Article
2021

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


D. V. Kolosnitsyn D. V. Kolosnitsyn , E. V. Karaseva E. V. Karaseva , E. V. Kuz’mina E. V. Kuz’mina , V. S. Kolosnitsyn V. S. Kolosnitsyn
Russian Journal of Electrochemistry
https://doi.org/10.1134/S1023193521030046
Abstract / Full Text

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).

Author information
  • Ufa Institute of Chemistry UFRC RAS, 450054, Ufa, Russia

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

References
  1. Kolosnitsyn, V.S. and Karaseva, E.V., Lithium–sulfur batteries: problems and solutions, Russ. J. Electrochem., 2008, vol. 44, p. 506. doi. https://doi.org/10.1134/S1023193508050029
  2. Galushkin, N.E., Yazvinskaya, N.N., Galushkin, D.N., and Galushkina, I.A., Nonlinear structural model of the battery, research of processes of relaxation after charge, Elektrokhim. Energ., 2014, vol. 14, no. 1, p. 45.
  3. Aksyutenok, M.V., Moskvichev, A.A., Gun’ko, Yu.L., Kozina, O.L., and Mikhalenko, M.G., Modeling of charge–discharge processes on a cadmium electrode of a nickel–cadmium battery, Izv. Vyssh. Uchebn. Zaved.: Khim. Khim. Tekhnol., 2012, vol. 55, no. 4, p. 96.
  4. Parthiban, T., Ravi, R., and Kalaiselvi, N., Exploration of artificial neural network [ANN] to predict the electrochemical characteristics of lithium-ion cells, Electrochim. Acta, 2007, vol. 53, p. 1877. https://doi.org/10.1016/j.electacta.2007.08.049
  5. Fotouhi, A., Auger, D.J., Propp, K., and Longo, S., Lithium–sulfur battery state-of-charge observability analysis and estimation, IEEE Trans. Power Electronics, 2018, vol. 33, no. 7, p. 5847. https://doi.org/10.1109/TPEL.2017.2740223
  6. Mochalov, S.E., Antipin, A.V., Nurgaliev, A.R., and Kolosnitsyn, V.S., Multichannel potentiostat-galvanostat for cycling of batteries and electrochemical cells, Elektrokhim. Energ., 2015, vol. 15, no. 1, p. 45.
  7. Kolosnitsyn, D.V., RF Inventor’s Certificate no. 2019611773, 2019.
  8. Kolosnitsyn, D.V., Kuzmina, E.V., and Karaseva, E.V., Automation of data processing of electrochemical studies of battery cells, Elektrokhim. Energ., 2019, vol. 19, no. 4, p. 186. https://doi.org/10.18500/1608-4039-2019-19-4-186-197