This paper considers the estimation of the state-of-charge of rechargeable batteries based on a classifier trained using two methods. One method uses the values of the parameters in an equivalent circuit model, identified using a frequency-domain approach. The other method is based on a mathematical approximation of the battery voltage time-response to a given 3 s current signal. Classification resorts to a linear discriminant analysis classifier trained both by experimental data and by data obtained through augmentation methods. It is shown that the time-domain based classifier may achieve better performance in terms of probability of correct state-of-charge classification, using experiments of significant less duration than those associated with the usage of the frequency-domain experiments.

Time-domain Battery State-of-Charge Estimation based on Domain-Transformation and Linear Discriminant Analysis

Carbone P.;De Angelis A.;Buchicchio E.;Santoni F.;Moschitta A.
2023

Abstract

This paper considers the estimation of the state-of-charge of rechargeable batteries based on a classifier trained using two methods. One method uses the values of the parameters in an equivalent circuit model, identified using a frequency-domain approach. The other method is based on a mathematical approximation of the battery voltage time-response to a given 3 s current signal. Classification resorts to a linear discriminant analysis classifier trained both by experimental data and by data obtained through augmentation methods. It is shown that the time-domain based classifier may achieve better performance in terms of probability of correct state-of-charge classification, using experiments of significant less duration than those associated with the usage of the frequency-domain experiments.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11391/1587533
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