# A Unified Machine Learning Framework for Li-Ion Battery State Estimation and Prediction +
## 5 Implementation and Experimental Results
### Descriptions of the Features and ML Models for the Applied Use Cases  
For the implemented pipelines, several features have been selected as more appropriate for these battery states according to the literature. In [16], raw data such as voltage, current, and temperature have been referred to as more suitable features as input to a neural network for SoC prediction, while voltage, current, and capacity are preferred for a model based on a support vector machine algorithm. In [26], cycle-based features are provided as input to a linear model for RUL selection, since they are domain-specific features that maintain high interpretability. Moreover, Ref. [29] presented a pipeline for SoC prediction with a deep learning model that has raw data and statistical features as the input. In summary, this pipeline has been used as the input for raw data such as voltage and temperature and statistics features such as the mean voltage and the mean current, since the latter are better values to feed the network compared to a large number of consecutive values for voltage and current.  
Table 2 presents the selected features for each group, as described in Section 4.1.3 regarding \"Feature Selection\", which were the most suitable for SoC and RUL prediction, chosen based on the specific pipelines applied according to the literature. Correspondingly, Table 3 demonstrates the specific parameters of each ML model applied in all use cases in this implementation, selected based on their suitability for SoC and RUL prediction.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Group of Features** & **Features** \\ \hline \hline \multirow{3}{*}{Raw data} & voltage (V) \\  & current (I) \\  & temperature (T) \\  & capacity (C) \\ \hline \hline \multirow{3}{*}{Statistics} & mean voltage (V\({}_{\text{mean}}\)) \\  & mean current (I\({}_{\text{mean}}\)) \\ \hline \multirow{3}{*}{Features by resampling} & resampled voltage in 5 mHz (V\({}_{\text{simpl}}\)) \\  & resampled voltage in 0.5 mHz (V\({}_{\text{0.5 mHz}}\)) \\  & resampled current in 5 mHz (I\({}_{\text{small}}\)) \\  & resampled current in 0.5 mHz (I\({}_{\text{0.5 mHz}}\)) \\ \hline \multirow{3}{*}{Features by interpolation} & linearly interpolated discharge capacity (QDLin) \\  & linearly interpolated temperature (TDLin) \\ \hline \multirow{3}{*}{Cycle-based features} & variance \(\Delta\)\(\Delta\)\({}_{\text{100-10}}\) \\  & minimum \(\Delta\)\({}_{\text{100-10}}\) \\  & skewness \(\Delta\)\({}_{\text{100-10}}\) \\  & kurtosis \(\Delta\)\({}_{\text{100-10}}\) \\  & difference between max discharge capacity and cycle 2 \\  & slope of the linear fit to the capacity fade curve, cycles 2 to 100 \\  & intercept of the linear fit to the capacity fade curve, cycles 2 to 100 \\  & discharge capacity, cycle 2 \\  & average charge time, first 5 cycles \\  & integral of temperature over time, cycles 2 to 100 \\  & minimum internal resistance, difference between cycle 100 and cycle 2 \\  & current cycle number \\  & total discharge time \\  & internal resistance (IR) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Features description for each group of features for the applied use cases.