Intelligent Vehicle Power Management through Neural Learning

被引：0

作者：

Park, Jungme ^{[1
]}

Chen, ZhiHang ^{[1
]}

Murphey, Yi L. ^{[1
]}

机构：

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

来源：

2010 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS IJCNN 2010 | 2010年

关键词：

PARALLEL HYBRID VEHICLE; AGENT;

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：

Power management for the Hybrid Electric Vehicle (HEV) is a challenging problem because of the dual-power-source nature of HEV design and implementation. In this paper, we present an Intelligent Power Controller, UMD_IPC, trained with a machine learning approach to provide optimal power flow for in-vehicle operations. The UMD_IPC is implemented in a HEV model provided by PSAT simulation environment, and its performances on three drive cycles are close to the optimal results generated by Dynamic Programming.

引用

页数：7

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