# A Unified Machine Learning Framework for Li-Ion Battery State Estimation and Prediction +
## 6 Discussion  
The experimental evaluation of the proposed unified framework across diverse datasets and use cases provides significant insights into its flexibility, performance, and broader applicability. The design philosophy behind the framework--centered on configurability and modularity--has proven effective in adapting to both single-cycle and lifetime battery data, supporting a wide range of predictive objectives including State of Charge (SoC) estimation and Remaining Useful Life (RUL) prediction. Figure 17: The evaluation of different pipelines for SoC prediction in use case 5 by the RMSE metric. Figure 18: The actual and predicted values of SoC for pipeline 25. The variety of implemented pipelines underscores the impact of design choices--such as feature selection strategies, training/testing splits, and machine learning model selection--on the final predictive accuracy. For instance, LSTMs consistently performed well in timeseries-based SoC estimation, particularly when combined with raw or resampled features, reflecting their strength in capturing temporal dependencies in battery behavior. Conversely, simpler models such as linear regression and Elastic Net demonstrated competitive performance for RUL prediction tasks when applied with carefully engineered cycle-based features, indicating that model complexity does not always equate to higher accuracy, particularly when data are well-structured and features are meaningful. For further evaluations of the proposed framework's efficiency, the results of approaches based on the literature have been compared with the ones of pipelines applied through this framework. More specifically, we performed a comparative analysis between pipelines 1 and 14 and pipelines implemented based on the literature regarding the SoC. Pipeline 1 was compared with the pipeline that was implemented with an LSTM model for SoC prediction and analyzed in [17], in terms of methodology and performance. In Table 15, the configuration parameters are presented for each pipeline for a clearer and more constructive comparison. In both pipelines, SoC prediction has been used the Madison dataset [19], which has been described in use case 1. In the case of SoC prediction, it is obvious that the differences between these two pipelines include the architecture of the model and the computational cost for the training process. The RMSE metric in each pipeline shows that there is a slightly better performance of pipeline 1 in use case 1, indicating the importance of the model's configuration. From this comparison, we can observe that the implemented framework is capable of handling different approaches for battery state prediction with the same or better performance, illustrating its flexibility and robustness in terms of pipeline configuration and behavior. For further evaluations we performed paired t-tests between different applied pipelines with the purpose of examining whether the difference in the pipelines' performance is statistically important or not. In the first place, we compared pipelines 8 and 9, and by the value of the \(p\)-value, we can conclude that the difference in mean squared errors is highly statistically significant. In other words, the FFNN pipeline significantly outperforms the SVR pipeline in terms of RMSE (\(p<<0.05\)). In a similar way, from the second comparison that we performed, we can observe that the autoML pipeline outperforms the FFNN model, and this difference is statistically important since a \(p\)-value of effectively zero indicates that the difference in mean squared errors is highly significant. In Table 16, the values of the parameters of paired \(t\)-tests are presented for each comparison conducted.