Intelligent Vehicle Power Management using Machine Learning and Fuzzy Logic

被引：0

作者：

Chen, ZhiHang ^{[1
]}

Masrur, M. Abul ^{[2
]}

Murphey, Yi L. ^{[1
]}

机构：

[1] Univ Michigan, Dept Elect & Comp Engn, Dearborn, MI 48128 USA

[2] US Army RDECOM TARDEC, Dearborn, MI USA

来源：

2008 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-5 | 2008年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present oar 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.

引用

页码：2353 / +

页数：2

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