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

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.