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Dokumenttyp:
Konferenzbeitrag 
Art des Konferenzbeitrags:
Textbeitrag / Aufsatz 
Autor(en):
Daniel Jerouschek, Ömer Tan, Ralph Kennel, Ahmet Taskiran 
Titel:
Modeling Lithium-Ion Batteries Using Machine Learning Algorithms for Mild-Hybrid Vehicle Applications 
Abstract:
The prediction of voltage levels in an automotive 48V mild hybrid power supply system is safety-relevant while also enabling greater efficiency. The high power-to-energy ratio in these power supply systems makes exact voltage prediction challenging, so that a method is established to model the behavior of the lithium-ion batteries by means of a recurrent neural network. The raw data are consequently pre-processed with over- and undersampling, normalization and sequentialization algorithms. The r...    »
 
Kongress- / Buchtitel:
Proceedings of SEST2021 - the 4th International Conference on Smart Energy Systems and Technologies (SEST) 
Ausrichter der Konferenz:
University of Vaasa 2021 
Datum der Konferenz:
6-8 September 2021 
Verlag / Institution:
IEEE 
Publikationsdatum:
27.09.2021 
Jahr:
2021 
Quartal:
3. Quartal 
Jahr / Monat:
2021-09 
Monat:
Sep 
Print-ISBN:
978-1-7281-7661-1 
E-ISBN:
978-1-7281-7660-4 
Reviewed:
ja 
Sprache:
en 
Semester:
SS 21 
TUM Einrichtung:
Lehrstuhl für Elektrische Antriebssysteme und Leistungselektronik