Predicting state of health and remaining useful lifetime of lithium-ion batteries for eVTOLs using data-driven machine learning

Master Thesis (2022)
Author(s)

B.L. Hennink (TU Delft - Aerospace Engineering)

Contributor(s)

M.D. Pavel – Mentor (Control & Simulation)

M.A. Mitici – Mentor (TU Delft - Aerospace Engineering)

J. Dong – Graduation committee member (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Faculty
Aerospace Engineering
More Info
expand_more
Publication Year
2022
Language
English
Graduation Date
10-06-2022
Awarding Institution
Delft University of Technology
Programme
Aerospace Engineering, Air Transport and Operations
Faculty
Aerospace Engineering
Downloads counter
217
Collections
thesis
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Files

ATO_MSc_Thesis_printing.pdf
(pdf | 5.96 Mb)
- Embargo expired in 10-06-2024
License info not available