Ever since the utilization of batteries in both the commercial and passenger vehicles demands the accurate estimation of state of charge (SoC) has become essential. Among the famous battery technologies, lithium sulfur (Li–S) batteries are most advanced technologies used by industry, particularly considering lithium-ion batteries and disadvantage associated with these batteries like heating and puncture issues. There were two most promising methods used in estimating the battery state; one is Coulomb counting, and other is open-circuit voltage monitoring. But these two methods have proven to be ineffective in the context of Li–S batteries, leading to a scarcity of accurate estimation methods. While existing approaches involving use of famous methods like extended Kalman filter (KF) and unscented KF exhibit sufficient performance in estimating SoC, their reliance primarily on a single filter utilizing a precalibrated behavioral reparameterization of an equivalent circuit network is noteworthy. In the pursuit of heightened performance, an alternative framework, denoted as the extended Kalman filter, is investigated. This methodology estimates short-term dynamic parameters pertained to the circuit through voltage and current measurements, which are subsequently assimilated into a more gradual SoC estimator. This research introduces a behavioral variant of the extended KF, specifically tailored for estimating Li–S batteries SoC. The proposed estimator is designed in such way that circuit current term as a deciding factor and also validated through the pulse discharge test, simulation validation is carried out through the driving cycles along with partial discharge phenomenons. The estimators put forward in this research showcase heightened accuracy, hinting at their potential extension for state-of-health measurement. The behavioral circuit reparameterization holds promise for broader applications in battery chemistries beyond Li–S.