An evaluation pattern generation scheme for electric components in hybrid electric vehicles

Author

Miyazaki, Taizo

Author_Institution

Bio & Meas. Syst. Lab., Hitachi, Ltd., Saitama, Japan

fYear

2010

fDate

8-10 Sept. 2010

Firstpage

530

Lastpage

535

Abstract

A novel multi-objective optimization scheme (MOEWE) is proposed for the purpose of generating the operation profiles of electric components, even if the control method of the components´ system is unknown. This method continuously receives evaluation feedback from system outputs and updates the scalar weights for each objective to estimate them. A continuous/discrete hybrid radial basis function network (HRBFN) is adopted to describe the values of selected scalar weights. The values are updated by reinforcement learning with feedback rewards generated by an estimator that uses the system outputs. Applying the process sequentially brings the operation profile close to the desired one. The proposed scheme was applied to a hybrid electric vehicle (HEV) simulation using the LA92 driving pattern. The results show that the scheme suitablygenerates the operation profiles of electric components.

Keywords

hybrid electric vehicles; learning (artificial intelligence); mechanical engineering computing; optimisation; radial basis function networks; LA92 driving pattern; electric components; evaluation pattern generation scheme; feedback rewards; hybrid electric vehicles; hybrid radial basis function network; multiobjective optimization scheme; reinforcement learning; Batteries; Engines; Force; Hybrid electric vehicles; Mathematical model; System-on-a-chip; hybrid electric vehicle; multi-objective optimization; operating profile; radial basis function; reinforcement learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Applications (CCA), 2010 IEEE International Conference on

Conference_Location

Yokohama

Print_ISBN

978-1-4244-5362-7

Electronic_ISBN

978-1-4244-5363-4

Type

conf

DOI

10.1109/CCA.2010.5611262

Filename

5611262