**6. Conclusions**

An approach for battery DT implementation was presented. The 5-step approach allows the readers to recognize the difference between a battery model and battery DT implementation. The first challenge recognized for battery DT implementation is making the battery operational data available to the model. Cloud services have been integrated with onboard-BMSs in the past, but this is not common. In the coming years, the data integration method for battery DTs has to be standardized, even though it might entail initial investment and implementation time. The second challenge recognized for battery DT implementation is that the method of model parameter-update during usage is not well established and still needs further research. The results of the paper indicate that, if tracked, the DFN model parameters will keep changing after a certain period of usage and cycling. The other methods proposed for parameter-update in the paper will be investigated in future work.

This paper provides a consistent view of a battery DT and the added value, i.e., its functionalities that it offers during battery operation and EoL. The benefits of battery DTs are: improved representation, performance estimation, behavioral predictions, optimization strategies, and integration of battery life cycle attributes to the remaining DT functions. Based on the results provided in this paper, a battery DT can widen the scope of current BMS functionalities by evaluating the degradation effect that the driving cycle has on the battery from an electrochemical or electrical perspective.

The quantitative uncertainty of the potential costs, infrastructure challenges, and return on investments for battery DTs still exist. However, the KPIs identified in this paper will play a significant role in quantifying the battery DT attributes. Promising future scope exists in evaluating the KPIs for DT across the life cycle phases of a battery. As part of the ongoing research to evaluate the feasibility of battery DTs, its functionalities and quantification of its attributes across the lifecycle will be explored in future works.

**Author Contributions:** Conceptualization, S.S.; approach, S.S.; implementation, S.S.; writing original draft preparation, S.S.; writing—review and editing, M.W., K.P.B.; visualization, S.S.; project administration, M.W.; funding acquisition, M.W. and K.P.B.; supervision, M.W. and K.P.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Federal Ministry for Economic Affairs and Energy (BMWi), funding code 19I21014C. The authors wish to thank the Federal Ministry for Economic Affairs and Energy, Germany for funding this work as part of the accompanying research of the project "iBMS".

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are openly available in GitHub at [69].

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