# A Unified Machine Learning Framework for Li-Ion Battery State Estimation and Prediction +
## 7 Conclusions and Future Work  
This work presented a unified, modular, and extensible framework for Li-ion battery health state estimation and prediction, leveraging machine learning methodologies. The proposed framework integrates flexible configuration parameters, standardized data processing pipelines, and a wide selection of predictive modeling techniques to address the diverse and complex requirements of battery state prediction tasks. By instantiating this framework for both SoC estimation and RUL prediction, we demonstrated its versatility and practical applicability across a variety of datasets and experimental conditions. Through a series of comprehensive use cases--including single-cycle and lifetime battery datasets--we validated the framework's adaptability across different configurations. The results confirmed that the choice of pipeline components, including feature engineering techniques and ML algorithms, significantly influences predictive performance. In particular, methods such as the LSTM and linear regression showed strong results in appropriate contexts, while AutoML proved to be a viable option for automating model selection and tuning, albeit with variable outcomes depending on dataset characteristics. The main contribution of this work lies in offering a systematic approach that promotes the reusability, transparency, and comparability of battery state estimation models. This unified methodology addresses many of the current challenges in battery analytics, such as heterogeneous data integration, state-dependent model selection, and the need for standardized evaluation protocols. Our future work will move towards the following directions: First, we will investigate the instantiation of the proposed framework to additional battery state objectives, such as SoH, SoP, and SoE. Second, we will integrate hybrid approaches that combine ML with physics-based modeling to enhance interpretability and robustness. In this way, we can evaluate various degradation mechanisms and battery chemistries regarding their \begin{table}
\begin{tabular}{c c c} \hline  & **Paired \(t\)-Test** & \\ \hline
**Pipelines Compared** & **t-Statistic** & **\(p\)-Value** \\ \hline pipeline 8: SVR model & \(-19.0473\) & \(4.91\times 10^{-60}\) \\ pipeline 9: FFNN model & & \\ pipeline 21: FFNN model & & \\ pipeline 22: autoML model & \(-521.7533\) & \(<\)0.0001 \\ \hline \end{tabular}
\end{table} 
Table 16: Statistical analysis between applied pipelines. influence in the pipeline's effectiveness and adjustability. Third, we will use datasets with information and variables from a BMS that depicts real-world driving conditions with the purpose of demonstrating the framework's robustness and flexibility under dynamic operating conditions. Moreover, we will study the influence of the battery's chemistry and temperature in the effectiveness of the pipelines regarding several battery states. This would be achieved by applying extensive pipelines on datasets with different battery chemistries and in different ambient temperatures and by applying the method of transfer learning between pipelines that use datasets referring to batteries with different cell chemistries and temperatures. Finally, we will apply the framework to real-time applications, and we will deploy it on edge computing platforms in order to facilitate its use in commercial BMSs. Conceptualization, A.F., A.B. and G.M. ; methodology, A.F., A.B. and G.M. ; software, A.F. and K.L. ; validation, A.F. ; formal analysis, A.F. and A.B. ; investigation, A.F. ; resources, G.M. ; data curation, A.F. and K.L. ; writing--original draft preparation, A.F., A.B., K.L. and G.M. ; writing--review and editing, A.F., A.B., K.L. and G.M. ; visualization, A.F. ; supervision, G.M. ; project administration, A.B. ; funding acquisition, G.M. All authors have read and agreed to the published version of the manuscript.