## 6 System Evaluation and Model Prediction Performance
### Models Prediction Performance
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{5}{c}{**Forecast Steps = 120 Days**} \\ \cline{2-6}  & **MSE** & **RMSE** & **MAE** & **MAPE** & **SMAPE** \\ \hline MLP & 22.33 & 4.73 & 4.01 & 5.27 & 5.43 \\ CNN & 21.82 & 4.67 & 3.98 & 6.31 & 6.11 \\ LSTM & 2.91 & 1.70 & 1.50 & 2.29 & 2.26 \\ GRU & 4.23 & 2.06 & 1.41 & 2.18 & 2.24 \\ Multi-head attention transformer & 13.33 & 3.65 & 3.11 & 4.62 & 4.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Prediction performance results of five distinct models trained on the FELL platform to forecast the SoH of electric vehicle batteries over the subsequent 120 days. The CNN model is composed of a 1D convolutional layer with 64 filters, followed by a Max-Pooling layer, a dropout layer, and a fully connected dense layer, amounting to a total of 6296 trainable parameters. The GRU model includes a GRU layer with 128 units, followed by two GRU layers with 64 and 32 units, and a fully connected dense layer with 16 units, totaling 99,528 trainable parameters. Finally, the multi-head attention transformer model comprises a multi-head attention layer, followed by fully connected dense layers, a reshape layer, and a TimeDistributed layer, resulting in a total of 192,763 trainable parameters. Figure 12 illustrates a comparison of the MSE of the five models in predicting the SoH of the user's electric vehicle battery over varying time horizons: 7 days, 30 days, and 120 days. Figure 13 provides a SMAPE comparison of models predicting battery SoH for the next 7 days, 30 days, and 120 days. The experimental results validate the functionality and effectiveness of the prediction model implemented by the FELL platform. Notably, the FELL system not only furnishes users with predictive outcomes regarding the electric vehicle battery status but also offers a comparative analysis of the prediction performance across multiple models for users' reference. Figure 12: MSE comparison of models predicting SoH of batteries for the next 7 days, 30 days, and 120 days. Figure 13: SMAPE comparison of models predicting SoH of batteries for the next 7 days, 30 days, and 120 days. Figure 14 offers a comprehensive comparison of the accuracy demonstrated by the five models in predicting short, medium, and long-term battery SoH. Through a thorough examination of the performance indicators across the five prediction models, FELL users can readily discern which model can provide the most accurate prediction results. It is evident that LSTM achieves the highest prediction accuracy, followed by the GRU model, and subsequently, the multi-head attention transformer model. Figures 15 and 16 depict the comparison results of the five prediction models implemented on the FELL platform for forecasting the SoH of electric vehicle batteries over the next 30 days and 120 days, respectively. Notably, these experimental findings align with the performance analysis results presented in Tables 2 and 3. Figure 16 shows a dramatic degradation of the EV battery, with more than a 10% capacity fade over four months. This rapid degradation may be attributed to factors such as extreme driving conditions, frequent fast charging, high temperatures, and other environmental factors that can accelerate battery degradation. In our study, we employed a two-step approach to estimate the SoH of electric vehicle batteries. Initially, we applied the EKF to estimate the SoH based on relevant battery characteristics, including voltage, current, temperature, and other states. Subsequently, we utilized the estimated SoH, along with other battery-related states and temporal features, as input fields to train time-series deep learning models. These deep learning models were specifically developed to predict the short-, medium-, and long-term SoH of electric vehicle batteries. By combining the EKF with deep learning models, our study achieved more accurate and reliable SoH predictions across various time horizons.