## 5 Conclusions  
The accurate estimation of the SoC using deep learning algorithms is essential for a BMS to protect electric vehicle batteries against deep discharge and overcharging. In this paper, experimental data obtained using a reliable database were collected on a Panasonic UR18650 ZY Li-ion battery, and simulation data using Matlab/Simulink software were used to test different profiles to ensure the reliability of the proposed models. A deep neural network (DNN), long short-term memory (LSTM) model, and gated recurrent unit (GRU) were used to estimate the SoC using both types of data to train, test, and validate each of the above models. A good correspondence in results was observed when comparing the estimated SoC with the real SoC, demonstrating the effectiveness of the suggested networks for learning the problem of estimating the SoC. Neural networks have proven their reliability, but the present DNN algorithm offered a robust and precise prediction for the battery sets used, with a mean error value under 0.5% and a maximum error value under 2.5%. Incorporating the effects of temperature and aging proved crucial to improving estimation accuracy, highlighting the importance of these factors often overlooked in previous studies. The future prospects of this research are promising. The results obtained can contribute to improving battery management systems in electric vehicles, thus increasing their efficiency and lifespan. In addition, this study paves the way for future research aimed at developing even more sophisticated neural network models, capable of handling varied and extreme conditions. Finally, although this study focused on lithium-ion batteries, the techniques developed can be adapted to other battery types, thus broadening the scope of this research. The dynamics of batteries will be influenced by aging after a period of frequent use. In terms of perspectives, future research could consider the effect of battery aging to estimate the remaining useful life (RUL) and state of health (SoH) of different commercial battery cells. The software Matlab/Simulink will enable us to consider the effect of battery aging to improve the performance of SoH estimation methods. This work will concentrate on battery SoH estimation for electric vehicles based on deep learning algorithms (DNN), with the aim of developing a smarter combined method that incorporates the different models to create an even more accurate and efficient estimation model. This study will be followed by the realization and design of a BMS prototype in which the proposed methods will be executed and tested on a real electric vehicle and presented in a future research paper. However, it is important to note that high temperatures can also have negative effects on the performance and life of the battery. For example, it can lead to increased electrode and electrolyte wear, as well as increased corrosion and decomposition of battery materials. Therefore, it is important to consider the effects of high temperatures on overall battery performance and take steps in the future to minimize these negative effects, such as cooling the battery and reducing battery use at high temperatures. **Author Contributions:** Conceptualization, S.E.F. and J.K.; methodology, S.E.F., J.K. and Z.H. ; software, S.E.F. ; validation, J.K., J.V., S.T., S.G., A.K., R.M. and M.O.J. ; formal analysis, S.E.F. ; investigation, S.E.F. ; resources, J.V., S.T., S.G. and A.R. ; data curation, S.E.F., Z.V. and O.L. ; writing--original draft preparation, S.E.F., J.K., Z.V. and O.L. ; writing--review and editing, J.V., S.T., S.G., A.K., R.M. and A.R. ; visualization, S.E.F., J.V. and Z.H. ; supervision, J.K., J.V. and M.O.J. ; project administration, J.K., R.M., Z.H., A.R. and M.O.J. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding.