Year: 2024
Type: article
Abstract: With the increasing number of heavy-duty trucks and their high fuel consumption characteristics, reducing fuel costs has become a primary challenge for the freight industry. Consequently, accurately predicting fuel consumption for heavy-duty trucks is crucial. However, existing fuel consumption prediction models still face challenges in terms of prediction accuracy. To address this issue, a model named Cross-LSTM Multi-Feature Distillation (CLMFD) is proposed. The CLMFD model employs the Crossformer model and the LSTM model as teacher and student models, respectively, utilizing multi-layer intermediate features for distillation. Fuel consumption data from a vehicular networking system was used in this study. Initially, the raw data were preprocessed by segmenting it into two-kilometer intervals, calculating sample features, and handling outliers using box plots. Feature selection was then performed using XGBoost. Subsequently, the CLMFD model was applied to predict fuel consumption. Experimental results demonstrate that the CLMFD model significantly outperforms baseline models in prediction performance. Ablation studies further indicate that the CLMFD model effectively integrates the strengths of both the Crossformer and LSTM, exhibiting superior predictive performance. Finally, predictions on data with varying masking rates show that the CLMFD model demonstrates robust performance. These findings validate the reliability and practicality of the CLMFD model, providing strong support for future research in fuel consumption prediction.
Source: Applied Sciences
DOI: 10.3390/app15010283
Authors: Kai Du, Qingqing Shi, Jingni Song, Dan Chen, Weiyu Liu
Institution: Chang'an University
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,728
Award: G8420481108
OpenAlex ID: W4405925648