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Title:

Modeling Lithium-Ion Batteries Using Machine Learning Algorithms for Mild-Hybrid Vehicle Applications

Document type:
Konferenzbeitrag
Contribution type:
Textbeitrag / Aufsatz
Author(s):
Daniel Jerouschek, Ömer Tan, Ralph Kennel, Ahmet Taskiran
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...     »
Book / Congress title:
Proceedings of SEST2021 - the 4th International Conference on Smart Energy Systems and Technologies (SEST)
Organization:
University of Vaasa 2021
Date of congress:
6-8 September 2021
Publisher:
IEEE
Date of publication:
27.09.2021
Year:
2021
Quarter:
3. Quartal
Year / month:
2021-09
Month:
Sep
Print-ISBN:
978-1-7281-7661-1
E-ISBN:
978-1-7281-7660-4
Reviewed:
ja
Language:
en
Fulltext / DOI:
doi:10.1109/SEST50973.2021.9543225
Semester:
SS 21
TUM Institution:
Lehrstuhl für Elektrische Antriebssysteme und Leistungselektronik
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