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

Author

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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)

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.

Suggested Citation

  • Sebastian Pohlmann & Ali Mashayekh & Manuel Kuder & Antje Neve & Thomas Weyh, 2023. "Data Augmentation and Feature Selection for the Prediction of the State of Charge of Lithium-Ion Batteries Using Artificial Neural Networks," Energies, MDPI, vol. 16(18), pages 1-14, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6750-:d:1244983
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    References listed on IDEAS

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    1. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
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    3. Jong-Hyun Lee & In-Soo Lee, 2021. "Lithium Battery SOH Monitoring and an SOC Estimation Algorithm Based on the SOH Result," Energies, MDPI, vol. 14(15), pages 1-16, July.
    4. Buberger, Johannes & Kersten, Anton & Kuder, Manuel & Eckerle, Richard & Weyh, Thomas & Thiringer, Torbjörn, 2022. "Total CO2-equivalent life-cycle emissions from commercially available passenger cars," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
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    Cited by:

    1. You, Yuqiang & Lin, Mingqiang & Meng, Jinhao & Wu, Ji & Wang, Wei, 2024. "Multi-scenario surface temperature estimation in lithium-ion batteries with transfer learning and LGT augmentation," Energy, Elsevier, vol. 304(C).
    2. Shun-Chung Wang & Zhi-Yao Zhang, 2023. "Research on Optimum Charging Current Profile with Multi-Stage Constant Current Based on Bio-Inspired Optimization Algorithms for Lithium-Ion Batteries," Energies, MDPI, vol. 16(22), pages 1-23, November.

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