Batteries are intricate electrochemical devices characterized by their non-linearity and stochasticity, which are contingent upon the prevailing circumstances and environmental factors. The complex and challenging issue of accurately estimating the State-of-Charge (SoC) of a battery is attributed to its non-linearity. The accurate estimation of the SoC of an Electric Vehicle (EV) battery is of utmost importance for the purposes of real-time monitoring, safety regulation, optimal energy utilization, and efficient vehicle operation. The utilization of this tool provides the determination of the critical aspects inside the Energy Management System (EMS) of an EV, hence influencing the vehicle’s range and performance. Various methodologies have been employed in the estimation of SoC for EV batteries. This work presents a comparative analysis of different SoC estimating methods for EV batteries. The methods evaluated include Direct Measurement, Coulomb Counting, Model-Based Method, Data-Driven Method, and Hybrid Method. The evaluation of each method’s performance is conducted based on metrics such as accuracy, complexity, and applicability across various driving conditions and environments. This paper constitutes a small part of the author’s critical review for their PhD research.