Print Email Facebook Twitter Predicting state of health and remaining useful lifetime of lithium-ion batteries for eVTOLs using data-driven machine learning Title Predicting state of health and remaining useful lifetime of lithium-ion batteries for eVTOLs using data-driven machine learning Author Hennink, Birgitte (TU Delft Aerospace Engineering) Contributor Pavel, M.D. (mentor) Mitici, M.A. (mentor) Dong, J. (graduation committee) Degree granting institution Delft University of Technology Programme Aerospace Engineering | Air Transport and Operations Date 2022-06-10 Subject Lithium-ion batteryState of healthRemaining useful lifetimeeVTOLMachine learning To reference this document use: http://resolver.tudelft.nl/uuid:4216b379-b6da-42a9-9369-b228ac1f0a2c Embargo date 2024-06-10 Part of collection Student theses Document type master thesis Rights © 2022 Birgitte Hennink Files file embargo until 2024-06-10