###### Abstract
The traditional methods of estimating the SoC have significant limitations under these variable conditions. Therefore, several techniques in this regard are used to estimate the SoC for electric vehicle applications [15]. There are many studies on improved SoC estimation approaches that give a robust estimate of the SoC [16], among which the following can be mentioned: electrical measurement only-methods, like the Open-Circuit Voltage (OCV) technique which gives an approximation of the SoC by the monotonic relationship between the SoC and its OCV [17]. Although the Coulomb Counting method, which estimates the state of charge by integrating the discharge current over time, is easy to implement, it requires regular recalibrations because the error in this estimate accumulates over time due to the low accuracy of the current sensor [18]. Various approaches have been developed to estimate the SoC of Li-ion batteries, incorporating different modeling techniques. These include electrical models, which concentrate on representing the battery behavior through circuit-based simulations, and mathematical equation models, which capture battery dynamics using mathematical formulations. In addition, electrochemical models are employed to capture the complex interaction of chemical reactions within the battery [19; 20]. Besides these models, filters can be added to correct model uncertainties to achieve acceptable estimation performance [21], like the adaptive unscented Kalman filter (AUKF) [22; 23; 24], adaptive extended Kalman filter (AEKF) [25; 26], which employs the covariance adaptation-based method, and the ARX based Kalman filter, which have been discussed in the literature to estimate the SoC. In reference [27], an IAUKF method is proposed as a way to improve the accuracy of the SoC estimation of lithium-ion batteries. The results show that the IAUKF approach is highly robust to the noise covariance of the initial measurement. To improve the performance of the conventional SMO for estimation accuracy and ability to track with less ripples, an Adaptive-Gain SMO (AGSMO) is suggested in reference [28]. The authors of reference [29] studied various scenarios in which systematic errors such as voltage sensor bias, current sensor bias, and initial parameter identification errors [30] existed and tested them using an amp-hour (Ah) integration, a dual extended Kalman filter (DEKF) algorithm,and the extended Kalman filter (EKF). The test showed the robustness of the three algorithms in the scenarios giving different deviations. The SoC can be estimated efficiently using the DEKF algorithm [29], in the case of systematic errors perturbation, since the maximum error of the SoC estimation by the algorithm is less than 3%, which is better than the results obtained using an EKF and Ah integration. In reference [31], the authors proposed a novel adaptive dual extended Kalman filter [32] (ADEKF) to estimate the SoC. The results demonstrated that the algorithm was capable of estimating the SoC. The algorithm could provide a good reference to the battery management system and had good convergence. The estimation error of the SoC was 2.03%. Furthermore, a detailed comparison between two different structures using a neural network (NN) was investigated in reference [33], that is, the direct structure based on NARX-NN with SoC as the network output and the indirect structure using an (RBF)-based NN embedded with UKF (RBFNN-UKF) with the voltage as the output. The RBFNN-UKF indirect estimation demonstrated an interesting superiority over the direct estimation. The currently available and commonly used algorithms for estimating SoC are KF [34; 35], H-infinity filter [36], PF [37], RLS, PI, and SMO. In reference [38], the PF, KF, and H-infinity approaches were compared. The results indicated that the performance of the UKF, CDKF, PF, and H-infinity filters was superior to the other nonlinear filters. In terms of accuracy, PF performed better compared to EKF and was more precise than \(H_{\infty}\). However, the running time was slower for the PF.