Economic and Efficient Hybrid Vehicle Fuel Economy and Emissions Modeling Using an Artificial Neural Network

High accuracy hybrid vehicle fuel consumption (FC) and emissions models used in practice today are the product of years of research, are physics based, and bear a large computational cost. However, it may be possible to replace these models with a non-physics based, higher accuracy, and computationally efficient versions. In this research, an alternative method is developed by training and testing a time series artificial neural network (ANN) using real world, on-road data for a hydraulic hybrid truck to predict instantaneous FC and emissions. Parameters affecting model fidelity were investigated including the number of neurons in the hidden layer, specific training inputs, dataset length, and hybrid system status. The results show that the ANN model was computationally faster and predicted FC within a mean absolute error of 0-0.1%. For emissions prediction the ANN model had a mean absolute error of 0-3% across CO2, CO, and NOx aggregate predicted concentrations. Overall, these results indicate that ANN models could be used for a variety of research applications due to their economic and computational benefits such as derivation of vehicle control strategies to reduce FC and emissions in modern vehicles.

• Record URL:
  https://doi.org/10.4271/2018-01-0315
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/01487191
• Supplemental Notes:
  • Abstract reprinted with permission of SAE International.
• Authors:
  • Asher, Zachary D
  • Galang, Abril A
  • Briggs, Will
  • Johnston, Brian
  • Bradley, Thomas H
  • Jathar, Shantanu
• Conference:
  • WCX World Congress Experience
  • Location: Detroit Michigan, United States
  • Date: 2018-4-10 to 2018-4-12
• Publication Date: 2018-4-3

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Serial:
  • SAE Technical Paper
  • Publisher: Society of Automotive Engineers (SAE)
  • ISSN: 0148-7191
  • EISSN: 2688-3627
  • Serial URL: http://papers.sae.org/

Subject/Index Terms

• TRT Terms: Exhaust gases; Fuel consumption; Hybrid vehicles; Mathematical models; Neural networks; Nitrogen oxides; Pollutants; Simulation
• Subject Areas: Energy; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01725010
• Record Type: Publication
• Source Agency: SAE International
• Report/Paper Numbers: 2018-01-0315
• Files: TRIS, SAE
• Created Date: Dec 13 2019 9:29AM