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State of Charge Estimation using Recurrent Neural Networks with Long Short-Term Memory for Lithium-Ion Batteries

Titelangaben

Bockrath, S. ; Rosskopf, A. ; Koffel, S. ; Waldhör, Stefan ; Srivastava, K. ; Lorentz, Vincent:
State of Charge Estimation using Recurrent Neural Networks with Long Short-Term Memory for Lithium-Ion Batteries.
2019
Veranstaltung: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society , Oct. 14–17, 2019 , Lisbon, Portugal.
(Veranstaltungsbeitrag: Kongress/Konferenz/Symposium/Tagung , Vortrag mit Paper )
DOI: https://doi.org/10.1109/IECON.2019.8926815

Volltext

Link zum Volltext (externe URL): Volltext

Angaben zu Projekten

Projekttitel:
Offizieller Projekttitel
Projekt-ID
DEMOBASE - DEsign and MOdelling for improved BAttery Safety and Efficiency
769900
AI4DI - Artificial Intelligence for Digitizing Industry
826060

Projektfinanzierung: European Union's Horizon 2020 research and innovation programme
ECSEL Joint Undertaking

Abstract

This paper presents an accurate state of charge (SOC) estimation algorithm using a recurrent neural network with long short-term memory (LSTM) for lithium-ion batteries (LIB) performing under real conditions. With its self-learning ability, this data-driven approach is able to model the highly non-linear behavior of LIB due to changes of environment and working conditions all along the battery lifetime. It is shown that the LSTM approach outperforms common physical-based models using Extended Kalman Filters (EKF) regarding accuracy and stability. To demonstrate this benefit for real-world applications, the provided network is trained and tested with data gathered from commercial industry applications in the domain of energy storage. The LSTM is evaluated and compared with an equivalent circuit model (ECM) using EKF under different working conditions. For dynamic loading profiles, the ECM-EKF achieves an error (RMSE) of 9.5% whereas the LSTM achieves an error (RMSE) of 5.0%.

Weitere Angaben

Publikationsform: Veranstaltungsbeitrag (Vortrag mit Paper)
Begutachteter Beitrag: Ja
Keywords: State of charge; Batteries; Estimation; Voltage measurement; Computer architecture; Microprocessors; Temperature measurement
Institutionen der Universität: Forschungseinrichtungen > Zentrale wissenschaftliche Einrichtungen > Bayerisches Zentrum für Batterietechnik - BayBatt
Forschungseinrichtungen
Forschungseinrichtungen > Zentrale wissenschaftliche Einrichtungen
Titel an der UBT entstanden: Nein
Themengebiete aus DDC: 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften
Eingestellt am: 03 Jun 2022 10:15
Letzte Änderung: 05 Sep 2025 06:26
URI: https://eref.uni-bayreuth.de/id/eprint/69848