Artificial Feature Extraction for Estimating State-of-Temperature in Lithium-Ion-Cells Using Various Long Short-Term Memory Architectures

Artificial Feature Extraction for Estimating State-of-Temperature in Lithium-Ion-Cells Using Various Long Short-Term Memory Architectures

The temperature in each cell of a battery system should be monitored to correctly track aging behavior and ensure safety requirements. To eliminate the need for additional hardware components, a software based prediction model is needed to track the temperature behavior. This study looks at machine...

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Bibliographic Details
Main Authors: Birke, K.P (Author), Fill, A. (Author), Kopp, M. (Author), Pross-Brakhage, J. (Author), Ströbel, M. (Author)
Format: Article
Language:English
Published: MDPI 2022
Subjects:
artificial feature extraction
battery management system
lithium-ion-battery
machine learning
state estimation
thermal management
Online Access:View Fulltext in Publisher

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