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

: Bockrath, Steffen; Rosskopf, Andreas; Koffel, Stéphane; Waldhör, Stefan; Srivastava, Kushal; Lorentz, Vincent

Postprint urn:nbn:de:0011-n-5619646 (757 KByte PDF)
MD5 Fingerprint: e51d29aa084c1908f8317bc5fa9e918b
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Erstellt am: 24.10.2019

Institute of Electrical and Electronics Engineers -IEEE-; IEEE Industrial Electronics Society -IES-:
IECON 2019, IEEE 45th Annual Conference of the Industrial Electronics Society. Proceedings : October 14-17, 2019, Lisbon, Portugal
Piscataway, NJ: IEEE, 2019
ISBN: 978-1-7281-4878-6
ISBN: 978-1-7281-4879-3
IEEE Industrial Electronics Society (IECON Annual Conference) <45, 2019, Lisbon>
European Commission EC
H2020; 826060; AI4DI
Artificial Intelligence for Digitizing Industry
European Commission EC
H2020; 769900; DEMOBASE
DEsign and MOdelling for improved BAttery Safety and Efficiency
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IISB ()

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%.