journal › Editorial › peer-review

6 Citations (Scopus)

Abstract

Nowadays, 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 [A1], [A2].

Original language	English
Pages (from-to)	607-610
Number of pages	4
Journal	IEEE/ASME Transactions on Mechatronics
Volume	28
Issue number	2
Early online date	23 Jan 2023
DOIs	https://doi.org/10.1109/tmech.2023.3234361
Publication status	Published - 18 Apr 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Prognostics and health management
Real-time systems
State of charge
Fault diagnosis
Temperature measurement
Lithium-ion batteries
Estimation

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10.1109/tmech.2023.3234361

Cite this

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title = "Guest Editorial Introduction to the Focused Section on Real-Time Monitoring, Diagnosis, and Prognosis and Health Management for Electric Vehicles",

abstract = "Nowadays, 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 [A1], [A2].",

keywords = "Prognostics and health management, Real-time systems, State of charge, Fault diagnosis, Temperature measurement, Lithium-ion batteries, Estimation",

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T1 - Guest Editorial Introduction to the Focused Section on Real-Time Monitoring, Diagnosis, and Prognosis and Health Management for Electric Vehicles

AU - Gao, Zhiwei

AU - Huang, Victor

AU - Wu, Lifeng

AU - Al Janaideh, Mohammad

AU - Palhares, Reinaldo Martinez

PY - 2023/4/18

Y1 - 2023/4/18

N2 - Nowadays, 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 [A1], [A2].

AB - Nowadays, 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 [A1], [A2].

KW - Prognostics and health management

KW - Real-time systems

KW - State of charge

KW - Fault diagnosis

KW - Temperature measurement

KW - Lithium-ion batteries

KW - Estimation

UR - https://www.scopus.com/pages/publications/85148418051

U2 - 10.1109/tmech.2023.3234361

DO - 10.1109/tmech.2023.3234361

M3 - Editorial

SN - 1083-4435

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SP - 607

EP - 610

JO - IEEE/ASME Transactions on Mechatronics

JF - IEEE/ASME Transactions on Mechatronics

IS - 2

ER -

Guest Editorial Introduction to the Focused Section on Real-Time Monitoring, Diagnosis, and Prognosis and Health Management for Electric Vehicles

Abstract

UN SDGs

Keywords

Access to Document

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