Impact of Thermal Variability on SOC Estimation Algorithms

TL;DR

This paper investigates how thermal variability affects the accuracy of state of charge estimation algorithms for lithium-ion batteries, comparing Extended Kalman Filter and coulomb counting across different temperatures.

Contribution

It provides a comparative analysis of SOC estimation methods under thermal variations, highlighting the impact of temperature on algorithm accuracy.

Findings

Graphene batteries have the highest SOC at optimal temperature.

SOC-temperature relationship is non-linear.

Extended Kalman Filter outperforms coulomb counting in accuracy.

Abstract

While the efficiency of renewable energy components like inverters and PV panels is at an all-time high, there are still research gaps for batteries. Lithium-ion batteries have a lot of potential, but there are still some problems that need fixing, such as thermal management. Because of this, the battery management system accomplishes its goal. In order for a battery management system (BMS) to function properly, it must make accurate estimates of all relevant parameters, including state of health, state of charge, and temperature; however, for the purposes of this article, we will only discuss SOC. The goal of this article is to estimate the SOC of a lithium-ion battery at different temperatures. Comparing the Extended Kalam filter algorithm to coulomb counting at various temperatures concludes this exhaustive investigation. The graphene battery has the highest SOC when operated at the optimal temperature, as determined by extensive analysis and correlation between SOC and temperature is not linear