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

Jieh Shian Young; Yi Pin Lin; Po Wen Shih

doi:10.4028/www.scientific.net/AMM.392.272

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

Jieh Shian Young, Yi Pin Lin, Po Wen Shih

Graduate Institute of Vehicle Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Mechanical and Electrical Technology V
Pages	272-276
Number of pages	5
DOIs	https://doi.org/10.4028/www.scientific.net/AMM.392.272
Publication status	Published - 2013 Oct 29
Event	5th International Conference on Mechanical and Electrical Technology, ICMET 2013 - Chengdu, China Duration: 2013 Jul 20 → 2013 Jul 21

Publication series

Name	Applied Mechanics and Materials
Volume	392
ISSN (Print)	1660-9336
ISSN (Electronic)	1662-7482

Other

Other	5th International Conference on Mechanical and Electrical Technology, ICMET 2013
Country	China
City	Chengdu
Period	13-07-20 → 13-07-21

All Science Journal Classification (ASJC) codes

Engineering(all)

Access to Document

10.4028/www.scientific.net/AMM.392.272

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Cite this

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title = "Neural network approach to gain scheduling for traction control of electrical vehicles",

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.",

author = "Young, {Jieh Shian} and Lin, {Yi Pin} and Shih, {Po Wen}",

year = "2013",

month = oct,

day = "29",

doi = "10.4028/www.scientific.net/AMM.392.272",

language = "English",

isbn = "9783037858349",

series = "Applied Mechanics and Materials",

pages = "272--276",

booktitle = "Mechanical and Electrical Technology V",

note = "5th International Conference on Mechanical and Electrical Technology, ICMET 2013 ; Conference date: 20-07-2013 Through 21-07-2013",

}

Young, JS, Lin, YP & Shih, PW 2013, Neural network approach to gain scheduling for traction control of electrical vehicles. in Mechanical and Electrical Technology V. Applied Mechanics and Materials, vol. 392, pp. 272-276, 5th International Conference on Mechanical and Electrical Technology, ICMET 2013, Chengdu, China, 13-07-20. https://doi.org/10.4028/www.scientific.net/AMM.392.272

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T1 - Neural network approach to gain scheduling for traction control of electrical vehicles

AU - Young, Jieh Shian

AU - Lin, Yi Pin

AU - Shih, Po Wen

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Y1 - 2013/10/29

N2 - 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.

AB - 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.

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