Intelligent vehicle power management through neural learning

Author

Park, Jungme ; Chen, Zhihang ; Murphey, Yi L.

Author_Institution

Dept. of Electr. & Comput. Eng., Univ. of Michigan-Dearborn, Dearborn, MI, USA

fYear

2010

fDate

18-23 July 2010

Firstpage

1

Lastpage

7

Abstract

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.

Keywords

dynamic programming; hybrid electric vehicles; learning (artificial intelligence); load flow control; neural nets; power control; power engineering computing; power system management; HEV model; PSAT simulation environment; UMD_IPC; dual-power-source nature; dynamic programming; hybrid electric vehicle; in-vehicle operation; intelligent power controller; intelligent vehicle power management; machine learning; neural learning; optimal power flow; Artificial neural networks; Batteries; Engines; Fuels; Gears; Hybrid electric vehicles;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks (IJCNN), The 2010 International Joint Conference on

Conference_Location

Barcelona

ISSN

1098-7576

Print_ISBN

978-1-4244-6916-1

Type

conf

DOI

10.1109/IJCNN.2010.5596725

Filename

5596725