Accession Number:

ADA490158

Title:

Intelligent Vehicle Power Management Using Machine Learning and Fuzzy Logic

Descriptive Note:

Conference paper

Corporate Author:

MICHIGAN UNIV DEARBORN

Personal Author(s):

• Chen, ZhiHang
• Masrur, M. A.
• Murphey, Yi L.

Report Date:

2008-06-01

Pagination or Media Count:

9.0

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.

Descriptors:

• *FUZZY LOGIC
• *ENGINES
• *GROUND VEHICLES
• *POWER SUPPLIES
• OPTIMIZATION
• LEARNING MACHINES
• LOSSES
• CONTROL SYSTEMS
• SYMPOSIA
• COMPUTERIZED SIMULATION
• ALGORITHMS
• MANAGEMENT

Subject Categories:

• Electric Power Production and Distribution
• Electrochemical Energy Storage
• Cybernetics
• Surface Transportation and Equipment

Distribution Statement:

APPROVED FOR PUBLIC RELEASE