###### Abstract
The experimental results showed that the proposed model learned the battery dynamics well and offered a good SoC estimation with root-mean-square errors (RMSEs) less than 1.1% and mean errors (MAEs) less than 1%. Thanks to recent advancements in artificial intelligence, data-driven science is garnering increasing interest due to its remarkable advantages in terms of flexibility and adaptability. Moreover, the implementation of this technique is not complicated and demands relatively limited computations [60]. Nevertheless, it differs from other available techniques like electrochemical models or Equivalent Circuit Models which take a long time to be constructed and parameterized manually. To overcome these challenges, this study explores the use of advanced deep neural network (DNN), gated recurrent unit (GRU), and long short-term memory (LSTM) models. These models are capable of capturing complex, nonlinear relationships between input variables (current, voltage, temperature, aging effect) and the SoC, offering higher accuracy. Supervised learning demonstrates the capability to accurately estimate the SoC across a range of ambient temperature conditions. This is particularly advantageous as traditional estimation methods [51] often necessitate the use of distinct models tailored to specific temperature environments [61]. Several interesting and original contributions of this work are highlighted as follows:  
* The proposed DNN, GRU, and LSTM networks are trained using large simulation and experimental datasets for the first time to estimate the SoC. Then, the three networks are compared in terms of MAE, MSE, RMSE, and \(R^{2}\). * By rigorously testing the models on experimental and simulated data, an accurate estimate of the SoC is obtained. This validation not only confirms the effectiveness of these models but also underlines the potential of simulation to accelerate the development of battery management systems, resulting in significant savings in terms of time and cost. * This study simulates the effects of aging for different battery types and capacities, under different charge/discharge profiles. By comprehensively examining the impact of aging on SoC estimation, the paper provides crucial information on long-term battery performance, essential for optimizing battery management systems and prolonging lifespan. * Most existing papers consider only input data (voltage, current, temperature) to estimate the SoC. For the first time, the proposed models for SoC estimation are trained using a diversified dataset including (current, voltage, temperature, and aging effect) as inputs and SoC as output, so the battery can be exposed to varying dynamics. * The comparison between deep neural networks and recurrent neural networks demonstrates the high precision of the DNN in SoC estimation. This discovery underlines the potential and reliability of the DNN architecture. * This comparative study of DNN, GRU, and LSTM models aims to identify the architecture that provides the greatest accuracy of SoC estimation performance in various operational scenarios. The aim is to facilitate the subsequent selection of a hybrid model, paving the way for even more reliable and efficient battery management systems. * Extensive scenario testing incorporating various charge/discharge profiles, operating temperatures, and battery aging effects validates the accuracy and reliability of these models. This comprehensive evaluation guarantees the robustness of these models, essential for practical deployment in a variety of applications. * Leveraging simulation techniques, the present study offers a cost-effective and rapid approach to testing different battery configurations, management systems, and aging scenarios. By avoiding time-consuming and costly physical testing, this approach accelerates the development and optimization of battery management systems, facilitating rapid innovation in this field. In this research paper, a comparative analysis of simulation and experimental data for the accurate estimation of the SoC is conducted.