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

Title data

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
Event: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society , Oct. 14–17, 2019 , Lisbon, Portugal.
(Conference item: Conference , Speech with paper )
DOI: https://doi.org/10.1109/IECON.2019.8926815

Official URL: Volltext

Project information

Project title:
Project's official titleProject's id
DEMOBASE: DEsign and MOdelling for improved BAttery Safety and Efficiency769900
AI4DI: Artificial Intelligence for Digitizing Industry826060

Project financing: Andere
Part of the research leading to these results has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 769900 (DEMOBASE). Part of the research leading to these results has received funding from the ECSEL Joint Undertaking under grant agreement No. 826060 (AI4DI). This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation program and the ECSEL member states (i.e., BMBF in Germany).

Abstract in another language

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%.

Further data

Item Type: Conference item (Speech with paper)
Refereed: Yes
Keywords: State of charge; Batteries; Estimation; Voltage measurement; Computer architecture; Microprocessors; Temperature measurement
Institutions of the University: Research Institutions > Research Centres > Bayerisches Zentrum für Batterietechnik - BayBatt
Research Institutions
Research Institutions > Research Centres
Result of work at the UBT: No
DDC Subjects: 600 Technology, medicine, applied sciences > 620 Engineering
Date Deposited: 03 Jun 2022 10:15
Last Modified: 10 Aug 2022 13:36
URI: https://eref.uni-bayreuth.de/id/eprint/69848