# A Unified Machine Learning Framework for Li-Ion Battery State Estimation and Prediction +
## 5 Implementation and Experimental Results
### Use Case 4: SoC Estimation on the Lifetime Dataset  
This use case deals with SoC estimation by processing a dataset created in the context of the European Union's Horizon 2020 project MARBEL \"Manufacturing and assembly of modular and reusable EV battery for environment-friendly and lightweight mobility\" that describes the whole lifetime of the battery [31]. It consists of 12 datasets describing the complete lifetime until the capacity reaches a certain percentage of its nominal capacity. This parameter has a range of values, from 91% to 99%, and thus, in some of these datasets, we can observe the degradation of the battery's maximum capacity. They have been created from different charging profiles, enabling the investigation of how each charging protocol affects the battery's cycle life. The cycle lives range from 400 to 500 cycles, and the datasets include four different charging protocols. The ambient temperature was controlled by the experimental chamber to be stable either at 10 \({}^{\circ}\)C or at 23 \({}^{\circ}\)C. It has been generated by CALB Prismatic Lithium-ion cells with NMC chemistry and a nominal capacity of 58 Ah. For this implementation, raw data and statistics have been selected as features. Table 11 presents the features that have been applied to this use case. Figure 13: The evaluation of different pipelines for RUL prediction in use case 3 by accuracy. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Pipeline** & \begin{tabular}{c} **Reference** \\ **Value** \\ **Estimation** \\ \end{tabular} & \begin{tabular}{c} **Definition of Training** \\ **and Testing Set** \\ \end{tabular} & **Features Selection** & \
\begin{tabular}{c} **ML Model** \\ **Selection** \\ \end{tabular} &
\begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} \\ \hline
18 & \begin{tabular}{c} remaining \\ life method \\ \end{tabular} & \begin{tabular}{c} set of datasets \\ \end{tabular} & \
\begin{tabular}{c} features \\ training: 85 \\ testing: 25 \\ \end{tabular} &
\begin{tabular}{c} cycle-based \\ features \\ current \\ cycle, total \\ \end{tabular} & CNN & 85.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: _Cont._In this use case, the most suitable evaluation metric is the RMSE, and it is measured in percentage (%) since it is expressed in the same unit as the target variable, in this case the SoC. In this case, four different pipelines have been implemented. The details about their configuration and their evaluation are presented in Table 12. Figure 15 depicts the RMSE for each pipeline, enabling the visualization of each pipeline's performance, and Figure 16 illustrates the actual and predicted values of the SoC for pipeline 21. In this use case, the number of datasets is limited, as described before, as well as the range of degradation of each battery. Nevertheless, pipeline 21 presents high accuracy since the features selected and the amount of training data are adequate and appropriate for this approach. Therefore, the pipelines that can be handled by this framework present diversity on data quality and quantity, enhancing the framework's flexibility and efficiency. Finally, in this pipeline, the input dataset presents a few spikes on the battery's behavior due to capacity imbalance in a few cycles. The model is not incapable of capturing this abnormality, since the training dataset is not adequate. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{3}{c}{**Features Selection for Use Case 4**} \\ \hline
**Raw Data** & **Statistics** \\ \hline voltage (V) & mean voltage (V\({}_{mean}\)) \\ \hline current (I) & mean current (I\({}_{mean}\)) \\ \hline temperature (T) \\ \hline capacity (C) \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Features applied in use case 4. Figure 14: The actual and predicted values of the RUL for pipeline 18. Figure 16: The actual and predicted values of SoC for pipeline 21.