Benutzer: Gast  Login
Titel:

Data Preparation and Training Methodology for Modeling Lithium-Ion Batteries Using a Long Short-Term Memory Neural Network for Mild-Hybrid Vehicle Applications

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Daniel Jerouschek, Ömer Tan, Ralph Kennel, Ahmet Taskiran
Abstract:
Voltage models of lithium-ion batteries (LIB) are used to estimate their future voltages, based on the assumption of a specific current profile, in order to ensure that the LIB remains in a safe operation mode. Data of measurable physical features—current, voltage and temperature—are processed using both over- and undersampling methods, in order to obtain evenly distributed and, therefore, appropriate data to train the model. The trained recurrent neural network (RNN) consists of two long s...     »
Stichworte:
lithium-ion battery (LIB); long short-term memories (LSTM); machine learning (ML); modeling; recurrent neural net (RNN)
Zeitschriftentitel:
Applied Sciences
Jahr:
2020
Jahr / Monat:
2020-11
Quartal:
4. Quartal
Monat:
Nov
Heft / Issue:
10, 7880
Reviewed:
ja
Sprache:
en
Volltext / DOI:
doi:10.3390/app10217880
Verlag / Institution:
MDPI
Semester:
WS 20-21
TUM Einrichtung:
Lehrstuhl für Elektrische Antriebssysteme und Leistungselektronik
 BibTeX