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  4. State of Charge Estimation using Recurrent Neural Networks with Long Short-Term Memory for Lithium-Ion Batteries
 
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2019
Conference Paper
Title

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

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%.
Author(s)
Bockrath, Steffen
Fraunhofer-Institut für Integrierte Systeme und Bauelementetechnologie IISB  
Rosskopf, Andreas  
Fraunhofer-Institut für Integrierte Systeme und Bauelementetechnologie IISB  
Koffel, Stéphane
Fraunhofer-Institut für Integrierte Systeme und Bauelementetechnologie IISB  
Waldhör, Stefan  orcid-logo
Fraunhofer-Institut für Integrierte Systeme und Bauelementetechnologie IISB  
Srivastava, Kushal
Fraunhofer-Institut für Integrierte Systeme und Bauelementetechnologie IISB  
Lorentz, Vincent  orcid-logo
Fraunhofer-Institut für Integrierte Systeme und Bauelementetechnologie IISB  
Mainwork
IECON 2019, IEEE 45th Annual Conference of the Industrial Electronics Society. Proceedings  
Project(s)
AI4DI  
DEMOBASE  
Funder
European Commission EC  
European Commission EC  
Conference
IEEE Industrial Electronics Society (IECON Annual Conference) 2019  
Open Access
File(s)
Download (757.87 KB)
Rights
Use according to copyright law
DOI
10.24406/publica-r-405404
10.1109/IECON.2019.8926815
Language
English
Fraunhofer-Institut für Integrierte Systeme und Bauelementetechnologie IISB  
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