## 3 Results
### Performance Evaluation of Prediction Models  
To compare the performance of the proposed multi-stage prediction technique with a single-stage prediction method, experiments were conducted using real electric vehicle (EV) operation data. The dataset was divided into training (60%), validation (20%), and testing (20%) sets per vehicle, with all experiments conducted on the testing dataset. The segment length \(\Delta t\) for the multi-stage model was set to 5 min, and a four-layer MLP (Multi-Layer Perceptron) was used for each segment predictor. The MLP architecture consists of an input layer, three hidden layers with 64, 32, and 16 neurons, respectively, and an output layer with a single neuron for energy consumption prediction. was used for each segment predictor. The ReLU activation function was used, and linear activation was applied in the output layer. The single-stage model employed a bidirectional LSTM, with the hidden state size set to 64. Both models were trained using the MSE loss function and the Adam optimizer (\(lr=0.001\)), with early stopping and a 20% dropout applied to prevent overfitting. The experimental results showed that the multi-stage technique achieved significant performance improvements of 26.3% in RMSE and 23.7% in MAE compared to the single-stage method (\(p<0.01\)). This performance gap is attributed to the structural advantage of the multi-stage model, which can flexibly adapt to changes in driving conditions through route segmentation. As shown in Figure 4, the single-stage model predicts a uniform energy consumption for the entire driving section, thus showing limitations in capturing local variability. In contrast, the multi-stage model performs independent predictions for each segment and models the temporal dependencies between adjacent segments, allowing for a much finer estimation of dynamic variability due to changes in speed, acceleration, and elevation. Particularly, as observed in the 140-200 s interval in Figure 4, when rapid changes in speed occur along with corresponding shifts in energy consumption patterns, the accuracy of the single-stage model significantly deteriorates. However, the multi-stage model maintains stable prediction performance even in such abrupt situations, suggesting that segment-based modeling enables more robust predictions against uncertainties. Furthermore, the multi-stage model also contributes to mitigating long-term dependency issues. As seen in Figure 4, the single-stage model often exhibits a tendency for past prediction errors to propagate to later points, resulting in cumulative errors. On the other hand, the multi-stage model performs independent predictions at the segment level  
Figure 4: Comparison of prediction results between single-stage and multi-stage models (sample driving sequence). and utilizes a cyclic structure to capture complex patterns inherent in long-term trends, significantly enhancing long-term stability compared to the single-stage model. Thus, the multi-stage model, by composing the total energy consumption from the sum of individual segment predictions, enables precise forecasting that considers both local variability and long-term trends. Such sophisticated demand prediction information can be directly utilized for driver route planning and battery management strategies and further contribute to decision-making efficiency across the energy ecosystem, such as in charging infrastructure development and demand-based billing systems. Figure 5 compares the performance of energy prediction models under different learning structures: centralized FL, vanilla FL, and partially decentralized FL. Partially decentralized FL is a hybrid approach that combines the advantages of centralized and decentralized architectures. It selects a subset of nodes to participate in global model updates based on their performance, data quality, or other criteria. The selected nodes' local models are then aggregated using weighted averaging, where the weights are determined by factors such as the nodes' data size or reliability.