ISSN 1608-4039 (Print)
ISSN 1680-9505 (Online)


Литиевые электрохимические системы

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

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%.

Flow batteries based on organic redox-systems for large-scale electric energy storage

Redox flow battery technology has been known since the 1970s. Their low specific characteristics have been of interest for a long time. Practical interest has arisen in recent decades because of the intensive development of alternative energy (such as solar and wind) and the regulation of peak loads in industrial networks.

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