Utilising Digital Twins for Smart Maintenance Planning of Fuel Cell in Electric Vehicles

Nasser Amaitik; Ming Zhang; Yuchun Xu; Tony Clark; Lucy Bastin

doi:10.1051/matecconf/202440110010

Utilising Digital Twins for Smart Maintenance Planning of Fuel Cell in Electric Vehicles

Nasser Amaitik, Ming Zhang, Yuchun Xu, Tony Clark, Lucy Bastin

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

This paper presents a framework utilising digital twins for predictive maintenance planning of fuel cells in electric vehicles, focusing on real-time condition monitoring and Remaining Useful Lifetime (RUL) prediction. By integrating advanced algorithms, it optimises maintenance schedules to reduce downtime and extend fuel cell lifetime. Despite relying on simulated data, the findings highlight the potential of digital twins to improve fuel cell reliability, and sustainability, illustrating their transformative impact on smart urban transportation systems.

Original language	English
Title of host publication	MATEC Web of Conferences
Publisher	EDP Sciences
Number of pages	8
Volume	401
DOIs	https://doi.org/10.1051/matecconf/202440110010
Publication status	Published - 27 Aug 2024
Event	21st International Conference on Manufacturing Research - University of Strathclyde, Glasgow, United Kingdom Duration: 28 Aug 2024 → 30 Aug 2024

Conference

Conference	21st International Conference on Manufacturing Research
Abbreviated title	ICMR 2024
Country/Territory	United Kingdom
City	Glasgow
Period	28/08/24 → 30/08/24

Bibliographical note

Copyright © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 e 4.0 (https://creativecommons.org/licenses/by/4.0/).

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10.1051/matecconf/202440110010

N Amaitik et al_matecconf_icmr2024_10010_Digital twins for smart maintenance planning_fuel cell in EVs
Copyright © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/)
Final published version, 1.94 MBLicence: CC BY 4.0

Cite this

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title = "Utilising Digital Twins for Smart Maintenance Planning of Fuel Cell in Electric Vehicles",

abstract = "This paper presents a framework utilising digital twins for predictive maintenance planning of fuel cells in electric vehicles, focusing on real-time condition monitoring and Remaining Useful Lifetime (RUL) prediction. By integrating advanced algorithms, it optimises maintenance schedules to reduce downtime and extend fuel cell lifetime. Despite relying on simulated data, the findings highlight the potential of digital twins to improve fuel cell reliability, and sustainability, illustrating their transformative impact on smart urban transportation systems.",

author = "Nasser Amaitik and Ming Zhang and Yuchun Xu and Tony Clark and Lucy Bastin",

note = "Copyright {\textcopyright} The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 e 4.0 (https://creativecommons.org/licenses/by/4.0/).; 21st International Conference on Manufacturing Research, ICMR 2024 ; Conference date: 28-08-2024 Through 30-08-2024",

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AU - Amaitik, Nasser

AU - Zhang, Ming

AU - Xu, Yuchun

AU - Clark, Tony

AU - Bastin, Lucy

N1 - Copyright © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 e 4.0 (https://creativecommons.org/licenses/by/4.0/).

PY - 2024/8/27

Y1 - 2024/8/27

N2 - This paper presents a framework utilising digital twins for predictive maintenance planning of fuel cells in electric vehicles, focusing on real-time condition monitoring and Remaining Useful Lifetime (RUL) prediction. By integrating advanced algorithms, it optimises maintenance schedules to reduce downtime and extend fuel cell lifetime. Despite relying on simulated data, the findings highlight the potential of digital twins to improve fuel cell reliability, and sustainability, illustrating their transformative impact on smart urban transportation systems.

AB - This paper presents a framework utilising digital twins for predictive maintenance planning of fuel cells in electric vehicles, focusing on real-time condition monitoring and Remaining Useful Lifetime (RUL) prediction. By integrating advanced algorithms, it optimises maintenance schedules to reduce downtime and extend fuel cell lifetime. Despite relying on simulated data, the findings highlight the potential of digital twins to improve fuel cell reliability, and sustainability, illustrating their transformative impact on smart urban transportation systems.

UR - https://www.matec-conferences.org/articles/matecconf/abs/2024/13/matecconf_icmr2024_10010/matecconf_icmr2024_10010.html

U2 - 10.1051/matecconf/202440110010

DO - 10.1051/matecconf/202440110010

M3 - Conference publication

VL - 401

BT - MATEC Web of Conferences

PB - EDP Sciences

T2 - 21st International Conference on Manufacturing Research

Y2 - 28 August 2024 through 30 August 2024

ER -

Utilising Digital Twins for Smart Maintenance Planning of Fuel Cell in Electric Vehicles

Abstract

Conference

Bibliographical note

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