Neural network approach to gain scheduling for traction control of electrical vehicles

Jieh Shian Young, Yi Pin Lin, Po Wen Shih

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

This paper proposes a gain scheduling approach by neural network to force control of the electric vehicle wheels. To approximate to the reality in simulation, we utilize the traction force database of the motor, called the current-RPM-torque database, instead of the slip ratio measurements. The system is nonlinear and a constant gain cannot overcome all road conditions of the traction force control for the electric vehicles. The appropriate gains for different road conditions can be the training data of the neural network. In this paper, the proper parameters for the RBF neural network are obtained. The appropriate gains which have to fit the assigned specifications in time domain seem to be inverse proportion to the slip ratio slope.

Original languageEnglish
Title of host publicationMechanical and Electrical Technology V
Pages272-276
Number of pages5
DOIs
Publication statusPublished - 2013 Oct 29
Event5th International Conference on Mechanical and Electrical Technology, ICMET 2013 - Chengdu, China
Duration: 2013 Jul 202013 Jul 21

Publication series

NameApplied Mechanics and Materials
Volume392
ISSN (Print)1660-9336
ISSN (Electronic)1662-7482

Other

Other5th International Conference on Mechanical and Electrical Technology, ICMET 2013
CountryChina
CityChengdu
Period13-07-2013-07-21

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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