**5. Conclusions**

In this work, a physics-based TECM and a data-driven ANN model have been implemented as thermal models for a prismatic li-ion cell equipped with electronics for the application of intelligent battery systems. For the TECM, a lumped thermal model of the cell has been combined with a lumped electronics model. The novel ANN approach with a NARX network is implemented with the same model interfaces as the TECM to fit into an electro-thermal model framework for a BEV application. Both models are parametrized/trained with datasets based on a reference hardware setup of our previous work [19]. Subsequently, the model behavior is validated and several applications of temperature estimation and prediction are investigated. The following conclusions can be made:


The focus of future work is on the development and optimization of the corresponding algorithms for SOP prediction and advanced thermal management functionalities and related verification on an intelligent battery system hardware.

**Author Contributions:** Conceptualization, J.K., M.S. and L.K.; methodology, J.K.; software, J.K. and M.S.; validation, J.K. and M.S.; investigation, J.K., M.S. and L.K.; writing—original draft preparation, J.K. and M.S.; writing—review and editing, J.K., M.S. and L.K.; visualization, J.K.; supervision, L.K. and C.E.; project administration, C.E. All authors have read and agreed to the published version of the manuscript.

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

**Acknowledgments:** The authors would like to express their appreciation to Michael Hinterberger (AUDI AG, Ingolstadt) for the valuable expert advice and acknowledge F. Haselbeck and L. Lechermann for the extensive discussions related to the implementation and training of learning systems. This work was supported by the AUDI AG, Ingolstadt, Germany.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Abbreviations**

The following abbreviations are used in this manuscript:

