Optical monitoring of second-life batteries enhanced by machine learning

Publikationstyp
Conference Paper
Date Issued
2022-05
Sprache
English
Author(s)
Kruse, Lars  
Schill, Jendrik
Landsiedel, Olaf  
Pachnicke, Stephan  
TORE-URI
https://hdl.handle.net/11420/53867
Citation
13th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2022
Contribution to Conference
13th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2022  
Publisher DOI
10.1109/PEDG54999.2022.9923150
Scopus ID
2-s2.0-85142023122
Publisher
IEEE
ISBN of container
978-1-6654-6618-9
978-1-6654-6619-6
We propose the usage of optical time domain reflectometry as a monitoring technique for 2nd-life battery pools. The approach promises significant cost reduction for the monitoring system compared to a distributed electrical sensing system. Furthermore, due to coherent reception a high sensitivity can be reached, which can be further improved by combining with machine learning algorithms for detection and classification.
Subjects
Battery monitoring
machine learning
OTDR
MLE@TUHH
DDC Class
600: Technology