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

Abstract

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.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2008
Accession Number
ADA495453

Entities

People

  • Abul Masrur
  • Anthony E. Phillips
  • Jungme Park
  • Ming Kuang
  • Yi L. Murphey
  • Zhihang Chen

Organizations

  • United States Army Tank Automotive Research, Development and Engineering Center

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Autonomous Vehicles
  • Computer Programming
  • Congestion
  • Electric Vehicles
  • Fuel Consumption
  • Hybrid Electric Vehicles
  • Machine Learning
  • Mathematical Models
  • Models
  • Neural Networks
  • Optimization
  • Simulations
  • Spark Ignition
  • Standards
  • Time Intervals
  • Vehicles

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Integrated Circuit Design and Technology.
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Neural Networks