# A Unified Machine Learning Framework for Li-Ion Battery State Estimation and Prediction +
## 4 Instantiation of the Proposed Framework to SoC Estimation and RUL Prediction
### Process for Battery State Prediction Instantiated for SoC and RUL
#### 4.2.4 Model Building  
Model building forms the basis for instrumenting and fine-tuning the selected algorithms. Based on the previously defined dimensions, the aforementioned trained algorithms and their configuration parameters are prepared for further processing. This step ensures that the chosen models are well-aligned with the problem context and data characteristics, enabling effective learning from historical patterns. It also involves initializing key settings such as learning rates, optimization strategies, and model-specific parameters to optimize performance. Table 1 presents the architecture of each ML model applied in this framework as well as the necessary tuning parameters. There are some parameters that are demonstrated without their values since they are configurable and are defined in the configuration process of this framework. In this way the framework provides flexibility and applicability to a range of different pipelines. Moreover, in Table 1, several advantages and disadvantages that contributed to ML model selection for this application are presented for each ML model.  
\begin{table}
\begin{tabular}{c c c l} \hline \hline
**ML Model** & **Tuning Parameters** & **Advantages and Disadvantages** \\ \hline \multirow{8}{*}{LSTM} & 4 layers (1 st: selected neurons; 4 th: & \\  & activation layer ReLu) & + & Temporal dependencies \\  & Loss: MAE & + & Handles historical data \\  & Optimizer: [PERSON] & + & Captures non-linear relationships \\  & Validation split: 0.2 & \(-\) & Training cost \\  & Epochs & \(-\) & Needs large amount of data \\  & Batch size & & \\ \hline \multirow{8}{*}{FFNN} & 4 layers with selected neurons in & \\  & the 2 nd and 3 rd layer & + & Captures non-linear relationships \\  & Activation function: ReLu & + & Flexibility in applications \\  & Optimizer: [PERSON] & + & Simple architecture \\  & Loss: MSE & \(-\) & No temporal dependencies \\  & Validation split: 0.2 & \(-\) & Overfitting risk \\  & Epochs & & \\  & Batch size & & \\ \hline \multirow{8}{*}{CNN} & 7 convolutional and max pooling & \\  & layers for detailed features and & + & Local patterns detection \\  & scalar features, respectively & + & Less parameters than fully \\  & 3 more layers with selected & & connected networks \\  & neurons in the 1 st one & \(-\) & Cannot handle temporal dependencies \\  & Activation function: ReLu & \(-\) & Complex architecture \\  & Optimizer: [PERSON] & & \\ \hline \multirow{8}{*}{AutoML} & Structured data regressor() & + & Automated model selection \\  & Loss: MSE & + & Automated hyperparameter tuning \\  & Epochs & + & High efficiency \\  & Batch size & \(-\) & Hard debugging \\  & Max trials & \(-\) & High computational cost \\ \hline \multirow{8}{*}{LR} & \multirow{4}{*}{Linear regressor()} & + & Simplicity \\  & & + & Low data requirement \\  & & \(-\) & Cannot handle \\  & & & non-linear relationships \\  & & \(-\) & Cannot handle outliers \\ \hline \multirow{8}{*}{EN} & \multirow{4}{*}{Elastic Net()} & + & Handles correlated features \\  & & + & Balances sparsity \\ \cline{1-1}  & \multirow{2}{*}{Grid search} & \(-\) & Requires tuning of two \\ \cline{1-1}  & & & regularization weights \\ \cline{1-1}  & & \(-\) & Cannot handle \\ \cline{1-1}  & & & non-linear relationships \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tuning parameters and characteristics for ML models.