Cd|KOH|NiOOH

Zn|NH4CI|MnO2

Li|LiClO4|MnO2

Pb|H2SO4|PbO2

H2|KOH|O2

Simulation and estimation of lithium-sulfur battery charge state using fuzzy neural network

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).

The possibility of determining the charge state of lithium-sulfur batteries using the ANFIS model was estimated. Easily measurable in practice physical quantities were used as input parameters of the model. They are the battery voltage, the rate of its change and the number of previous cycles. The analysis of ANFIS models with various parameters (the number and type of membership functions) was carried out. It was shown that ANFIS is a model that makes it possible to estimate the charge state of a lithium-sulfur battery with the accuracy of more than 95%. The proposed type of models can be used in control and monitoring systems, together with digital aggregated twins, for additional training of models based on real data and increasing the accuracy of estimating the charge state of lithium-sulfur batteries.

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