BATTERY STATE OF HEALTH DETERMINATION THROUGH VARIOUS TECHNIQUES

Abstract

In the 21st century, there has been a significant increase in demand for electric vehicles and energy storage technology. To meet this demand, various technologies have been developed, including lithium-ion batteries, sodium-ion batteries, and vanadium flow batteries. However, many high-energy batteries face challenges related to material longevity and safety. As a result, it is crucial to incorporate a battery state determination algorithm into battery management systems. There are several parameters that determine the battery's state, such as its health, charge level, power status, function, and safety. In this manuscript, a new algorithm is developed for assessment of the state of health of the battery through various techniques. The secondary and primary batteries were used for creating the state of health estimation model. As a secondary battery, a popular Li ion (NCM) cylindrical battery referred to as NCR18650 (Panasonic was selected and as a primary battery cylindrical lithium thionyl battery (Minamoto) was selected. In this research work, the state of charge of a battery was determined based on the thermodynamics parameters of the battery which is fitted through equation: SOC = 𝛼 + 𝛽 ∗ Δ𝐻 + 𝛾 ∗ Δ𝑆, where Δ𝐻 − enthalpy, Δ𝑆 – entropy, SOC – state of charge, and 𝛼, 𝛽, 𝛾 −are coefficients which depend on state of health and chemistry of a battery [1]. on of the dependence of coefficients to the state of health of the battery was done. One of the problems during the thermodynamics parameters measurement includes its long measurement time. Therefore, more fast measurement method of entropy and enthalpy was developed. Additionally, the state of health of the secondary battery was analyzed by and emerging technique of machine learning. A suitable algorithm and feature was chosen to assess state of health of the battery based on charge profile data.