Fuel Rate Prediction for Heavy-Duty Trucks

Fuel cost contributes significantly to the high operation cost of heavy-duty trucks. Developing fuel rate prediction models is the cornerstone of fuel consumption optimization approaches for heavy-duty trucks. However, limited by accurate features directly related to the truck's fuel consumption, state-of-the-art models show poor performance and are rarely deployed in practice. In this paper, we use the truck's engine management system (EMS) and Instant Fuel Meter (IFM) to collect a three-month dataset during the period of December 2019 to June 2020. Seven prediction models, including linear regression, polynomial regression, MLP, CNN, LSTM, CNN-LSTM, and AutoML, are investigated and evaluated to predict real-time fuel rate. The evaluation results show that the EMS and IFM dataset help to improve the coefficient of determination of traditional linear/polynomial models from 0.87 to 0.96, while learning-based approach AutoML improves the coefficient of determination to attain 0.99. Besides, we explore the actual deployment of fuel rate prediction with transfer learning and path planning for autonomous driving.

Automated summary

Fuel cost is a major factor contributing to the high operation cost of heavy-duty trucks. Developing fuel rate prediction models is crucial for optimizing fuel consumption in these trucks. However, the existing models show poor performance due to the lack of accurate features directly related to fuel consumption. In this study, we collect a three-month dataset using the truck's engine management system and Instant Fuel Meter, and evaluate seven prediction models. The results show that the dataset improves the performance of traditional linear/polynomial models and the AutoML approach achieves the highest coefficient of determination. Additionally, we explore the practical deployment of fuel rate prediction for autonomous driving using transfer learning and path planning.