## 2 Materials and Methods
### Multi-Stage Power Consumption Prediction
The batch size was set to 128, the number of epochs was set to 50, and the Adam optimizer was used. The model structure and hyperparameters were optimized through a grid search on the validation dataset. The segment length \(\Delta t\) for the multi-stage model was set to 5 min, and a three-layer multi-layer perceptron (MLP) was used for each segment predictor. The sizes of the hidden layers were 64 and 32, respectively, with ReLU activation functions applied. For the single prediction model, the LSTM, the hidden state size was set to 128 with two layers stacked. Table 1 compares the energy consumption prediction performance of the single model and the multi-stage model based on root mean squared error (RMSE) and mean absolute error (MAE). The multi-stage MLP model achieved a reduction in error of 19.8% in RMSE and 23.6% in MAE compared to the single-stage LSTM. The improvement in performance was particularly noticeable in complex driving cycles that included long-distance driving  
(Figure 2), which can be attributed to the multi-stage model's ability to finely reflect the characteristics of each driving segment. The proposed multi-stage prediction technique achieves significant performance improvement over the single prediction model by reflecting the dynamic changes in the driving environment through route segmentation. The effect is particularly pronounced in situations involving complex urban driving or long-distance travel, where non-standard patterns are mixed. This is believed to be due to the segment-specific precision modeling's advantageous effect on absorbing uncertainties. Further enhancements in prediction performance could be expected by exploring methods such as hierarchical clustering or attention mechanisms to dynamically learn route patterns and key features. Additionally, enhancing the model's scalability and generalization ability through transfer learning from cumulative driving data and model ensembles specialized for road types and vehicle models is also a key follow-up task. In the long term, developing a collaborative prediction framework that shares energy information between vehicles in real-time based on V2X communication could also be considered. Ultimately, the multi-stage energy prediction technique proposed in this study has significance as a core foundational technology for improving energy efficiency in response to the changing data environment due to the acceleration of electric vehicle adoption. It not only optimizes energy for individual drivers but also has potential applications in charging infrastructure design and grid demand management. However, additional research may be necessary for the real-time processing of large-scale data and its lightweight implementation in embedded systems. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **RMSE (kWh)** & **MAE (kWh)** \\ \hline Single-stage LSTM & 3.901 \(\pm\) 0.285 & 2.426 \(\pm\) 0.194 \\ Multi-stage MLP & 3.127 \(\pm\) 0.174 & 1.853 \(\pm\) 0.132 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of energy consumption prediction performance. Figure 2: The improvement in performance per driving cycle for the multi-stage model compared to the single model.