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, the authors 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, they explore the actual deployment of fuel rate prediction with transfer learning and path planning for autonomous driving.

• Record URL:
  • http://dx.doi.org/10.1109/TITS.2023.3265007
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2023, IEEE.
• Authors:
  • Liu, Liankai
  • Li, Wei
  • Wang, Dawei
  • Wu, Yi
  • Yang, Ruiyang
  • Shi, Weisong
• Publication Date: 2023-8

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 8222-8235
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 24
  • Issue Number: 8
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Data analysis; Fuel consumption; Heavy duty trucks; Predictive models; Prices
• Subject Areas: Data and Information Technology; Energy; Finance; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01891750
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 28 2023 5:10PM