Data Augmentation and Feature Selection for the Prediction of the State of Charge of Lithium-Ion Batteries Using Artificial Neural Networks

Sebastian Pohlmann (), Ali Mashayekh, Manuel Kuder, Antje Neve and Thomas Weyh
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Sebastian Pohlmann: Institute of Distributed Intelligent Systems, University of the Bundeswehr, Werner-Heisenberg-Weg 39, 85577 Neubiberg, Germany
Ali Mashayekh: Institute of Electrical Energy Systems, University of the Bundeswehr, Werner-Heisenberg-Weg 39, 85577 Neubiberg, Germany
Manuel Kuder: Bavertis GmbH, Marienwerderstraße 6, 81929 Munich, Germany
Antje Neve: Institute of Distributed Intelligent Systems, University of the Bundeswehr, Werner-Heisenberg-Weg 39, 85577 Neubiberg, Germany
Thomas Weyh: Institute of Electrical Energy Systems, University of the Bundeswehr, Werner-Heisenberg-Weg 39, 85577 Neubiberg, Germany

Energies, 2023, vol. 16, issue 18, 1-14

Abstract: Lithium-ion batteries are a key technology for the electrification of the transport sector and the corresponding move to renewable energy. It is vital to determine the condition of lithium-ion batteries at all times to optimize their operation. Because of the various loading conditions these batteries are subjected to and the complex structure of the electrochemical systems, it is not possible to directly measure their condition, including their state of charge. Instead, battery models are used to emulate their behavior. Data-driven models have become of increasing interest because they demonstrate high levels of accuracy with less development time; however, they are highly dependent on their database. To overcome this problem, in this paper, the use of a data augmentation method to improve the training of artificial neural networks is analyzed. A linear regression model, as well as a multilayer perceptron and a convolutional neural network, are trained with different amounts of artificial data to estimate the state of charge of a battery cell. All models are tested on real data to examine the applicability of the models in a real application. The lowest test error is obtained for the convolutional neural network, with a mean absolute error of 0.27%. The results highlight the potential of data-driven models and the potential to improve the training of these models using artificial data.

Keywords: lithium-ion batteries; state of charge; machine learning; artificial neural networks; data augmentation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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