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
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 |
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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 |