This work focuses on the implementation of two nonlinear filters, namely the Extended Kalman Filter (EKF) and the Ensemble Kalman Filter (EnKF). The main objective is to assess the performance of these filters in the nonlinear state estimation problem of a Lithium-ion battery's state of charge. The problem statement, rationale, and objectives of the study are outlined. The theoretical foundation of both filters is presented, emphasizing their probabilistic framework derived from the Bayes filter. The dynamic system representing the battery's charge and discharge, with respect to the charging current, is modeled and simulated. The results of the performance evaluation of both filters in estimating the battery's state of charge are provided. The findings demonstrate satisfactory performance of both filters in reconstructing the true system states. However, the EnKF filter exhibits superior performance compared to the EKF filter, as observed through graphical and numerical analysis based on the root mean square error (RMSE) index.