## 6 Conclusions  
The article examined and measured a battery module comprising 16 cells, resulting in the acquisition of charging, and discharging curves. Binary classifiers and regression algorithms are described, analyzed, and implemented to assess the BESS based on Health and Charge indicators. The statistical results achieve a validation and testing accuracy of 96% and 95% for binary classifiers, while 98% and 97% corresponding to regression algorithms. The research work aimed to familiarize the reader with the importance of BESS operation according to the profile status and EOL criteria through Machine Learning. Regarding  
Figure 10: Graphical results of the regression algorithms. Test and predicted values are represented by the SOH vs. cycles.  
the implementation of both regression and binary classifiers, hyperparameter tuning and cross-validation must be considered to achieve optimal metrics of performance. Thus, the first step is to become familiar with the mathematical foundations of the computational algorithm to explain the behavior of the dataset in a battery.  
The main contribution of this research work is providing an initial methodology for estimating Health and Charge indicators in a BESS through binary classifiers and regression algorithms. The novelty associated with the proposed methods is the implementation of robust computational algorithms that not only automatically classify the profile status of a BESS, but also perform data science techniques to optimize the modeling during battery operation. Innovative techniques are summarized to obtain helpful insights about the KPIs in a BESS, all to demonstrate that promising results can be obtained when computer science techniques are implemented under the framework of renewable energy technologies.  
Finally, the data-driven approaches discussed in this article correspond to validated methodologies that the scientific community has implemented for different types of problems, BESS being one of the most important to achieve the energy transition. Furthermore, different datasets must be processed to evaluate the lifetime operation of a BESS, specially to test battery cells and design diagnostics methodologies; however, it is important to mention that external factors must be considered to build several types of models, such as thermal conditions, mechanical degradation, vibrations, etc. This work is the basis for designing a methodology for the diagnostics of BESSs through Machine Learning network architectures by the utilization of Explainable Artificial Intelligence methods. Moreover, this work provides a research environment for battery management in digital twin and electric vehicle applications to test and compare the performance of diverse types of battery modeling. This will help to establish assessment and verification procedures for fault diagnostics to support commercial consulting, research, and testing for enterprises based on the digital twin concept.  
Conceptualization, R.G.Z.; methodology, R.G.Z.; software, R.G.Z.; validation, R.G.Z., V.R. and A.R.; formal analysis, T.V., A.K. and R.G.Z.; investigation, R.G.Z.; resources, R.G.Z.; data curation, R.G.Z. and V.R.; writing--original draft preparation, R.G.Z. and V.R.; writing--review and editing, R.G.Z. and A.R.; visualization, R.G.Z. and V.R.; supervision, T.V., A.K. and A.R.; project administration, A.R.; funding acquisition, A.R. All authors have read and agreed to the published version of the manuscript.  
The research has been supported by the Estonian Research Council under grant PSG453 \"Digital twin for propulsion drive of an autonomous electric vehicle\".  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.  
The authors declare no conflict of interest.