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• W2897898980 abstract "This article presents a methodology of the prediction of chosen parameters of an energy storage system in subsequent recharging cycles. Because of a nonlinear description of process dynamics, a coupling of the terminal voltage and temperatures of a battery, the Recurrent Artificial Neural Network structure (R-ANN) is used. A training and testing data were gathered at the laboratory stand with the Valve-Regulated Lead-Acid Absorbent Glass Mat battery (VRLA AGM). As a result, we present an experimental evaluation of the proposed methodology." @default.
• W2897898980 created "2018-10-26" @default.
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• W2897898980 date "2018-01-01" @default.
• W2897898980 modified "2023-09-26" @default.
• W2897898980 title "Prediction of Selected Parameters of Energy Storage System using Recurrent Neural Networks" @default.
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• W2897898980 doi "https://doi.org/10.1016/j.ifacol.2018.09.524" @default.
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