| Issue |
MATEC Web Conf.
Volume 401, 2024
21st International Conference on Manufacturing Research (ICMR2024)
|
|
|---|---|---|
| Article Number | 10010 | |
| Number of page(s) | 8 | |
| Section | Manufacturing / Engineering Management | |
| DOI | https://doi.org/10.1051/matecconf/202440110010 | |
| Published online | 27 August 2024 | |
Utilising Digital Twins for Smart Maintenance Planning of Fuel Cell in Electric Vehicles
College of Engineering and Physical Sciences, Aston University, Birmingham, B4 7ET, UK
* Corresponding authors: n.amaitik@aston.ac.uk and y.xu16@aston.ac.uk
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.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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