Intelligent Vehicle Power Management Using Machine Learning and Fuzzy Logic

Abstract

We present our research in optimal power management for a generic vehicle power system that has multiple power sources using machine learning and fuzzy logic. A machine learning algorithm, LOPPS, has been developed to learn about optimal power source combinations with respect to minimum power loss for all possible load requests and various system power states. The results generated by the LOPPS are used to build a fuzzy power controller (FPC). FPC is integrated into a simulation program implemented by using a generic simulation software as indicated in reference [22] and is used to dynamically allocate optimal power sources during online drive. The simulation results generated by FPC show that the proposed machine learning algorithm combined with fuzzy logic is a promising technology for vehicle power management.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2008
Accession Number
ADA490158

Entities

People

  • M. A. Masrur
  • Yi L. Murphey
  • Zhihang Chen

Organizations

  • University of Michigan

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Autonomous Vehicles
  • Combustion
  • Computer Programs
  • Energy
  • Energy Consumption
  • Fuel Cells
  • Ground Vehicles
  • Hybrid Electric Vehicles
  • Learning
  • Logic
  • Machine Learning
  • Mathematical Models
  • Simulations
  • Time Intervals
  • Vehicles

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Database Systems and Applications
  • Mathematical Modeling and Probability Theory.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks