###### Abstract
The results of the experiments indicated good performance in accuracy and runtime for the H-infinite filter. As a result, this method offered a suitable approach towards SoC online estimation. The recursive least squares method enabled a precise calibration of dynamic model parameters and real-time variations while preserving the simplicity of computation. However, in reference [39], Multiple Adaptive Forgetting Factor Recursive Least Squares (MAFF-RLS) estimates were employed for the estimation of the SoC and the cellular model's internal parameters. The KF is currently the most widely used approach for SoC estimation. The EKF is the most commonly used in the literature [40; 41] because of its capability to operate on a nonlinear system and due to its simplicity compared to more sophisticated KFs. There exist several different variants of the KF; in reference [42], eighteen different KF types were listed. Regarding performance, the EKF demonstrated high precision. Various approaches, including an adaptive EKF, unscented KF, central difference KF, and Dual EKF were proposed to develop a better EKF and improve its performance. An Artificial Neural Network (ANN) was employed in references [43; 44] to estimate the SoC. The neural network (NN) in reference [44] was coupled with a fuzzy model to generate an Adaptive Neuro-Fuzzy Inference System (ANFIS). The method SMO provides good reliability and robustness to modeling errors and uncertainties. However, in reference [15], the SMO method was coupled with a NN algorithm to decrease the error in the estimation of the SoC. Also, in reference [45], the SoC was estimated by an SMO using a standard driving profile. In Reference [46], for the purpose of achieving a solution that is simple and reducing the processing time while obtaining similar performance compared to other popular estimators, a new algorithm based on disturbance observation-based (DOB) was developed. The output of the proposed model demonstrated that the DOB algorithm was able to estimate the SoC with more accuracy than conventional approaches. Machine learning (ML) methods can also be used to generate a data-driven model [47; 48] and identify the nonlinear relationship between the SoC and measurable variables such as current, voltage, and temperature [49; 50; 51]. In reference [52], to estimate the thermal and electrical characteristics of lithium-ion battery cells, the authors evaluated the performance of four ML methods (Random Forest Regressor (RFR), linear regression, Decision Tree Regressor (DTR), and K-nearest neighbors regressor) [53]. In that case study, the DTR model gave the best performance. In reference [54], the authors apply a long short-term memory neural network (LSTM). The model based on the LSTM algorithm outperforms several algorithms [54] such as RF, SVR, and simple RNN. The mean absolute error (MAE) of the SoC estimation reaches 0.91%. The proposed method demonstrates satisfactory performance in accuracy and robustness in the case of a battery with different temperature conditions. There are also other algorithms in the literature to estimate the SoC [9; 55], including the support vector machine (SVM) and K-nearest neighbors (KNN) [56] models. In reference [57], the authors proposed a state-of-charge estimation model based on a dynamically driven recurrent network (DDRN). A nonlinear autoregressive architecture with exogenous inputs (NARX) of the DDRN was applied for the SoC and state-of-health (SoH) estimation [58]. A recurrent neural network with long short-term memory (LSTM) and an unscented Kalman filter (UKF) were combined to estimate the SoC of a 18650 lithium iron phosphate battery in reference [59]. The UKF was incorporated into the network to filter the output noise and improve the accuracy of the SoC estimation. The SoC estimate was the model's output, and its inputs were (current, voltage, temperature).