**5. Conclusions**

In the current research paper, the following most relevant contributions of the authors can be highlighted:


Based on six statistical criteria values for all three SOC estimators, as a behavior response to an FTP-75 driving cycle profile test, it was possible to decide based on SOC accuracy performance if both models are suitable to be used in Part 2 [30], for adaptive Kalman filter SOC estimators design and implementation. Furthermore, the overall performance analysis indicates that both models are accurate and suitable to be used in Part 2 [30]. In future work, our investigations will continue an improved modelling approach, by integrating the e ffect of degradation, temperature and SOC e ffects. New directions of research in energy managemen<sup>t</sup> systems to develop power optimization techniques and for possible extensions to learning machine SOC estimation techniques will be a grea<sup>t</sup> challenge.

**Author Contributions:** R.-E.T., has contributed for, algorithm conceptualization, software, original draft preparation and writing it; N.T., has contributed for battery models investigation and validation, performed MATLAB simulations and formal analysis of the results; M.Z., has contributed for project administration, supervision, and results visualization; S.-M.R. has contributed for methodology, data curation and supervision. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** Research funding (discovery grant) for this project from the Natural Sciences and Engineering Research Council of Canada (NSERC) is gratefully acknowledged.

**Conflicts of Interest:** The authors declare no conflict of interest. This research received no external funding.
