# Prediction of Truck Fuel Consumption Based on Crossformer-LSTM Characteristic Distillation
## 6 Conclusions  
This study is based on truck driving data from a vehicle networking system of a certain group. The raw data were preprocessed by dividing the driving data into samples every two kilometers and calculating various features for each sample. Using the XGBoost feature selection method, seven key features--criuse time, braking count, average speed, downhill, uphill, RPM, and average altitude--were identified as the main factors influencing fuel consumption. Based on these, a fuel consumption prediction model was developed using CLMFD and compared with BP neural networks, RF, RNN, Transformer, and PatchTST models.  
Experimental results show that the CLMFD model achieves the best predictive performance, with an MSE of 0.0962, significantly outperforming the other models. In particular, when the data contains outliers, the CLMFD model effectively transfers knowledge from the Crossformer model to LSTM, enabling LSTM to better adapt to complex time series data, thereby improving the model's accuracy and robustness.  
The proposed CLMFD model not only excels in complex time series prediction tasks but also enhances the model's generalization capability through multi-feature distillation. It is suitable for multi-scenario, multi-variable time series tasks and demonstrates strong scalability. However, there are some limitations in practical applications: firstly, the model's complexity, parameter count, and computational time may pose challenges for real-world deployment, and secondly, the model is sensitive to hyperparameter selection, which may require additional tuning to achieve optimal performance; moreover, the current study has not been extensively validated in low-computation devices or real-time scenarios, and further optimization is needed for practical use.  
In future research, we will expand the dataset and incorporate additional features, including road conditions, traffic patterns, and weather factors, to better understand their impact on fuel consumption. Moreover, since the current dataset includes only a limited number of vehicle types, the variations in fuel consumption across different models have yet to be explored. Future work will also focus on developing fuel consumption prediction models tailored to specific vehicle types, thereby improving the model's versatility and providing more precise support for vehicle energy efficiency and emission reduction efforts.  
Conceptualization, K.D. and J.S.; formal analysis, D.C. and W.L.; methodology, Q.S.; resources, K.D. and W.L.; supervision, J.S.; validation, D.C.; writing--original draft, Q.S.; writing--review and editing, K.D., J.S., D.C. and W.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under grant 52202385, and the Anhui Provincial Key Laboratory of Urban Rail Transit Safety and Emergency Management, Hefei University under grant 2024 GD0009.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
This research benefited greatly from the contributions of [PERSON] and [PERSON], whose valuable revisions and feedback during the revision stage significantly improved the manuscript.  
The author declares no conflicts of interest.