The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/e221fc2f-6c79-4933-b7a7-2d6b28c21391

van Oosterom, Simon (2025): Code underlying the research of end-to-end predictive maintenance planning for eVTOL batteries. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/e221fc2f-6c79-4933-b7a7-2d6b28c21391.v1

Categories

• Aerospace Transport
• Applied Mathematics
• Environmentally Sustainable Transport
• Transport
• Mathematical Sciences

Keywords

eVTOL predict-then-optimize predictive maintenance

Licence

CC BY-NC 4.0

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by Simon van Oosterom

This repository contains the code developed for predictive maintenance planning simulations for a fleet of eVTOL (electric vertical take-off and landing) aircraft at Delft University of Technology, as part of Simon van Oosterom's PhD Thesis project (2025).

It is being made public both to act as supplementary data for publications and the PhD thesis of Simon van Oosterom, and in order for other researchers to use this repository in their own work.

History

• 2025-02-12 first online, published, posted

Publisher

4TU.ResearchData

Format

.py

Code hosting project url

https://gitlab.tudelft.nl/sjmvanoosterom/paper_4_evtol_batteries

Data link

https://kilthub.cmu.edu/articles/dataset/eVTOL_Battery_Dataset/14226830

Organizations

TU Delft, Faculty of Aerospace Engineering, Department of Control and Operations