Lithium-ion batteries have become an integral component of machines and products in every field of modern life. In order to assure optimal use of the batteries, it is necessary to accurately predict their various parameters such as state-of-health (SoH), end of life (EoL) and state-of-charge (SoC). In this paper the use of the third-degree polynomial and hybrid function for SoH estimation and remaining useful life (RUL) prediction are further validated on a different dataset. Furthermore, linear interpolation is used to enlarge the dataset and achieve more accurate results. Finally, the battery state-of-health estimation in terms of temperature dependency is analyzed.

State of Health analysis for Lithium-ion Batteries considering temperature effect

Petkovski, E.;Cristaldi, L.
2022-01-01

Abstract

Lithium-ion batteries have become an integral component of machines and products in every field of modern life. In order to assure optimal use of the batteries, it is necessary to accurately predict their various parameters such as state-of-health (SoH), end of life (EoL) and state-of-charge (SoC). In this paper the use of the third-degree polynomial and hybrid function for SoH estimation and remaining useful life (RUL) prediction are further validated on a different dataset. Furthermore, linear interpolation is used to enlarge the dataset and achieve more accurate results. Finally, the battery state-of-health estimation in terms of temperature dependency is analyzed.
2022
2022 IEEE International Workshop on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2022 - Proceedings
978-1-6654-8574-6
Battery management systems; State of health; States of charges; Temperature dependencies; Third degree polynomial
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1227453
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