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About the Possibility of Simulation the Discharge Characteristics of Lithium–Sulfur Batteries Using Fuzzi Neural Networks

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Abstract

In this work we considered the possibility of simulation of changes in the characteristics of lithium-sulfur batteries during cycling using an Adaptive Neuro-Fuzzy Inference System, ANFIS. The discharge profiles and the curve of decrease of discharge capacity of lithium-sulfur cells during cycling have been simulated. Neural network training was performed on every 5th cycle from the first to 95 cycles. It was shown that the simulated discharge profiles of lithium-sulfur cells are in good agreement with the experimental discharge profiles. The forecast depth of the decrease in the discharge capacity of lithium-sulfur cells during cycling with an accuracy of \( \gg \)5% was 45 cycles. Simulation time of one discharge profile lasts 4.5 seconds, which makes it possible to use this approach in the development of control and monitoring systems for batteries (Battery Management System, BMS).

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Notes

  1. BMS—Battery Management System.

  2. In this context, modeling means building a model rather than a computational experiment.

  3. SoH—State of Health, i.e., the cell efficiency.

  4. SoC—State of Charge, i.e., the charging state of the cell.

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Funding

This study was carried out within the frames of the State Grant on the subject no. AAAA-A20-120012090022-1 (V.S. Kolosnitsyn) and financially supported by the Russian Science Foundation (project no. 17-73-20115) “Experimental and theoretical study of mechanism of irreversible processes in lithium–sulfur batteries” (D.V. Kolosnitsyn, E.V. Karaseva, and E.V. Kuz’mina).

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Authors and Affiliations

  1. Ufa Institute of Chemistry UFRC RAS, 450054, Ufa, Russia

    D. V. Kolosnitsyn, E. V. Karaseva, E. V. Kuz’mina & V. S. Kolosnitsyn

Authors
  1. D. V. Kolosnitsyn
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  2. E. V. Karaseva
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  3. E. V. Kuz’mina
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  4. V. S. Kolosnitsyn
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Correspondence to D. V. Kolosnitsyn.

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This article does not contain any studies involving human participants or animals performed by any of the authors.

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The authors declare that they have no conflicts of interest.

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Translated by T. Safonova

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Kolosnitsyn, D.V., Karaseva, E.V., Kuz’mina, E.V. et al. About the Possibility of Simulation the Discharge Characteristics of Lithium–Sulfur Batteries Using Fuzzi Neural Networks. Russ J Electrochem 57, 306–309 (2021). https://doi.org/10.1134/S1023193521030046

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  • DOI: https://doi.org/10.1134/S1023193521030046

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