Intelligent Vehicle Power Control Based on Prediction of Road Type and Traffic Congestions

This paper presents a machine learning approach to the efficient vehicle power management and an intelligent power controller (IPC) that applies the learnt knowledge about the optimal power control parameters specific to road types and traffic congestion levels to online vehicle power control. The IPC uses a neural network for online prediction of roadway types and traffic congestion levels. The IPC and the prediction model have been implemented in a conventional (non-hybrid) vehicle model for online vehicle power control in a simulation program. The benefits of the IPC combined with the predicted drive cycle are demonstrated through simulation. Experiment results show that the IPC gives close to optimal performances.

• Record URL:
  • http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA495453&Location=U2&doc=GetTRDoc.pdf
• 
• Corporate Authors:

  Army Research Development and Engineering Command

  Warren, Michigan  United States 
• Authors:
  • Park, J H
  • Chen, Z
  • Kuang, M
  • Masrur, Abul
  • Phillips, A
• Publication Date: 2008-9

Language

• English

Media Info

• Media Type: Print
• Pagination: 6p

Subject/Index Terms

• TRT Terms: Computer online services; Data collection; Highway operations; Hybrid vehicles; Intelligent vehicles; Learning; Neural networks; Optimization; Traffic congestion; Traffic flow
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01135214
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 21 2009 8:11AM